blob_id stringlengths 40 40 | bodies listlengths 2 6 | bodies_text stringlengths 196 6.73k | class_docstring stringlengths 0 700 | class_name stringlengths 1 86 | detected_licenses listlengths 0 45 | format_version stringclasses 1
value | full_text stringlengths 438 7.52k | id stringlengths 40 40 | length_bytes int64 506 50k | license_type stringclasses 2
values | methods listlengths 2 6 | n_methods int64 2 6 | original_id stringlengths 38 40 ⌀ | prompt stringlengths 153 4.25k | prompted_full_text stringlengths 645 10.7k | revision_id stringlengths 40 40 | skeleton stringlengths 162 4.34k | snapshot_name stringclasses 1
value | snapshot_source_dir stringclasses 1
value | solution stringlengths 302 7.33k | source stringclasses 1
value | source_path stringlengths 4 177 | source_repo stringlengths 6 110 | split stringclasses 1
value | star_events_count int64 0 209k |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
cb2de76ad47851fb38b0704606852e640f8a5359 | [
"csv_file = 'data/customer_data.csv'\nnew_data = database.read_data(csv_file)\nexpected = [{'user_id': '37431', 'name': 'Bill Gates', 'address': '500 5th Ave, Seattle, WA', 'phone_number': '206-709-3100', 'email': 'bgates@microsoft.com'}, {'user_id': '18720', 'name': 'Steve Ballmer', 'address': '123 Bel Air Road Lo... | <|body_start_0|>
csv_file = 'data/customer_data.csv'
new_data = database.read_data(csv_file)
expected = [{'user_id': '37431', 'name': 'Bill Gates', 'address': '500 5th Ave, Seattle, WA', 'phone_number': '206-709-3100', 'email': 'bgates@microsoft.com'}, {'user_id': '18720', 'name': 'Steve Ballmer... | Tests functionality of MongoDB in database module. | TestDatabase | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestDatabase:
"""Tests functionality of MongoDB in database module."""
def test_read_data(self):
"""Tests if csv input files will be formatted correctly to list of dictionaries."""
<|body_0|>
def test_import_data(self):
"""Tests data will be imported correctly, s... | stack_v2_sparse_classes_36k_train_011300 | 3,859 | no_license | [
{
"docstring": "Tests if csv input files will be formatted correctly to list of dictionaries.",
"name": "test_read_data",
"signature": "def test_read_data(self)"
},
{
"docstring": "Tests data will be imported correctly, setting up database tables with proper formats.",
"name": "test_import_d... | 5 | null | Implement the Python class `TestDatabase` described below.
Class description:
Tests functionality of MongoDB in database module.
Method signatures and docstrings:
- def test_read_data(self): Tests if csv input files will be formatted correctly to list of dictionaries.
- def test_import_data(self): Tests data will be ... | Implement the Python class `TestDatabase` described below.
Class description:
Tests functionality of MongoDB in database module.
Method signatures and docstrings:
- def test_read_data(self): Tests if csv input files will be formatted correctly to list of dictionaries.
- def test_import_data(self): Tests data will be ... | 5dac60f39e3909ff05b26721d602ed20f14d6be3 | <|skeleton|>
class TestDatabase:
"""Tests functionality of MongoDB in database module."""
def test_read_data(self):
"""Tests if csv input files will be formatted correctly to list of dictionaries."""
<|body_0|>
def test_import_data(self):
"""Tests data will be imported correctly, s... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TestDatabase:
"""Tests functionality of MongoDB in database module."""
def test_read_data(self):
"""Tests if csv input files will be formatted correctly to list of dictionaries."""
csv_file = 'data/customer_data.csv'
new_data = database.read_data(csv_file)
expected = [{'us... | the_stack_v2_python_sparse | students/N0vA/lesson10/assignment/test_database.py | JavaRod/SP_Python220B_2019 | train | 1 |
6177e37be64b94b52bf5b0bf6e35181e92159170 | [
"self.interval = interval\nthread = threading.Thread(target=self.update_information, args=())\nthread.setDaemon(True)\nthread.start()",
"while True:\n full_weather = prediction_weather_funct()\n print(f'#### Updating Weather Information @ {datetime.now()} ####')\n self.update = Weatherforecast\n sle... | <|body_start_0|>
self.interval = interval
thread = threading.Thread(target=self.update_information, args=())
thread.setDaemon(True)
thread.start()
<|end_body_0|>
<|body_start_1|>
while True:
full_weather = prediction_weather_funct()
print(f'#### Updating... | Weatherforecast | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Weatherforecast:
def __init__(self, interval):
"""Constructor: Make a background job whihch automatically updates the weather infomration. (int) Interval: time to sleep after running update function"""
<|body_0|>
def update_information(self):
"""Method that runs in b... | stack_v2_sparse_classes_36k_train_011301 | 5,735 | no_license | [
{
"docstring": "Constructor: Make a background job whihch automatically updates the weather infomration. (int) Interval: time to sleep after running update function",
"name": "__init__",
"signature": "def __init__(self, interval)"
},
{
"docstring": "Method that runs in background updating global... | 2 | stack_v2_sparse_classes_30k_train_000496 | Implement the Python class `Weatherforecast` described below.
Class description:
Implement the Weatherforecast class.
Method signatures and docstrings:
- def __init__(self, interval): Constructor: Make a background job whihch automatically updates the weather infomration. (int) Interval: time to sleep after running u... | Implement the Python class `Weatherforecast` described below.
Class description:
Implement the Weatherforecast class.
Method signatures and docstrings:
- def __init__(self, interval): Constructor: Make a background job whihch automatically updates the weather infomration. (int) Interval: time to sleep after running u... | 5efeebedd4695ef9d904beb707a1538ba049b187 | <|skeleton|>
class Weatherforecast:
def __init__(self, interval):
"""Constructor: Make a background job whihch automatically updates the weather infomration. (int) Interval: time to sleep after running update function"""
<|body_0|>
def update_information(self):
"""Method that runs in b... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Weatherforecast:
def __init__(self, interval):
"""Constructor: Make a background job whihch automatically updates the weather infomration. (int) Interval: time to sleep after running update function"""
self.interval = interval
thread = threading.Thread(target=self.update_information, a... | the_stack_v2_python_sparse | dbbus/apps/prediction/get_prediction.py | mofiebiger/DublinBus | train | 1 | |
9fa6f4fcc902ec6f654df3cbcfff050e01c1e997 | [
"client = test_client.TestClient(context.node['baseurl'])\npid = object_info.identifier.value()\nresponse = client.get(context.TOKEN, pid)\nchecksum_from_get = test_utilities.calculate_checksum_on_string(response, object_info.checksum.algorithm)\nassert object_info.checksum.value() == checksum_from_get\nresponse = ... | <|body_start_0|>
client = test_client.TestClient(context.node['baseurl'])
pid = object_info.identifier.value()
response = client.get(context.TOKEN, pid)
checksum_from_get = test_utilities.calculate_checksum_on_string(response, object_info.checksum.algorithm)
assert object_info.ch... | Test050Get | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Test050Get:
def validate_object(self, object_info):
"""Get object and verify retrieved information against its ObjectInfo."""
<|body_0|>
def test_010_get_object_by_invalid_pid(self):
"""404 NotFound when attempting to get non-existing object."""
<|body_1|>
... | stack_v2_sparse_classes_36k_train_011302 | 2,629 | permissive | [
{
"docstring": "Get object and verify retrieved information against its ObjectInfo.",
"name": "validate_object",
"signature": "def validate_object(self, object_info)"
},
{
"docstring": "404 NotFound when attempting to get non-existing object.",
"name": "test_010_get_object_by_invalid_pid",
... | 3 | stack_v2_sparse_classes_30k_train_008754 | Implement the Python class `Test050Get` described below.
Class description:
Implement the Test050Get class.
Method signatures and docstrings:
- def validate_object(self, object_info): Get object and verify retrieved information against its ObjectInfo.
- def test_010_get_object_by_invalid_pid(self): 404 NotFound when ... | Implement the Python class `Test050Get` described below.
Class description:
Implement the Test050Get class.
Method signatures and docstrings:
- def validate_object(self, object_info): Get object and verify retrieved information against its ObjectInfo.
- def test_010_get_object_by_invalid_pid(self): 404 NotFound when ... | d72a9461894d9be7d71178fb7310101b8ef9066a | <|skeleton|>
class Test050Get:
def validate_object(self, object_info):
"""Get object and verify retrieved information against its ObjectInfo."""
<|body_0|>
def test_010_get_object_by_invalid_pid(self):
"""404 NotFound when attempting to get non-existing object."""
<|body_1|>
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Test050Get:
def validate_object(self, object_info):
"""Get object and verify retrieved information against its ObjectInfo."""
client = test_client.TestClient(context.node['baseurl'])
pid = object_info.identifier.value()
response = client.get(context.TOKEN, pid)
checksum... | the_stack_v2_python_sparse | test_utilities/src/d1_test/stress_tester/projects/_unit_test_bases_for_stress_tests/tier_1_mn_read_get.py | DataONEorg/d1_python | train | 15 | |
82c19359a2f4462a41d36691641ccd563e80513f | [
"C = self.COEFFS[imt]\nln_y_ref = self._get_ln_y_ref(rup, dists, C)\nexp1 = np.exp(C['phi3'] * (sites.vs30.clip(-np.inf, 1130) - 360))\nexp2 = np.exp(C['phi3'] * (1130 - 360))\nmean = self._get_mean(sites, C, ln_y_ref, exp1, exp2)\nstddevs = self._get_stddevs(sites, rup, C, stddev_types, ln_y_ref, exp1, exp2)\nretu... | <|body_start_0|>
C = self.COEFFS[imt]
ln_y_ref = self._get_ln_y_ref(rup, dists, C)
exp1 = np.exp(C['phi3'] * (sites.vs30.clip(-np.inf, 1130) - 360))
exp2 = np.exp(C['phi3'] * (1130 - 360))
mean = self._get_mean(sites, C, ln_y_ref, exp1, exp2)
stddevs = self._get_stddevs(s... | Implements GMPE developed by Brian S.-J. Chiou and Robert R. Youngs and published as "An NGA Model for the Average Horizontal Component of Peak Ground Motion and Response Spectra" (2008, Earthquake Spectra, Volume 24, No. 1, pages 173-215). | ChiouYoungs2008 | [
"BSD-3-Clause",
"AGPL-3.0-only"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ChiouYoungs2008:
"""Implements GMPE developed by Brian S.-J. Chiou and Robert R. Youngs and published as "An NGA Model for the Average Horizontal Component of Peak Ground Motion and Response Spectra" (2008, Earthquake Spectra, Volume 24, No. 1, pages 173-215)."""
def get_mean_and_stddevs(sel... | stack_v2_sparse_classes_36k_train_011303 | 14,578 | permissive | [
{
"docstring": "See :meth:`superclass method <.base.GroundShakingIntensityModel.get_mean_and_stddevs>` for spec of input and result values.",
"name": "get_mean_and_stddevs",
"signature": "def get_mean_and_stddevs(self, sites, rup, dists, imt, stddev_types)"
},
{
"docstring": "Add site effects to... | 4 | null | Implement the Python class `ChiouYoungs2008` described below.
Class description:
Implements GMPE developed by Brian S.-J. Chiou and Robert R. Youngs and published as "An NGA Model for the Average Horizontal Component of Peak Ground Motion and Response Spectra" (2008, Earthquake Spectra, Volume 24, No. 1, pages 173-215... | Implement the Python class `ChiouYoungs2008` described below.
Class description:
Implements GMPE developed by Brian S.-J. Chiou and Robert R. Youngs and published as "An NGA Model for the Average Horizontal Component of Peak Ground Motion and Response Spectra" (2008, Earthquake Spectra, Volume 24, No. 1, pages 173-215... | 0da9ba5a575360081715e8b90c71d4b16c6687c8 | <|skeleton|>
class ChiouYoungs2008:
"""Implements GMPE developed by Brian S.-J. Chiou and Robert R. Youngs and published as "An NGA Model for the Average Horizontal Component of Peak Ground Motion and Response Spectra" (2008, Earthquake Spectra, Volume 24, No. 1, pages 173-215)."""
def get_mean_and_stddevs(sel... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ChiouYoungs2008:
"""Implements GMPE developed by Brian S.-J. Chiou and Robert R. Youngs and published as "An NGA Model for the Average Horizontal Component of Peak Ground Motion and Response Spectra" (2008, Earthquake Spectra, Volume 24, No. 1, pages 173-215)."""
def get_mean_and_stddevs(self, sites, rup... | the_stack_v2_python_sparse | openquake/hazardlib/gsim/chiou_youngs_2008.py | GFZ-Centre-for-Early-Warning/shakyground | train | 1 |
e46cfcd3f40f76248b8e0b88734e1db0e747a6ab | [
"with QuantumTape() as tape:\n qml.BasisState(np.array([1, 0]), wires=[0, 1])\n qml.RY(0.5, wires=[1])\n qml.CNOT(wires=[0, 1])\n qml.expval(qml.PauliZ(0) @ qml.PauliY(1))\ncopied_tape = tape.copy()\nassert copied_tape is not tape\nassert copied_tape.operations == tape.operations\nassert copied_tape.obs... | <|body_start_0|>
with QuantumTape() as tape:
qml.BasisState(np.array([1, 0]), wires=[0, 1])
qml.RY(0.5, wires=[1])
qml.CNOT(wires=[0, 1])
qml.expval(qml.PauliZ(0) @ qml.PauliY(1))
copied_tape = tape.copy()
assert copied_tape is not tape
ass... | Test for tape copying behaviour | TestTapeCopying | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestTapeCopying:
"""Test for tape copying behaviour"""
def test_shallow_copy(self):
"""Test that shallow copying of a tape results in all contained data being shared between the original tape and the copy"""
<|body_0|>
def test_shallow_copy_with_operations(self, copy_fn)... | stack_v2_sparse_classes_36k_train_011304 | 49,877 | permissive | [
{
"docstring": "Test that shallow copying of a tape results in all contained data being shared between the original tape and the copy",
"name": "test_shallow_copy",
"signature": "def test_shallow_copy(self)"
},
{
"docstring": "Test that shallow copying of a tape and operations allows parameters ... | 4 | stack_v2_sparse_classes_30k_train_008000 | Implement the Python class `TestTapeCopying` described below.
Class description:
Test for tape copying behaviour
Method signatures and docstrings:
- def test_shallow_copy(self): Test that shallow copying of a tape results in all contained data being shared between the original tape and the copy
- def test_shallow_cop... | Implement the Python class `TestTapeCopying` described below.
Class description:
Test for tape copying behaviour
Method signatures and docstrings:
- def test_shallow_copy(self): Test that shallow copying of a tape results in all contained data being shared between the original tape and the copy
- def test_shallow_cop... | 0c1c805fd5dfce465a8955ee3faf81037023a23e | <|skeleton|>
class TestTapeCopying:
"""Test for tape copying behaviour"""
def test_shallow_copy(self):
"""Test that shallow copying of a tape results in all contained data being shared between the original tape and the copy"""
<|body_0|>
def test_shallow_copy_with_operations(self, copy_fn)... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TestTapeCopying:
"""Test for tape copying behaviour"""
def test_shallow_copy(self):
"""Test that shallow copying of a tape results in all contained data being shared between the original tape and the copy"""
with QuantumTape() as tape:
qml.BasisState(np.array([1, 0]), wires=[0... | the_stack_v2_python_sparse | artifacts/old_dataset_versions/original_commits_v02/pennylane/pennylane#1243/before/test_tape.py | MattePalte/Bugs-Quantum-Computing-Platforms | train | 4 |
708a80ef1b51d9c7eec6a9fab8496cc2144e7ed8 | [
"rval, pval, qval = (root.val, p.val, q.val)\nif pval < rval and qval < rval:\n return self.lowestCommonAncestor_MK1(root.left, p, q)\nelif pval > rval and qval > rval:\n return self.lowestCommonAncestor_MK1(root.right, p, q)\nelse:\n return root",
"pval, qval = (p.val, q.val)\ncurr = root\nwhile curr:\n... | <|body_start_0|>
rval, pval, qval = (root.val, p.val, q.val)
if pval < rval and qval < rval:
return self.lowestCommonAncestor_MK1(root.left, p, q)
elif pval > rval and qval > rval:
return self.lowestCommonAncestor_MK1(root.right, p, q)
else:
return roo... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def lowestCommonAncestor_MK1(self, root: TreeNode, p: TreeNode, q: TreeNode) -> TreeNode:
"""Recursive"""
<|body_0|>
def lowestCommonAncestor_MK2(self, root: TreeNode, p: TreeNode, q: TreeNode) -> TreeNode:
"""Iterative"""
<|body_1|>
<|end_skeleton... | stack_v2_sparse_classes_36k_train_011305 | 1,002 | no_license | [
{
"docstring": "Recursive",
"name": "lowestCommonAncestor_MK1",
"signature": "def lowestCommonAncestor_MK1(self, root: TreeNode, p: TreeNode, q: TreeNode) -> TreeNode"
},
{
"docstring": "Iterative",
"name": "lowestCommonAncestor_MK2",
"signature": "def lowestCommonAncestor_MK2(self, root... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def lowestCommonAncestor_MK1(self, root: TreeNode, p: TreeNode, q: TreeNode) -> TreeNode: Recursive
- def lowestCommonAncestor_MK2(self, root: TreeNode, p: TreeNode, q: TreeNode)... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def lowestCommonAncestor_MK1(self, root: TreeNode, p: TreeNode, q: TreeNode) -> TreeNode: Recursive
- def lowestCommonAncestor_MK2(self, root: TreeNode, p: TreeNode, q: TreeNode)... | d7ba416d22becfa8f2a2ae4eee04c86617cd9332 | <|skeleton|>
class Solution:
def lowestCommonAncestor_MK1(self, root: TreeNode, p: TreeNode, q: TreeNode) -> TreeNode:
"""Recursive"""
<|body_0|>
def lowestCommonAncestor_MK2(self, root: TreeNode, p: TreeNode, q: TreeNode) -> TreeNode:
"""Iterative"""
<|body_1|>
<|end_skeleton... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def lowestCommonAncestor_MK1(self, root: TreeNode, p: TreeNode, q: TreeNode) -> TreeNode:
"""Recursive"""
rval, pval, qval = (root.val, p.val, q.val)
if pval < rval and qval < rval:
return self.lowestCommonAncestor_MK1(root.left, p, q)
elif pval > rval and... | the_stack_v2_python_sparse | 0235. Lowest Common Ancestor of a Binary Search Tree/Solution.py | faterazer/LeetCode | train | 4 | |
d0f0e7fa29fe3f4f724257441a2149379f211c71 | [
"if dhce == None:\n dhce = DataHolderCallEncapsulator()\ndh = dhce.encapsulate_call(SheetPreProcessor.pre_strip, dh)\ndh, meta_dh = dhce.encapsulate_call(TriangleHeaderFinder.find_triangle_headers, dh, return_meta=True)\ndh = dhce.encapsulate_call(DateFiller.identify_and_gen_date_cols, dh, replace_col=False)\ndh... | <|body_start_0|>
if dhce == None:
dhce = DataHolderCallEncapsulator()
dh = dhce.encapsulate_call(SheetPreProcessor.pre_strip, dh)
dh, meta_dh = dhce.encapsulate_call(TriangleHeaderFinder.find_triangle_headers, dh, return_meta=True)
dh = dhce.encapsulate_call(DateFiller.identi... | TrianglePipeline | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TrianglePipeline:
def triangle_pipeline_dh(dh, dhce=None, tri_type='single', n_outputs=1):
"""Performs a list of operations on an incoming DataHolder (dh) :param dh: :param dhce: :param tri_type: :param n_outputs: :return:"""
<|body_0|>
def table_triangle_pipeline_dh(dh, dhc... | stack_v2_sparse_classes_36k_train_011306 | 3,987 | permissive | [
{
"docstring": "Performs a list of operations on an incoming DataHolder (dh) :param dh: :param dhce: :param tri_type: :param n_outputs: :return:",
"name": "triangle_pipeline_dh",
"signature": "def triangle_pipeline_dh(dh, dhce=None, tri_type='single', n_outputs=1)"
},
{
"docstring": "Performs a ... | 2 | null | Implement the Python class `TrianglePipeline` described below.
Class description:
Implement the TrianglePipeline class.
Method signatures and docstrings:
- def triangle_pipeline_dh(dh, dhce=None, tri_type='single', n_outputs=1): Performs a list of operations on an incoming DataHolder (dh) :param dh: :param dhce: :par... | Implement the Python class `TrianglePipeline` described below.
Class description:
Implement the TrianglePipeline class.
Method signatures and docstrings:
- def triangle_pipeline_dh(dh, dhce=None, tri_type='single', n_outputs=1): Performs a list of operations on an incoming DataHolder (dh) :param dh: :param dhce: :par... | ee0ad1bb43e8df525c2d747f23ef8e2580f72f0f | <|skeleton|>
class TrianglePipeline:
def triangle_pipeline_dh(dh, dhce=None, tri_type='single', n_outputs=1):
"""Performs a list of operations on an incoming DataHolder (dh) :param dh: :param dhce: :param tri_type: :param n_outputs: :return:"""
<|body_0|>
def table_triangle_pipeline_dh(dh, dhc... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TrianglePipeline:
def triangle_pipeline_dh(dh, dhce=None, tri_type='single', n_outputs=1):
"""Performs a list of operations on an incoming DataHolder (dh) :param dh: :param dhce: :param tri_type: :param n_outputs: :return:"""
if dhce == None:
dhce = DataHolderCallEncapsulator()
... | the_stack_v2_python_sparse | python_back_end/triangle_formatting/triangle_pipeline.py | Henler/ReBridge_data_cloud | train | 0 | |
60a6bc4edc2623c992baddf2529b7324c1a8bf47 | [
"def dfs(root):\n if not root:\n return\n res.append(str(root.val) + ',')\n dfs(root.left)\n dfs(root.right)\nres = []\ndfs(root)\nreturn ''.join(res)",
"lst = data.split(',')\nlst.pop()\nstack = []\nhead = None\nfor n in lst:\n n = int(n)\n if not head:\n head = TreeNode(n)\n ... | <|body_start_0|>
def dfs(root):
if not root:
return
res.append(str(root.val) + ',')
dfs(root.left)
dfs(root.right)
res = []
dfs(root)
return ''.join(res)
<|end_body_0|>
<|body_start_1|>
lst = data.split(',')
... | Codec2 | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Codec2:
def serialize(self, root: TreeNode) -> str:
"""Encodes a tree to a single string."""
<|body_0|>
def deserialize(self, data: str) -> TreeNode:
"""Decodes your encoded data to tree."""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
def dfs(root)... | stack_v2_sparse_classes_36k_train_011307 | 1,478 | no_license | [
{
"docstring": "Encodes a tree to a single string.",
"name": "serialize",
"signature": "def serialize(self, root: TreeNode) -> str"
},
{
"docstring": "Decodes your encoded data to tree.",
"name": "deserialize",
"signature": "def deserialize(self, data: str) -> TreeNode"
}
] | 2 | stack_v2_sparse_classes_30k_train_000600 | Implement the Python class `Codec2` described below.
Class description:
Implement the Codec2 class.
Method signatures and docstrings:
- def serialize(self, root: TreeNode) -> str: Encodes a tree to a single string.
- def deserialize(self, data: str) -> TreeNode: Decodes your encoded data to tree. | Implement the Python class `Codec2` described below.
Class description:
Implement the Codec2 class.
Method signatures and docstrings:
- def serialize(self, root: TreeNode) -> str: Encodes a tree to a single string.
- def deserialize(self, data: str) -> TreeNode: Decodes your encoded data to tree.
<|skeleton|>
class ... | 3f0ffd519404165fd1a735441b212c801fd1ad1e | <|skeleton|>
class Codec2:
def serialize(self, root: TreeNode) -> str:
"""Encodes a tree to a single string."""
<|body_0|>
def deserialize(self, data: str) -> TreeNode:
"""Decodes your encoded data to tree."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Codec2:
def serialize(self, root: TreeNode) -> str:
"""Encodes a tree to a single string."""
def dfs(root):
if not root:
return
res.append(str(root.val) + ',')
dfs(root.left)
dfs(root.right)
res = []
dfs(root)
... | the_stack_v2_python_sparse | Problems/0400_0499/0449_Serialize_and_Deserialize_BST/Project_Python3/TreeNode/Codec2.py | NobuyukiInoue/LeetCode | train | 0 | |
06f60112d8b0de54934ffb50a1d0743103898ab3 | [
"self.clock = False\nself.bus = bus\nself.bus.register(self)\nself._data = [0] * (16635 + 1)\nreturn",
"if self.bus.address > 16635:\n return\nif self.bus.mode == MODE.READ:\n self.bus.data = self._data[self.bus.address]\nelif self.bus.mode == MODE.WRITE:\n self._data[self.bus.address] = self.bus.data\nr... | <|body_start_0|>
self.clock = False
self.bus = bus
self.bus.register(self)
self._data = [0] * (16635 + 1)
return
<|end_body_0|>
<|body_start_1|>
if self.bus.address > 16635:
return
if self.bus.mode == MODE.READ:
self.bus.data = self._data[... | class ROM ======================== Cette classe représente la mémoire du micro-ordinateur. Elle contient une fonction qui permet d'uploader le code dans la mémoire ROM (un peu comme nous pourrions le faire avec certains micro-controlleur USB). À chaque coup d'horloge (clock/event), la classe vérifie si elle doit effect... | ROM | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ROM:
"""class ROM ======================== Cette classe représente la mémoire du micro-ordinateur. Elle contient une fonction qui permet d'uploader le code dans la mémoire ROM (un peu comme nous pourrions le faire avec certains micro-controlleur USB). À chaque coup d'horloge (clock/event), la cla... | stack_v2_sparse_classes_36k_train_011308 | 4,546 | permissive | [
{
"docstring": "Constructeur de la classe ROM. Le constructeur s'occupe d'initialiser la mémoire et lie ce composant avec le bus. :example: >>> test = ROM(modBus.Bus()) :param bus: Composant Bus du Micro-Ordinateur. :type bus: Bus",
"name": "__init__",
"signature": "def __init__(self, bus)"
},
{
... | 3 | stack_v2_sparse_classes_30k_train_003328 | Implement the Python class `ROM` described below.
Class description:
class ROM ======================== Cette classe représente la mémoire du micro-ordinateur. Elle contient une fonction qui permet d'uploader le code dans la mémoire ROM (un peu comme nous pourrions le faire avec certains micro-controlleur USB). À chaq... | Implement the Python class `ROM` described below.
Class description:
class ROM ======================== Cette classe représente la mémoire du micro-ordinateur. Elle contient une fonction qui permet d'uploader le code dans la mémoire ROM (un peu comme nous pourrions le faire avec certains micro-controlleur USB). À chaq... | 0a3a9b0deffa16e8c851eb53e6aad1a8983936e6 | <|skeleton|>
class ROM:
"""class ROM ======================== Cette classe représente la mémoire du micro-ordinateur. Elle contient une fonction qui permet d'uploader le code dans la mémoire ROM (un peu comme nous pourrions le faire avec certains micro-controlleur USB). À chaque coup d'horloge (clock/event), la cla... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ROM:
"""class ROM ======================== Cette classe représente la mémoire du micro-ordinateur. Elle contient une fonction qui permet d'uploader le code dans la mémoire ROM (un peu comme nous pourrions le faire avec certains micro-controlleur USB). À chaque coup d'horloge (clock/event), la classe vérifie s... | the_stack_v2_python_sparse | Modules/04-04-ROM.py | MarcAndreJean/PCONC | train | 0 |
ac32dc961bdc88a0c130842aada1d9ffcd011db9 | [
"args = parser.parse_args()\nif int(args['debug']) not in [1, 2]:\n raise Exception('The debug parameter is wrong, 1 is True and 2 is False')\ntry:\n control.system.system_config_create(platform_name=args['platform_name'], version_information=args['version_information'], copyright=args['copyright'], user_auth... | <|body_start_0|>
args = parser.parse_args()
if int(args['debug']) not in [1, 2]:
raise Exception('The debug parameter is wrong, 1 is True and 2 is False')
try:
control.system.system_config_create(platform_name=args['platform_name'], version_information=args['version_infor... | System | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class System:
def post(self):
"""初始化系统配置 --- tags: - system config parameters: - name: platform_name in: query type: string description: 平台名称 required: true - name: version_information type: string in: query description: 版本信息 - name: copyright type: string in: query description: 版权 - name: use... | stack_v2_sparse_classes_36k_train_011309 | 8,495 | no_license | [
{
"docstring": "初始化系统配置 --- tags: - system config parameters: - name: platform_name in: query type: string description: 平台名称 required: true - name: version_information type: string in: query description: 版本信息 - name: copyright type: string in: query description: 版权 - name: user_authentication_mode type: string ... | 3 | null | Implement the Python class `System` described below.
Class description:
Implement the System class.
Method signatures and docstrings:
- def post(self): 初始化系统配置 --- tags: - system config parameters: - name: platform_name in: query type: string description: 平台名称 required: true - name: version_information type: string i... | Implement the Python class `System` described below.
Class description:
Implement the System class.
Method signatures and docstrings:
- def post(self): 初始化系统配置 --- tags: - system config parameters: - name: platform_name in: query type: string description: 平台名称 required: true - name: version_information type: string i... | d25871dc66dfbd9f04e3d4d95843e39de286cfc8 | <|skeleton|>
class System:
def post(self):
"""初始化系统配置 --- tags: - system config parameters: - name: platform_name in: query type: string description: 平台名称 required: true - name: version_information type: string in: query description: 版本信息 - name: copyright type: string in: query description: 版权 - name: use... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class System:
def post(self):
"""初始化系统配置 --- tags: - system config parameters: - name: platform_name in: query type: string description: 平台名称 required: true - name: version_information type: string in: query description: 版本信息 - name: copyright type: string in: query description: 版权 - name: user_authenticati... | the_stack_v2_python_sparse | app/main/base/apis/system.py | zcl-organization/naguan | train | 0 | |
8fd147a88f6df4ee9c477c301c500fae331e8d70 | [
"self.results = {}\nself.calls = 0\nself.arg_names = None",
"if self.arg_names is not None:\n raise AssertionError('Instantiate a new Memoizer for each function')\nself.arg_names = inspect.getfullargspec(func).args\n\n@wraps(func)\ndef wrapper(*args, **kwargs):\n key = ', '.join([\"('{}', {})\".format(arg_n... | <|body_start_0|>
self.results = {}
self.calls = 0
self.arg_names = None
<|end_body_0|>
<|body_start_1|>
if self.arg_names is not None:
raise AssertionError('Instantiate a new Memoizer for each function')
self.arg_names = inspect.getfullargspec(func).args
@wr... | Memoizer | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Memoizer:
def __init__(self):
"""Class to store the results of a function given a set of inputs."""
<|body_0|>
def __call__(self, func):
"""Memoize decorator. Any time a function is called that a memoizer has been attached to its results are stored in the results dic... | stack_v2_sparse_classes_36k_train_011310 | 25,943 | permissive | [
{
"docstring": "Class to store the results of a function given a set of inputs.",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Memoize decorator. Any time a function is called that a memoizer has been attached to its results are stored in the results dictionary or ret... | 2 | stack_v2_sparse_classes_30k_test_000290 | Implement the Python class `Memoizer` described below.
Class description:
Implement the Memoizer class.
Method signatures and docstrings:
- def __init__(self): Class to store the results of a function given a set of inputs.
- def __call__(self, func): Memoize decorator. Any time a function is called that a memoizer h... | Implement the Python class `Memoizer` described below.
Class description:
Implement the Memoizer class.
Method signatures and docstrings:
- def __init__(self): Class to store the results of a function given a set of inputs.
- def __call__(self, func): Memoize decorator. Any time a function is called that a memoizer h... | c21e8859bdb20737352147b9904797ac99985b73 | <|skeleton|>
class Memoizer:
def __init__(self):
"""Class to store the results of a function given a set of inputs."""
<|body_0|>
def __call__(self, func):
"""Memoize decorator. Any time a function is called that a memoizer has been attached to its results are stored in the results dic... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Memoizer:
def __init__(self):
"""Class to store the results of a function given a set of inputs."""
self.results = {}
self.calls = 0
self.arg_names = None
def __call__(self, func):
"""Memoize decorator. Any time a function is called that a memoizer has been attache... | the_stack_v2_python_sparse | autoarray/structures/arrays/two_d/array_2d_util.py | jonathanfrawley/PyAutoArray_copy | train | 0 | |
114be6a63b7532b7a4dd36acd74b34a8c05c3549 | [
"assert len(master_key) in AES.rounds_by_key_size\nself.n_rounds = AES.rounds_by_key_size[len(master_key)]\nself._key_matrices = self._expand_key(master_key)",
"key_columns = bytes2matrix(master_key)\niteration_size = len(master_key) // 4\ni = 1\nwhile len(key_columns) < (self.n_rounds + 1) * 4:\n word = list(... | <|body_start_0|>
assert len(master_key) in AES.rounds_by_key_size
self.n_rounds = AES.rounds_by_key_size[len(master_key)]
self._key_matrices = self._expand_key(master_key)
<|end_body_0|>
<|body_start_1|>
key_columns = bytes2matrix(master_key)
iteration_size = len(master_key) // ... | AES | [
"Unlicense"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AES:
def __init__(self, master_key):
"""Initializes the object with a given key."""
<|body_0|>
def _expand_key(self, master_key):
"""Expands and returns a list of key matrices for the given master_key."""
<|body_1|>
def encrypt_block(self, plaintext):
... | stack_v2_sparse_classes_36k_train_011311 | 4,275 | permissive | [
{
"docstring": "Initializes the object with a given key.",
"name": "__init__",
"signature": "def __init__(self, master_key)"
},
{
"docstring": "Expands and returns a list of key matrices for the given master_key.",
"name": "_expand_key",
"signature": "def _expand_key(self, master_key)"
... | 3 | stack_v2_sparse_classes_30k_train_016781 | Implement the Python class `AES` described below.
Class description:
Implement the AES class.
Method signatures and docstrings:
- def __init__(self, master_key): Initializes the object with a given key.
- def _expand_key(self, master_key): Expands and returns a list of key matrices for the given master_key.
- def enc... | Implement the Python class `AES` described below.
Class description:
Implement the AES class.
Method signatures and docstrings:
- def __init__(self, master_key): Initializes the object with a given key.
- def _expand_key(self, master_key): Expands and returns a list of key matrices for the given master_key.
- def enc... | cda0db4888322cce759a7362de88fff5cc79f599 | <|skeleton|>
class AES:
def __init__(self, master_key):
"""Initializes the object with a given key."""
<|body_0|>
def _expand_key(self, master_key):
"""Expands and returns a list of key matrices for the given master_key."""
<|body_1|>
def encrypt_block(self, plaintext):
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AES:
def __init__(self, master_key):
"""Initializes the object with a given key."""
assert len(master_key) in AES.rounds_by_key_size
self.n_rounds = AES.rounds_by_key_size[len(master_key)]
self._key_matrices = self._expand_key(master_key)
def _expand_key(self, master_key):... | the_stack_v2_python_sparse | Codegate/2022 Finals/aesmaster/aes.py | Qwaz/solved-hacking-problem | train | 100 | |
f2b0fbc98fc2bd8fe90294854641aa4a3e97e921 | [
"self.event = None\nself.concrete_categories = None\nself.abstract_categories = None\nself.key = key\nif not regex_objects:\n if model:\n regex_objs = RegexCategory.objects.select_related().filter(model_type=model)\n else:\n regex_objs = RegexCategory.objects.all()\nelse:\n regex_objs = regex... | <|body_start_0|>
self.event = None
self.concrete_categories = None
self.abstract_categories = None
self.key = key
if not regex_objects:
if model:
regex_objs = RegexCategory.objects.select_related().filter(model_type=model)
else:
... | Use a regular expression to map the external category text to internal categories | RegexRule | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RegexRule:
"""Use a regular expression to map the external category text to internal categories"""
def __init__(self, key, model, regex_objects=None):
"""# note: regexes are compiled here- they are not strings self.source_rules = defaultdict(list) # built from a table self.null_rules... | stack_v2_sparse_classes_36k_train_011312 | 10,087 | no_license | [
{
"docstring": "# note: regexes are compiled here- they are not strings self.source_rules = defaultdict(list) # built from a table self.null_rules = [] # from items in table with source NULL for row in model: key(row) -> category each model is of the form: regular_expression We basically build 2 different types... | 2 | stack_v2_sparse_classes_30k_val_000946 | Implement the Python class `RegexRule` described below.
Class description:
Use a regular expression to map the external category text to internal categories
Method signatures and docstrings:
- def __init__(self, key, model, regex_objects=None): # note: regexes are compiled here- they are not strings self.source_rules... | Implement the Python class `RegexRule` described below.
Class description:
Use a regular expression to map the external category text to internal categories
Method signatures and docstrings:
- def __init__(self, key, model, regex_objects=None): # note: regexes are compiled here- they are not strings self.source_rules... | c4992d80f984f3360eb2018c5a1b13ce962a55b4 | <|skeleton|>
class RegexRule:
"""Use a regular expression to map the external category text to internal categories"""
def __init__(self, key, model, regex_objects=None):
"""# note: regexes are compiled here- they are not strings self.source_rules = defaultdict(list) # built from a table self.null_rules... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RegexRule:
"""Use a regular expression to map the external category text to internal categories"""
def __init__(self, key, model, regex_objects=None):
"""# note: regexes are compiled here- they are not strings self.source_rules = defaultdict(list) # built from a table self.null_rules = [] # from ... | the_stack_v2_python_sparse | abextra/pundit/classification_rules.py | danbretl/mid-tier | train | 0 |
620040f16423e947db1c5e7c291bb39f196554cf | [
"self.students = []\nself.grades = {}\nself.isSorted = True",
"if student in self.students:\n raise ValueError('Duplicate Error')\nself.students.append(student)\nself.grades[student.getIdNum()] = []\nself.isSorted = False",
"try:\n self.grades[student.getIdNum()].append(grade)\nexcept KeyError:\n raise... | <|body_start_0|>
self.students = []
self.grades = {}
self.isSorted = True
<|end_body_0|>
<|body_start_1|>
if student in self.students:
raise ValueError('Duplicate Error')
self.students.append(student)
self.grades[student.getIdNum()] = []
self.isSorted... | A mapping from students to a list of grades | Grades | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Grades:
"""A mapping from students to a list of grades"""
def __init__(self):
"""Creates empty grade book"""
<|body_0|>
def addStudent(self, student):
"""Assumes: student is of type student Add student to the grade book."""
<|body_1|>
def addGrades(s... | stack_v2_sparse_classes_36k_train_011313 | 2,545 | no_license | [
{
"docstring": "Creates empty grade book",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Assumes: student is of type student Add student to the grade book.",
"name": "addStudent",
"signature": "def addStudent(self, student)"
},
{
"docstring": "Assumes: ... | 6 | stack_v2_sparse_classes_30k_train_007527 | Implement the Python class `Grades` described below.
Class description:
A mapping from students to a list of grades
Method signatures and docstrings:
- def __init__(self): Creates empty grade book
- def addStudent(self, student): Assumes: student is of type student Add student to the grade book.
- def addGrades(self,... | Implement the Python class `Grades` described below.
Class description:
A mapping from students to a list of grades
Method signatures and docstrings:
- def __init__(self): Creates empty grade book
- def addStudent(self, student): Assumes: student is of type student Add student to the grade book.
- def addGrades(self,... | 93e5e2a5e9355b4dc94ce2071351ee4bf280b9a8 | <|skeleton|>
class Grades:
"""A mapping from students to a list of grades"""
def __init__(self):
"""Creates empty grade book"""
<|body_0|>
def addStudent(self, student):
"""Assumes: student is of type student Add student to the grade book."""
<|body_1|>
def addGrades(s... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Grades:
"""A mapping from students to a list of grades"""
def __init__(self):
"""Creates empty grade book"""
self.students = []
self.grades = {}
self.isSorted = True
def addStudent(self, student):
"""Assumes: student is of type student Add student to the grade... | the_stack_v2_python_sparse | python_programming_exercise/classHierarchy/Grades.py | dskeshav/python_practise | train | 0 |
cdd6d3f86cb6b835fb751b5260be3eaa29ee6a31 | [
"response = await self.response\nif not response.code.is_successful():\n raise error.ResponseWrappingError(response)\nreturn response",
"try:\n return await self.response\nexcept error.RenderableError as e:\n return e.to_message()\nexcept Exception:\n return Message(code=INTERNAL_SERVER_ERROR)"
] | <|body_start_0|>
response = await self.response
if not response.code.is_successful():
raise error.ResponseWrappingError(response)
return response
<|end_body_0|>
<|body_start_1|>
try:
return await self.response
except error.RenderableError as e:
... | A utility class that offers the :attr:`response_raising` and :attr:`response_nonraising` alternatives to waiting for the :attr:`response` future whose error states can be presented either as an unsuccessful response (eg. 4.04) or an exception. It also provides some internal tools for handling anything that has a :attr:... | BaseUnicastRequest | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BaseUnicastRequest:
"""A utility class that offers the :attr:`response_raising` and :attr:`response_nonraising` alternatives to waiting for the :attr:`response` future whose error states can be presented either as an unsuccessful response (eg. 4.04) or an exception. It also provides some internal... | stack_v2_sparse_classes_36k_train_011314 | 43,955 | permissive | [
{
"docstring": "An awaitable that returns if a response comes in and is successful, otherwise raises generic network exception or a :class:`.error.ResponseWrappingError` for unsuccessful responses. Experimental Interface.",
"name": "response_raising",
"signature": "async def response_raising(self)"
},... | 2 | null | Implement the Python class `BaseUnicastRequest` described below.
Class description:
A utility class that offers the :attr:`response_raising` and :attr:`response_nonraising` alternatives to waiting for the :attr:`response` future whose error states can be presented either as an unsuccessful response (eg. 4.04) or an ex... | Implement the Python class `BaseUnicastRequest` described below.
Class description:
A utility class that offers the :attr:`response_raising` and :attr:`response_nonraising` alternatives to waiting for the :attr:`response` future whose error states can be presented either as an unsuccessful response (eg. 4.04) or an ex... | 66bd6f66b5ba9f17c5c688dfae51a3aace7e0070 | <|skeleton|>
class BaseUnicastRequest:
"""A utility class that offers the :attr:`response_raising` and :attr:`response_nonraising` alternatives to waiting for the :attr:`response` future whose error states can be presented either as an unsuccessful response (eg. 4.04) or an exception. It also provides some internal... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class BaseUnicastRequest:
"""A utility class that offers the :attr:`response_raising` and :attr:`response_nonraising` alternatives to waiting for the :attr:`response` future whose error states can be presented either as an unsuccessful response (eg. 4.04) or an exception. It also provides some internal tools for ha... | the_stack_v2_python_sparse | aiocoap/protocol.py | chrysn/aiocoap | train | 258 |
6a930c8a1353c5becbaa2a1cf78191bacd40dd5c | [
"cur = head\na = []\nwhile cur:\n a.append(cur.val)\n cur = cur.next\nfor i in range(len(a)):\n if a[i] != a[len(a) - 1 - i]:\n return False\nreturn True",
"if not head.next:\n return True\ndummy = ListNode()\ndummy.next = head\none, two = (dummy, dummy)\nwhile two and two.next:\n one = one.... | <|body_start_0|>
cur = head
a = []
while cur:
a.append(cur.val)
cur = cur.next
for i in range(len(a)):
if a[i] != a[len(a) - 1 - i]:
return False
return True
<|end_body_0|>
<|body_start_1|>
if not head.next:
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def isPalindrome(self, head: ListNode) -> bool:
"""Time Complexity : O(N), Space Complexity : O(N) Solution : list만들어서 two pointer로 비교"""
<|body_0|>
def isPalindrome(self, head: ListNode) -> bool:
"""Time Complexity : O(N), Space Complexity : O(1) Solution ... | stack_v2_sparse_classes_36k_train_011315 | 2,407 | no_license | [
{
"docstring": "Time Complexity : O(N), Space Complexity : O(N) Solution : list만들어서 two pointer로 비교",
"name": "isPalindrome",
"signature": "def isPalindrome(self, head: ListNode) -> bool"
},
{
"docstring": "Time Complexity : O(N), Space Complexity : O(1) Solution : 아래 방식과 동일한데 절반 위치를 구하고 리스트를 분리... | 3 | stack_v2_sparse_classes_30k_train_021274 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def isPalindrome(self, head: ListNode) -> bool: Time Complexity : O(N), Space Complexity : O(N) Solution : list만들어서 two pointer로 비교
- def isPalindrome(self, head: ListNode) -> bo... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def isPalindrome(self, head: ListNode) -> bool: Time Complexity : O(N), Space Complexity : O(N) Solution : list만들어서 two pointer로 비교
- def isPalindrome(self, head: ListNode) -> bo... | c26aef2a59e5cc2d9b0658b9c7386a43267ff8a1 | <|skeleton|>
class Solution:
def isPalindrome(self, head: ListNode) -> bool:
"""Time Complexity : O(N), Space Complexity : O(N) Solution : list만들어서 two pointer로 비교"""
<|body_0|>
def isPalindrome(self, head: ListNode) -> bool:
"""Time Complexity : O(N), Space Complexity : O(1) Solution ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def isPalindrome(self, head: ListNode) -> bool:
"""Time Complexity : O(N), Space Complexity : O(N) Solution : list만들어서 two pointer로 비교"""
cur = head
a = []
while cur:
a.append(cur.val)
cur = cur.next
for i in range(len(a)):
... | the_stack_v2_python_sparse | Leetcode/Palindrome_Linked_List.py | hanwgyu/algorithm_problem_solving | train | 5 | |
38f68dffd2c450516dc303be3de45b34a725d797 | [
"n = len(nums)\nL, R = (0, 0)\ntotal = 0\nres = n + 1\nwhile R < n:\n total += nums[R]\n while total >= s:\n res = min(res, R - L + 1)\n total -= nums[L]\n L += 1\n R += 1\nreturn res if res < n + 1 else 0",
"if not nums or min(nums) > s or sum(nums) < s:\n return 0\nn = len(nums)... | <|body_start_0|>
n = len(nums)
L, R = (0, 0)
total = 0
res = n + 1
while R < n:
total += nums[R]
while total >= s:
res = min(res, R - L + 1)
total -= nums[L]
L += 1
R += 1
return res if re... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def minSubArrayLen(self, s, nums):
"""滑动窗口 如果窗口内的和>=s 窗口左边界右移,更新res,直到窗口内的和不再大于等于s 然后窗口右边界右移 :param s: :param nums: :return:"""
<|body_0|>
def minSubArrayLen2(self, s, nums):
"""想错了 应该用滑动窗口 超时 ls[i] 前i-1个数组的前缀和 :type s: int :type nums: List[int] :rtype: int... | stack_v2_sparse_classes_36k_train_011316 | 1,976 | no_license | [
{
"docstring": "滑动窗口 如果窗口内的和>=s 窗口左边界右移,更新res,直到窗口内的和不再大于等于s 然后窗口右边界右移 :param s: :param nums: :return:",
"name": "minSubArrayLen",
"signature": "def minSubArrayLen(self, s, nums)"
},
{
"docstring": "想错了 应该用滑动窗口 超时 ls[i] 前i-1个数组的前缀和 :type s: int :type nums: List[int] :rtype: int",
"name": "mi... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def minSubArrayLen(self, s, nums): 滑动窗口 如果窗口内的和>=s 窗口左边界右移,更新res,直到窗口内的和不再大于等于s 然后窗口右边界右移 :param s: :param nums: :return:
- def minSubArrayLen2(self, s, nums): 想错了 应该用滑动窗口 超时 ls[... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def minSubArrayLen(self, s, nums): 滑动窗口 如果窗口内的和>=s 窗口左边界右移,更新res,直到窗口内的和不再大于等于s 然后窗口右边界右移 :param s: :param nums: :return:
- def minSubArrayLen2(self, s, nums): 想错了 应该用滑动窗口 超时 ls[... | 5d3574ccd282d0146c83c286ae28d8baaabd4910 | <|skeleton|>
class Solution:
def minSubArrayLen(self, s, nums):
"""滑动窗口 如果窗口内的和>=s 窗口左边界右移,更新res,直到窗口内的和不再大于等于s 然后窗口右边界右移 :param s: :param nums: :return:"""
<|body_0|>
def minSubArrayLen2(self, s, nums):
"""想错了 应该用滑动窗口 超时 ls[i] 前i-1个数组的前缀和 :type s: int :type nums: List[int] :rtype: int... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def minSubArrayLen(self, s, nums):
"""滑动窗口 如果窗口内的和>=s 窗口左边界右移,更新res,直到窗口内的和不再大于等于s 然后窗口右边界右移 :param s: :param nums: :return:"""
n = len(nums)
L, R = (0, 0)
total = 0
res = n + 1
while R < n:
total += nums[R]
while total >= s:
... | the_stack_v2_python_sparse | 209_长度最小的子数组.py | lovehhf/LeetCode | train | 0 | |
f97d89b1feb547da59ea5e9d91a5d623d89fc423 | [
"self.traclist = traclist\nassert order in {1, 2, 3, 4, 5}\nself.order = order\ndiffusion = len(diff_coef) > 0\nself.diffusion = diffusion\nif diffusion:\n self.ids2 = grid.ids2\n self.diff_coef = diff_coef\nself.i0 = {}\nself.i1 = {}\nngbs = param['neighbours']\nfor d in 'ijk':\n i0 = 0\n i1 = 0\n i... | <|body_start_0|>
self.traclist = traclist
assert order in {1, 2, 3, 4, 5}
self.order = order
diffusion = len(diff_coef) > 0
self.diffusion = diffusion
if diffusion:
self.ids2 = grid.ids2
self.diff_coef = diff_coef
self.i0 = {}
self.... | Tracer_numerics | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Tracer_numerics:
def __init__(self, param, grid, traclist, order, diff_coef=[]):
"""- grid: nyles grid object - traclist: list of prognostic variables that are advected, e.g., traclist = ['b'] - order: can be 1, 3, or 5; sets the order of the upwind scheme used to calculate the advection... | stack_v2_sparse_classes_36k_train_011317 | 3,260 | permissive | [
{
"docstring": "- grid: nyles grid object - traclist: list of prognostic variables that are advected, e.g., traclist = ['b'] - order: can be 1, 3, or 5; sets the order of the upwind scheme used to calculate the advection of the tracer - diffcoef is a dictionnary with diffusion coefficient for each variable",
... | 2 | stack_v2_sparse_classes_30k_train_005839 | Implement the Python class `Tracer_numerics` described below.
Class description:
Implement the Tracer_numerics class.
Method signatures and docstrings:
- def __init__(self, param, grid, traclist, order, diff_coef=[]): - grid: nyles grid object - traclist: list of prognostic variables that are advected, e.g., traclist... | Implement the Python class `Tracer_numerics` described below.
Class description:
Implement the Tracer_numerics class.
Method signatures and docstrings:
- def __init__(self, param, grid, traclist, order, diff_coef=[]): - grid: nyles grid object - traclist: list of prognostic variables that are advected, e.g., traclist... | 8d5989699127f4897c3591f01f218b2f796ba938 | <|skeleton|>
class Tracer_numerics:
def __init__(self, param, grid, traclist, order, diff_coef=[]):
"""- grid: nyles grid object - traclist: list of prognostic variables that are advected, e.g., traclist = ['b'] - order: can be 1, 3, or 5; sets the order of the upwind scheme used to calculate the advection... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Tracer_numerics:
def __init__(self, param, grid, traclist, order, diff_coef=[]):
"""- grid: nyles grid object - traclist: list of prognostic variables that are advected, e.g., traclist = ['b'] - order: can be 1, 3, or 5; sets the order of the upwind scheme used to calculate the advection of the tracer... | the_stack_v2_python_sparse | core/tracer.py | pvthinker/Nyles | train | 19 | |
bba310e00c1afdffef382cfdc4ce467497ec007c | [
"super(FeedbackLinearizedParkController, self).__init__(path, L, is_ned, is_flat)\nassert type(acceleration) is numpy.ndarray, 'acceleration must be numpy array'\nassert acceleration.shape[1] == 3, 'acceleration must be nx3'\nassert acceleration.shape == path.shape, 'path and acceleration must ... | <|body_start_0|>
super(FeedbackLinearizedParkController, self).__init__(path, L, is_ned, is_flat)
assert type(acceleration) is numpy.ndarray, 'acceleration must be numpy array'
assert acceleration.shape[1] == 3, 'acceleration must be nx3'
assert acceleration.shape == path.shap... | A class generalizing the park controller to accelerating trajectories The park controller is developed for linear trajectories (ie tracking a line from one point to another). Here we generalize that through the use of feedback linearization. A nominal acceleration is provided for every point along the trajectory and is... | FeedbackLinearizedParkController | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FeedbackLinearizedParkController:
"""A class generalizing the park controller to accelerating trajectories The park controller is developed for linear trajectories (ie tracking a line from one point to another). Here we generalize that through the use of feedback linearization. A nominal accelera... | stack_v2_sparse_classes_36k_train_011318 | 19,298 | permissive | [
{
"docstring": "Constructor Arguments: path: an nx3 numpy specifying positions along the path in 3-d space acceleration: an nx3 numpy array specifying the nominal acceleration at each point on the path L: the lookahead distance on the path. is_ned: optional flag indicating if the path is given in north, east, d... | 2 | stack_v2_sparse_classes_30k_train_001951 | Implement the Python class `FeedbackLinearizedParkController` described below.
Class description:
A class generalizing the park controller to accelerating trajectories The park controller is developed for linear trajectories (ie tracking a line from one point to another). Here we generalize that through the use of fee... | Implement the Python class `FeedbackLinearizedParkController` described below.
Class description:
A class generalizing the park controller to accelerating trajectories The park controller is developed for linear trajectories (ie tracking a line from one point to another). Here we generalize that through the use of fee... | 6827886916e36432ce1d806f0a78edef6c9270d9 | <|skeleton|>
class FeedbackLinearizedParkController:
"""A class generalizing the park controller to accelerating trajectories The park controller is developed for linear trajectories (ie tracking a line from one point to another). Here we generalize that through the use of feedback linearization. A nominal accelera... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class FeedbackLinearizedParkController:
"""A class generalizing the park controller to accelerating trajectories The park controller is developed for linear trajectories (ie tracking a line from one point to another). Here we generalize that through the use of feedback linearization. A nominal acceleration is provi... | the_stack_v2_python_sparse | pybots/src/robot_control/path_following.py | aivian/robots | train | 0 |
740a08a2d13f21b2d2207e5ba94cabce294b0b7c | [
"super(KernelVar, self).__init__()\nself.embd_dim = embd_dim\nself.hidden_dim = hidden_dim\nself.kernel_dim = kernel_dim\nself.layer1 = nn.Linear(2 * embd_dim, hidden_dim)\nself.layer2 = nn.Linear(hidden_dim, hidden_dim)\nself.layer3 = nn.Linear(hidden_dim, kernel_dim)\nself.net = nn.Sequential(self.layer1, nn.ReLU... | <|body_start_0|>
super(KernelVar, self).__init__()
self.embd_dim = embd_dim
self.hidden_dim = hidden_dim
self.kernel_dim = kernel_dim
self.layer1 = nn.Linear(2 * embd_dim, hidden_dim)
self.layer2 = nn.Linear(hidden_dim, hidden_dim)
self.layer3 = nn.Linear(hidden_d... | KernelVar | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class KernelVar:
def __init__(self, embd_dim, hidden_dim, kernel_dim):
"""Currently, this creates a 2-hidden-layer network with ELU non-linearities."""
<|body_0|>
def forward(self, words):
"""Given words, returns batch_kernel of dimension [-1, kernel_dim]"""
<|body... | stack_v2_sparse_classes_36k_train_011319 | 30,546 | permissive | [
{
"docstring": "Currently, this creates a 2-hidden-layer network with ELU non-linearities.",
"name": "__init__",
"signature": "def __init__(self, embd_dim, hidden_dim, kernel_dim)"
},
{
"docstring": "Given words, returns batch_kernel of dimension [-1, kernel_dim]",
"name": "forward",
"si... | 2 | stack_v2_sparse_classes_30k_train_007557 | Implement the Python class `KernelVar` described below.
Class description:
Implement the KernelVar class.
Method signatures and docstrings:
- def __init__(self, embd_dim, hidden_dim, kernel_dim): Currently, this creates a 2-hidden-layer network with ELU non-linearities.
- def forward(self, words): Given words, return... | Implement the Python class `KernelVar` described below.
Class description:
Implement the KernelVar class.
Method signatures and docstrings:
- def __init__(self, embd_dim, hidden_dim, kernel_dim): Currently, this creates a 2-hidden-layer network with ELU non-linearities.
- def forward(self, words): Given words, return... | 86859b7612433cc6349b427b47c54986224e702a | <|skeleton|>
class KernelVar:
def __init__(self, embd_dim, hidden_dim, kernel_dim):
"""Currently, this creates a 2-hidden-layer network with ELU non-linearities."""
<|body_0|>
def forward(self, words):
"""Given words, returns batch_kernel of dimension [-1, kernel_dim]"""
<|body... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class KernelVar:
def __init__(self, embd_dim, hidden_dim, kernel_dim):
"""Currently, this creates a 2-hidden-layer network with ELU non-linearities."""
super(KernelVar, self).__init__()
self.embd_dim = embd_dim
self.hidden_dim = hidden_dim
self.kernel_dim = kernel_dim
... | the_stack_v2_python_sparse | dpp_nets/layers/layers.py | mbp28/dpp_nets | train | 1 | |
e0cfcac258f71173b2928e5b9a051d6277354935 | [
"campaign_objs = Campaign.objects.all().order_by('-id')\nserializer = CampaignSerializer(campaign_objs, many=True)\nreturn Response(create_response(serializer.data))",
"serializer = CampaignSerializer(data=request.data)\nif serializer.is_valid():\n serializer.save()\n return Response(create_response(seriali... | <|body_start_0|>
campaign_objs = Campaign.objects.all().order_by('-id')
serializer = CampaignSerializer(campaign_objs, many=True)
return Response(create_response(serializer.data))
<|end_body_0|>
<|body_start_1|>
serializer = CampaignSerializer(data=request.data)
if serializer.is... | this view is used to create,update,list and delete Campaign's | CampaignView | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CampaignView:
"""this view is used to create,update,list and delete Campaign's"""
def get(self, request):
"""get list of all campaigns"""
<|body_0|>
def post(self, request):
"""create new campaign"""
<|body_1|>
def put(self, request):
"""upda... | stack_v2_sparse_classes_36k_train_011320 | 48,154 | permissive | [
{
"docstring": "get list of all campaigns",
"name": "get",
"signature": "def get(self, request)"
},
{
"docstring": "create new campaign",
"name": "post",
"signature": "def post(self, request)"
},
{
"docstring": "update existing campaign",
"name": "put",
"signature": "def ... | 3 | stack_v2_sparse_classes_30k_train_001837 | Implement the Python class `CampaignView` described below.
Class description:
this view is used to create,update,list and delete Campaign's
Method signatures and docstrings:
- def get(self, request): get list of all campaigns
- def post(self, request): create new campaign
- def put(self, request): update existing cam... | Implement the Python class `CampaignView` described below.
Class description:
this view is used to create,update,list and delete Campaign's
Method signatures and docstrings:
- def get(self, request): get list of all campaigns
- def post(self, request): create new campaign
- def put(self, request): update existing cam... | 590d8f6d597b9bafa1d0263edb95490f16570c37 | <|skeleton|>
class CampaignView:
"""this view is used to create,update,list and delete Campaign's"""
def get(self, request):
"""get list of all campaigns"""
<|body_0|>
def post(self, request):
"""create new campaign"""
<|body_1|>
def put(self, request):
"""upda... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CampaignView:
"""this view is used to create,update,list and delete Campaign's"""
def get(self, request):
"""get list of all campaigns"""
campaign_objs = Campaign.objects.all().order_by('-id')
serializer = CampaignSerializer(campaign_objs, many=True)
return Response(create... | the_stack_v2_python_sparse | newscout_web/api/v1/views.py | rsqwerty/newscout_web | train | 0 |
3f2182fed3b446fed3e3b342c1f22ddb93e5d681 | [
"self._dbName = os.environ['MOPS_DBINSTANCE']\nself._instance = mopsInstance = Instance(self._dbName)\nself._conn = self._instance.get_dbh()\nself._cursor = self._conn.cursor()\nsql = 'select tracklet_id, v_ra, v_dec, v_tot, ' + 'v_ra_sigma, v_dec_sigma, pos_ang_deg, ' + 'gcr_arcsec, ext_epoch, ext_ra, ext_dec, ext... | <|body_start_0|>
self._dbName = os.environ['MOPS_DBINSTANCE']
self._instance = mopsInstance = Instance(self._dbName)
self._conn = self._instance.get_dbh()
self._cursor = self._conn.cursor()
sql = 'select tracklet_id, v_ra, v_dec, v_tot, ' + 'v_ra_sigma, v_dec_sigma, pos_ang_deg, ... | TrackletTests | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TrackletTests:
def setUp(self):
"""Just create an Orbit instance from data in the DB. We will use that instance in our tests. We choose a field with tracklets associated to it."""
<|body_0|>
def testFetchDetections(self):
"""Look at the number of detections associate... | stack_v2_sparse_classes_36k_train_011321 | 17,613 | no_license | [
{
"docstring": "Just create an Orbit instance from data in the DB. We will use that instance in our tests. We choose a field with tracklets associated to it.",
"name": "setUp",
"signature": "def setUp(self)"
},
{
"docstring": "Look at the number of detections associatedto this tracklet. Then inv... | 2 | stack_v2_sparse_classes_30k_test_000745 | Implement the Python class `TrackletTests` described below.
Class description:
Implement the TrackletTests class.
Method signatures and docstrings:
- def setUp(self): Just create an Orbit instance from data in the DB. We will use that instance in our tests. We choose a field with tracklets associated to it.
- def tes... | Implement the Python class `TrackletTests` described below.
Class description:
Implement the TrackletTests class.
Method signatures and docstrings:
- def setUp(self): Just create an Orbit instance from data in the DB. We will use that instance in our tests. We choose a field with tracklets associated to it.
- def tes... | 06858b7e935243da7a3f55b3e5097d6440e0c1c2 | <|skeleton|>
class TrackletTests:
def setUp(self):
"""Just create an Orbit instance from data in the DB. We will use that instance in our tests. We choose a field with tracklets associated to it."""
<|body_0|>
def testFetchDetections(self):
"""Look at the number of detections associate... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TrackletTests:
def setUp(self):
"""Just create an Orbit instance from data in the DB. We will use that instance in our tests. We choose a field with tracklets associated to it."""
self._dbName = os.environ['MOPS_DBINSTANCE']
self._instance = mopsInstance = Instance(self._dbName)
... | the_stack_v2_python_sparse | python/MOPS/test.py | ldenneau/mopsng | train | 0 | |
1d433af29808bd16c2a16eb9955d1ec66eed418d | [
"created = self['created_at']\ncreated = fromisoformat_z(created)\nreturn created",
"updated = self['updated_at']\nupdated = fromisoformat_z(updated)\nreturn updated"
] | <|body_start_0|>
created = self['created_at']
created = fromisoformat_z(created)
return created
<|end_body_0|>
<|body_start_1|>
updated = self['updated_at']
updated = fromisoformat_z(updated)
return updated
<|end_body_1|>
| Base class for miscellaneous 1Password objects as returned by 'op get <object class>' | OPBaseObject | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class OPBaseObject:
"""Base class for miscellaneous 1Password objects as returned by 'op get <object class>'"""
def created_at(self) -> datetime:
"""datetime : The created_at attribute parsed as a datetime object"""
<|body_0|>
def updated_at(self) -> datetime:
"""datet... | stack_v2_sparse_classes_36k_train_011322 | 11,729 | permissive | [
{
"docstring": "datetime : The created_at attribute parsed as a datetime object",
"name": "created_at",
"signature": "def created_at(self) -> datetime"
},
{
"docstring": "datetime : The updated_at attribute parsed as a datetime object",
"name": "updated_at",
"signature": "def updated_at(... | 2 | stack_v2_sparse_classes_30k_train_008665 | Implement the Python class `OPBaseObject` described below.
Class description:
Base class for miscellaneous 1Password objects as returned by 'op get <object class>'
Method signatures and docstrings:
- def created_at(self) -> datetime: datetime : The created_at attribute parsed as a datetime object
- def updated_at(sel... | Implement the Python class `OPBaseObject` described below.
Class description:
Base class for miscellaneous 1Password objects as returned by 'op get <object class>'
Method signatures and docstrings:
- def created_at(self) -> datetime: datetime : The created_at attribute parsed as a datetime object
- def updated_at(sel... | 3ced5acf3667f1af73cad26ae0ef31e8c4b19585 | <|skeleton|>
class OPBaseObject:
"""Base class for miscellaneous 1Password objects as returned by 'op get <object class>'"""
def created_at(self) -> datetime:
"""datetime : The created_at attribute parsed as a datetime object"""
<|body_0|>
def updated_at(self) -> datetime:
"""datet... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class OPBaseObject:
"""Base class for miscellaneous 1Password objects as returned by 'op get <object class>'"""
def created_at(self) -> datetime:
"""datetime : The created_at attribute parsed as a datetime object"""
created = self['created_at']
created = fromisoformat_z(created)
... | the_stack_v2_python_sparse | pyonepassword/op_objects.py | zcutlip/pyonepassword | train | 48 |
01caf40f97f5504a55072101e5c74f35e11d43d6 | [
"import skipthoughts\nself.encode = skipthoughts.encode\nif datadir is None:\n datadir = os.path.realpath('__file__')\nself.datadir = self.datadir\nself.cache_table = {}\nself.uni_bi = uni_bi\nif uni_bi in ('uni', 'bi'):\n self.N = 2400\nelif uni_bi == 'combined':\n self.N = 4800\nelse:\n raise ValueErr... | <|body_start_0|>
import skipthoughts
self.encode = skipthoughts.encode
if datadir is None:
datadir = os.path.realpath('__file__')
self.datadir = self.datadir
self.cache_table = {}
self.uni_bi = uni_bi
if uni_bi in ('uni', 'bi'):
self.N = 24... | SkipThought | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SkipThought:
def __init__(self, datadir, uni_bi='combined'):
"""Embed Skip_Thought vectors, using precomputed model in npy format. Args: uni_bi: possible values are "uni", "bi" or "combined" determining what kind of embedding should be used. todo: is argument ndim working properly?"""
... | stack_v2_sparse_classes_36k_train_011323 | 5,893 | no_license | [
{
"docstring": "Embed Skip_Thought vectors, using precomputed model in npy format. Args: uni_bi: possible values are \"uni\", \"bi\" or \"combined\" determining what kind of embedding should be used. todo: is argument ndim working properly?",
"name": "__init__",
"signature": "def __init__(self, datadir,... | 2 | null | Implement the Python class `SkipThought` described below.
Class description:
Implement the SkipThought class.
Method signatures and docstrings:
- def __init__(self, datadir, uni_bi='combined'): Embed Skip_Thought vectors, using precomputed model in npy format. Args: uni_bi: possible values are "uni", "bi" or "combine... | Implement the Python class `SkipThought` described below.
Class description:
Implement the SkipThought class.
Method signatures and docstrings:
- def __init__(self, datadir, uni_bi='combined'): Embed Skip_Thought vectors, using precomputed model in npy format. Args: uni_bi: possible values are "uni", "bi" or "combine... | d494b3041069d377d6a7a9c296a14334f2fa5acc | <|skeleton|>
class SkipThought:
def __init__(self, datadir, uni_bi='combined'):
"""Embed Skip_Thought vectors, using precomputed model in npy format. Args: uni_bi: possible values are "uni", "bi" or "combined" determining what kind of embedding should be used. todo: is argument ndim working properly?"""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SkipThought:
def __init__(self, datadir, uni_bi='combined'):
"""Embed Skip_Thought vectors, using precomputed model in npy format. Args: uni_bi: possible values are "uni", "bi" or "combined" determining what kind of embedding should be used. todo: is argument ndim working properly?"""
import s... | the_stack_v2_python_sparse | python/brmson_dataset-sts/dataset-sts-master/pysts/embedding.py | LiuFang816/SALSTM_py_data | train | 10 | |
40afbc8e67eed4a8aebbd09f6555623008e9a649 | [
"Aperture.__init__(self, diameter, origin, theta, blocker_diameter, flipped)\nself.f = focal_length\nself.matrix = np.array([[1.0, 0], [-1.0 / self.f, 1.0]])\nself.draw_arcs = False",
"plotted_objects = Element.plot(self, ax)\nplotted_objects += plotting.plot_aperture(ax, self)\nif plot_blockers:\n plotted_obj... | <|body_start_0|>
Aperture.__init__(self, diameter, origin, theta, blocker_diameter, flipped)
self.f = focal_length
self.matrix = np.array([[1.0, 0], [-1.0 / self.f, 1.0]])
self.draw_arcs = False
<|end_body_0|>
<|body_start_1|>
plotted_objects = Element.plot(self, ax)
plo... | Lens | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Lens:
def __init__(self, focal_length: float, diameter: float, origin=[0.0, 0.0], theta=0.0, blocker_diameter: float=float('+Inf'), flipped=False):
"""Creates an lens element Args: focal_length: (float) focal length of the lens diameter: (float) diameter of the lens origin: position of t... | stack_v2_sparse_classes_36k_train_011324 | 2,387 | permissive | [
{
"docstring": "Creates an lens element Args: focal_length: (float) focal length of the lens diameter: (float) diameter of the lens origin: position of the center of the lens theta: rotation angle of aperture (with respect the abscissa) blocker_diameter: (float, optional) size of the aperture blocker flipped: (... | 2 | stack_v2_sparse_classes_30k_train_004875 | Implement the Python class `Lens` described below.
Class description:
Implement the Lens class.
Method signatures and docstrings:
- def __init__(self, focal_length: float, diameter: float, origin=[0.0, 0.0], theta=0.0, blocker_diameter: float=float('+Inf'), flipped=False): Creates an lens element Args: focal_length: ... | Implement the Python class `Lens` described below.
Class description:
Implement the Lens class.
Method signatures and docstrings:
- def __init__(self, focal_length: float, diameter: float, origin=[0.0, 0.0], theta=0.0, blocker_diameter: float=float('+Inf'), flipped=False): Creates an lens element Args: focal_length: ... | 145a87c075cd66c45db7cccb5672d6103b4c3547 | <|skeleton|>
class Lens:
def __init__(self, focal_length: float, diameter: float, origin=[0.0, 0.0], theta=0.0, blocker_diameter: float=float('+Inf'), flipped=False):
"""Creates an lens element Args: focal_length: (float) focal length of the lens diameter: (float) diameter of the lens origin: position of t... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Lens:
def __init__(self, focal_length: float, diameter: float, origin=[0.0, 0.0], theta=0.0, blocker_diameter: float=float('+Inf'), flipped=False):
"""Creates an lens element Args: focal_length: (float) focal length of the lens diameter: (float) diameter of the lens origin: position of the center of t... | the_stack_v2_python_sparse | raypy2d/elements/lens.py | null179/python-raypy | train | 0 | |
3b93749b15014d6832d4848b651754e25fc57837 | [
"user = UserFactory.create()\ncourse = CourseFactory.create()\navailable_grade = ProctoredExamGradeFactory.create(user=user, course=course, exam_run__course=course, exam_run__eligibility_past=True)\nProctoredExamGradeFactory.create(user=user, course=course, exam_run__course=course, exam_run__eligibility_future=True... | <|body_start_0|>
user = UserFactory.create()
course = CourseFactory.create()
available_grade = ProctoredExamGradeFactory.create(user=user, course=course, exam_run__course=course, exam_run__eligibility_past=True)
ProctoredExamGradeFactory.create(user=user, course=course, exam_run__course=... | Tests for ProctoredExamGrade | ProctoredExamGradeTests | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ProctoredExamGradeTests:
"""Tests for ProctoredExamGrade"""
def test_for_user_course(self):
"""Tests that for_user_course() does not return unavailable grades"""
<|body_0|>
def test_set_score(self, grade_adjust, expected_passed_value, expected_grade_str):
"""Test... | stack_v2_sparse_classes_36k_train_011325 | 8,033 | permissive | [
{
"docstring": "Tests that for_user_course() does not return unavailable grades",
"name": "test_for_user_course",
"signature": "def test_for_user_course(self)"
},
{
"docstring": "Tests that the set_score helper method sets score-related fields appropriately",
"name": "test_set_score",
"s... | 3 | null | Implement the Python class `ProctoredExamGradeTests` described below.
Class description:
Tests for ProctoredExamGrade
Method signatures and docstrings:
- def test_for_user_course(self): Tests that for_user_course() does not return unavailable grades
- def test_set_score(self, grade_adjust, expected_passed_value, expe... | Implement the Python class `ProctoredExamGradeTests` described below.
Class description:
Tests for ProctoredExamGrade
Method signatures and docstrings:
- def test_for_user_course(self): Tests that for_user_course() does not return unavailable grades
- def test_set_score(self, grade_adjust, expected_passed_value, expe... | d6564caca0b7bbfd31e67a751564107fd17d6eb0 | <|skeleton|>
class ProctoredExamGradeTests:
"""Tests for ProctoredExamGrade"""
def test_for_user_course(self):
"""Tests that for_user_course() does not return unavailable grades"""
<|body_0|>
def test_set_score(self, grade_adjust, expected_passed_value, expected_grade_str):
"""Test... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ProctoredExamGradeTests:
"""Tests for ProctoredExamGrade"""
def test_for_user_course(self):
"""Tests that for_user_course() does not return unavailable grades"""
user = UserFactory.create()
course = CourseFactory.create()
available_grade = ProctoredExamGradeFactory.create(... | the_stack_v2_python_sparse | grades/models_test.py | mitodl/micromasters | train | 35 |
5629ad020469bb4f0749842a5e0a615cc8c15d4c | [
"Frame.__init__(self, master)\nself.pack()\nself.createArtistWidgets()",
"top_frame = Frame(self)\nself.labelInput = Label(top_frame, text='Artist Name')\nself.text_in = Entry(top_frame)\nself.labelResult = Label(top_frame, text='Result')\nself.labelInput.pack()\nself.text_in.pack()\nself.labelResult.pack()\ntop_... | <|body_start_0|>
Frame.__init__(self, master)
self.pack()
self.createArtistWidgets()
<|end_body_0|>
<|body_start_1|>
top_frame = Frame(self)
self.labelInput = Label(top_frame, text='Artist Name')
self.text_in = Entry(top_frame)
self.labelResult = Label(top_frame,... | Application main window class. | getArtist_UI | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class getArtist_UI:
"""Application main window class."""
def __init__(self, master=None):
"""Main frame initialization (mostly delegated)"""
<|body_0|>
def createArtistWidgets(self):
"""Add all the widgets to the main frame."""
<|body_1|>
def handle(self):... | stack_v2_sparse_classes_36k_train_011326 | 10,077 | no_license | [
{
"docstring": "Main frame initialization (mostly delegated)",
"name": "__init__",
"signature": "def __init__(self, master=None)"
},
{
"docstring": "Add all the widgets to the main frame.",
"name": "createArtistWidgets",
"signature": "def createArtistWidgets(self)"
},
{
"docstrin... | 3 | stack_v2_sparse_classes_30k_train_020408 | Implement the Python class `getArtist_UI` described below.
Class description:
Application main window class.
Method signatures and docstrings:
- def __init__(self, master=None): Main frame initialization (mostly delegated)
- def createArtistWidgets(self): Add all the widgets to the main frame.
- def handle(self): Han... | Implement the Python class `getArtist_UI` described below.
Class description:
Application main window class.
Method signatures and docstrings:
- def __init__(self, master=None): Main frame initialization (mostly delegated)
- def createArtistWidgets(self): Add all the widgets to the main frame.
- def handle(self): Han... | 2dba11861f91e4bdc1ef28279132a6d8dd4ccf54 | <|skeleton|>
class getArtist_UI:
"""Application main window class."""
def __init__(self, master=None):
"""Main frame initialization (mostly delegated)"""
<|body_0|>
def createArtistWidgets(self):
"""Add all the widgets to the main frame."""
<|body_1|>
def handle(self):... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class getArtist_UI:
"""Application main window class."""
def __init__(self, master=None):
"""Main frame initialization (mostly delegated)"""
Frame.__init__(self, master)
self.pack()
self.createArtistWidgets()
def createArtistWidgets(self):
"""Add all the widgets to ... | the_stack_v2_python_sparse | Mux_src/Fix_All_Music_Guis.py | rduvalwa5/Mux | train | 0 |
774651c63242d17c9e71ace0381e97dbf5109f70 | [
"super(SeriesIdWorker, self).__init__()\nself.q = genre_url_queue\nself.set_lock = set_lock\nself.id_set = id_set\nself.crawler = SeriesCrawler()\nself.logger = log.logger",
"self.set_lock.acquire()\nfor s_id in s_ids:\n self.id_set.add(s_id)\nself.set_lock.release()",
"while True:\n new_url = self.q.get(... | <|body_start_0|>
super(SeriesIdWorker, self).__init__()
self.q = genre_url_queue
self.set_lock = set_lock
self.id_set = id_set
self.crawler = SeriesCrawler()
self.logger = log.logger
<|end_body_0|>
<|body_start_1|>
self.set_lock.acquire()
for s_id in s_id... | Thread that works on contributing to the bag of series_ids retrieved from iTunes, indicating what podcasts are currently available on iTunes | SeriesIdWorker | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SeriesIdWorker:
"""Thread that works on contributing to the bag of series_ids retrieved from iTunes, indicating what podcasts are currently available on iTunes"""
def __init__(self, genre_url_queue, id_set, set_lock):
"""Constructor: genre_url_queue [string Queue] - Concurrently-safe... | stack_v2_sparse_classes_36k_train_011327 | 6,254 | permissive | [
{
"docstring": "Constructor: genre_url_queue [string Queue] - Concurrently-safe queue filled with genre paginated URLs that we're grabbing series_ids froms id_set [set of ints] - Set of series_ids seen set_lock [Lock] - Lock needed to add things to the global id_set",
"name": "__init__",
"signature": "d... | 3 | null | Implement the Python class `SeriesIdWorker` described below.
Class description:
Thread that works on contributing to the bag of series_ids retrieved from iTunes, indicating what podcasts are currently available on iTunes
Method signatures and docstrings:
- def __init__(self, genre_url_queue, id_set, set_lock): Constr... | Implement the Python class `SeriesIdWorker` described below.
Class description:
Thread that works on contributing to the bag of series_ids retrieved from iTunes, indicating what podcasts are currently available on iTunes
Method signatures and docstrings:
- def __init__(self, genre_url_queue, id_set, set_lock): Constr... | 061d0f9cccf278363ffaeb27fc655743b1052ae5 | <|skeleton|>
class SeriesIdWorker:
"""Thread that works on contributing to the bag of series_ids retrieved from iTunes, indicating what podcasts are currently available on iTunes"""
def __init__(self, genre_url_queue, id_set, set_lock):
"""Constructor: genre_url_queue [string Queue] - Concurrently-safe... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SeriesIdWorker:
"""Thread that works on contributing to the bag of series_ids retrieved from iTunes, indicating what podcasts are currently available on iTunes"""
def __init__(self, genre_url_queue, id_set, set_lock):
"""Constructor: genre_url_queue [string Queue] - Concurrently-safe queue filled... | the_stack_v2_python_sparse | podcatch/utpodcatch/ils/series_grabber.py | cuappdev/archives | train | 0 |
e1656ffdaf59263dedc65a40ae50f6ab1f052084 | [
"self.servo1 = s1\nself.servo2 = s2\nself.servo3 = s3\nself.servo4 = s4\nself.speed_list = speed_list\nif calibrate:\n self.servo1.calibration()\n self.servo2.calibration()\n self.servo3.calibration()\n self.servo4.calibration()\ns1.speed(0)\ns2.speed(0)\ns3.speed(0)\ns4.speed(0)\nself.offset = offset",... | <|body_start_0|>
self.servo1 = s1
self.servo2 = s2
self.servo3 = s3
self.servo4 = s4
self.speed_list = speed_list
if calibrate:
self.servo1.calibration()
self.servo2.calibration()
self.servo3.calibration()
self.servo4.calibr... | Task for sending the most up to date outputs to the motor :param s1: Parameter is the first servo object. :param s2: Parameter is the second servo object. :param s3: Parameter is the third servo object. :param s4: Parameter is the fourth servo object. :param speed_list: A parameter containing a list of commanded speeds... | MotorControlTask | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MotorControlTask:
"""Task for sending the most up to date outputs to the motor :param s1: Parameter is the first servo object. :param s2: Parameter is the second servo object. :param s3: Parameter is the third servo object. :param s4: Parameter is the fourth servo object. :param speed_list: A par... | stack_v2_sparse_classes_36k_train_011328 | 1,815 | no_license | [
{
"docstring": "Constructor for the MotorControlTask",
"name": "__init__",
"signature": "def __init__(self, s1, s2, s3, s4, speed_list, offset=0, calibrate=False)"
},
{
"docstring": "Method to update the speed of each motor with the newest outputs",
"name": "run",
"signature": "def run(s... | 2 | stack_v2_sparse_classes_30k_test_000987 | Implement the Python class `MotorControlTask` described below.
Class description:
Task for sending the most up to date outputs to the motor :param s1: Parameter is the first servo object. :param s2: Parameter is the second servo object. :param s3: Parameter is the third servo object. :param s4: Parameter is the fourth... | Implement the Python class `MotorControlTask` described below.
Class description:
Task for sending the most up to date outputs to the motor :param s1: Parameter is the first servo object. :param s2: Parameter is the second servo object. :param s3: Parameter is the third servo object. :param s4: Parameter is the fourth... | 4be3c9dd4dc2ef1c9d74502ad30900627fe5f862 | <|skeleton|>
class MotorControlTask:
"""Task for sending the most up to date outputs to the motor :param s1: Parameter is the first servo object. :param s2: Parameter is the second servo object. :param s3: Parameter is the third servo object. :param s4: Parameter is the fourth servo object. :param speed_list: A par... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MotorControlTask:
"""Task for sending the most up to date outputs to the motor :param s1: Parameter is the first servo object. :param s2: Parameter is the second servo object. :param s3: Parameter is the third servo object. :param s4: Parameter is the fourth servo object. :param speed_list: A parameter contai... | the_stack_v2_python_sparse | src/motor_control_task.py | mfgeorge/pyCopter | train | 5 |
65d88785f79bd16f39bfc2dad2f2d76f92978334 | [
"super(Encoder, self).__init__()\nself.hidden_dim = hidden_dim // 2 if bidir else hidden_dim\nself.n_layers = n_layers * 2 if bidir else n_layers\nself.bidir = bidir\nself.lstm = nn.LSTM(embedding_dim, self.hidden_dim, n_layers, dropout=dropout, bidirectional=bidir)\nself.h0 = Parameter(torch.zeros(1), requires_gra... | <|body_start_0|>
super(Encoder, self).__init__()
self.hidden_dim = hidden_dim // 2 if bidir else hidden_dim
self.n_layers = n_layers * 2 if bidir else n_layers
self.bidir = bidir
self.lstm = nn.LSTM(embedding_dim, self.hidden_dim, n_layers, dropout=dropout, bidirectional=bidir)
... | Encoder class for Pointer-Net | Encoder | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Encoder:
"""Encoder class for Pointer-Net"""
def __init__(self, embedding_dim, hidden_dim, n_layers, dropout, bidir):
"""Initiate Encoder :param Tensor embedding_dim: Number of embbeding channels :param int hidden_dim: Number of hidden units for the LSTM :param int n_layers: Number o... | stack_v2_sparse_classes_36k_train_011329 | 12,775 | no_license | [
{
"docstring": "Initiate Encoder :param Tensor embedding_dim: Number of embbeding channels :param int hidden_dim: Number of hidden units for the LSTM :param int n_layers: Number of layers for LSTMs :param float dropout: Float between 0-1 :param bool bidir: Bidirectional",
"name": "__init__",
"signature"... | 3 | stack_v2_sparse_classes_30k_train_005998 | Implement the Python class `Encoder` described below.
Class description:
Encoder class for Pointer-Net
Method signatures and docstrings:
- def __init__(self, embedding_dim, hidden_dim, n_layers, dropout, bidir): Initiate Encoder :param Tensor embedding_dim: Number of embbeding channels :param int hidden_dim: Number o... | Implement the Python class `Encoder` described below.
Class description:
Encoder class for Pointer-Net
Method signatures and docstrings:
- def __init__(self, embedding_dim, hidden_dim, n_layers, dropout, bidir): Initiate Encoder :param Tensor embedding_dim: Number of embbeding channels :param int hidden_dim: Number o... | 86a480b9196053cee9a3287e023dd12a13bb5df8 | <|skeleton|>
class Encoder:
"""Encoder class for Pointer-Net"""
def __init__(self, embedding_dim, hidden_dim, n_layers, dropout, bidir):
"""Initiate Encoder :param Tensor embedding_dim: Number of embbeding channels :param int hidden_dim: Number of hidden units for the LSTM :param int n_layers: Number o... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Encoder:
"""Encoder class for Pointer-Net"""
def __init__(self, embedding_dim, hidden_dim, n_layers, dropout, bidir):
"""Initiate Encoder :param Tensor embedding_dim: Number of embbeding channels :param int hidden_dim: Number of hidden units for the LSTM :param int n_layers: Number of layers for ... | the_stack_v2_python_sparse | PointerNet.py | EleanorHYW/Rerank | train | 0 |
c87919dc93fafaa2d8e584a99a9487bedd084d4f | [
"if velocity is not None:\n numvel = len(velocity)\n myvel = velocity\n if numvel > dim:\n dim = numvel\n elif numvel < dim:\n myvel = np.zeros(shape=(dim,))\n for i in range(numvel):\n myvel[i] = velocity[i]\n self._velocity = myvel\nelse:\n self._velocity = np.zer... | <|body_start_0|>
if velocity is not None:
numvel = len(velocity)
myvel = velocity
if numvel > dim:
dim = numvel
elif numvel < dim:
myvel = np.zeros(shape=(dim,))
for i in range(numvel):
myvel[i] =... | Solution initializer for a uniform flow. A uniform flow is the same everywhere and should have a zero RHS. .. automethod:: __init__ .. automethod:: __call__ .. automethod:: exact_rhs | Uniform | [
"X11",
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Uniform:
"""Solution initializer for a uniform flow. A uniform flow is the same everywhere and should have a zero RHS. .. automethod:: __init__ .. automethod:: __call__ .. automethod:: exact_rhs"""
def __init__(self, *, dim=1, nspecies=0, rho=1.0, p=1.0, e=2.5, velocity=None, mass_fracs=None... | stack_v2_sparse_classes_36k_train_011330 | 32,800 | permissive | [
{
"docstring": "Initialize uniform flow parameters. Parameters ---------- dim: int specify the number of dimensions for the flow nspecies: int specify the number of species in the flow rho: float specifies the density p: float specifies the pressure e: float specifies the internal energy velocity: numpy.ndarray... | 3 | stack_v2_sparse_classes_30k_train_017545 | Implement the Python class `Uniform` described below.
Class description:
Solution initializer for a uniform flow. A uniform flow is the same everywhere and should have a zero RHS. .. automethod:: __init__ .. automethod:: __call__ .. automethod:: exact_rhs
Method signatures and docstrings:
- def __init__(self, *, dim=... | Implement the Python class `Uniform` described below.
Class description:
Solution initializer for a uniform flow. A uniform flow is the same everywhere and should have a zero RHS. .. automethod:: __init__ .. automethod:: __call__ .. automethod:: exact_rhs
Method signatures and docstrings:
- def __init__(self, *, dim=... | 47f144782258eae2b1fb39520e96f414ae176ff4 | <|skeleton|>
class Uniform:
"""Solution initializer for a uniform flow. A uniform flow is the same everywhere and should have a zero RHS. .. automethod:: __init__ .. automethod:: __call__ .. automethod:: exact_rhs"""
def __init__(self, *, dim=1, nspecies=0, rho=1.0, p=1.0, e=2.5, velocity=None, mass_fracs=None... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Uniform:
"""Solution initializer for a uniform flow. A uniform flow is the same everywhere and should have a zero RHS. .. automethod:: __init__ .. automethod:: __call__ .. automethod:: exact_rhs"""
def __init__(self, *, dim=1, nspecies=0, rho=1.0, p=1.0, e=2.5, velocity=None, mass_fracs=None):
""... | the_stack_v2_python_sparse | mirgecom/initializers.py | kaushikcfd/mirgecom | train | 0 |
46850c8332c1f12a8f83b9b691ffedd863f2c29d | [
"try:\n return self.load_cached_obj('native.coordinates')\nexcept:\n pass\nds = self.dataset\ntimes = self.get_available_dates()\nlons = podpac.crange(ds['lon'][0], ds['lon'][-1], ds['lon'][1] - ds['lon'][0])\nlats = podpac.crange(ds['lat'][0], ds['lat'][-1], ds['lat'][1] - ds['lat'][0])\ncoords = podpac.Coor... | <|body_start_0|>
try:
return self.load_cached_obj('native.coordinates')
except:
pass
ds = self.dataset
times = self.get_available_dates()
lons = podpac.crange(ds['lon'][0], ds['lon'][-1], ds['lon'][1] - ds['lon'][0])
lats = podpac.crange(ds['lat'][... | Summary Attributes ---------- base_dir_url : TYPE Description base_url : TYPE Description date_url_re : TYPE Description product : TYPE Description site : TYPE Description | AirMOSS_Site | [
"Apache-2.0",
"LicenseRef-scancode-warranty-disclaimer"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AirMOSS_Site:
"""Summary Attributes ---------- base_dir_url : TYPE Description base_url : TYPE Description date_url_re : TYPE Description product : TYPE Description site : TYPE Description"""
def get_native_coordinates(self):
"""Summary Returns ------- TYPE Description"""
<|b... | stack_v2_sparse_classes_36k_train_011331 | 5,736 | permissive | [
{
"docstring": "Summary Returns ------- TYPE Description",
"name": "get_native_coordinates",
"signature": "def get_native_coordinates(self)"
},
{
"docstring": "Summary Returns ------- TYPE Description",
"name": "get_available_dates",
"signature": "def get_available_dates(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_017417 | Implement the Python class `AirMOSS_Site` described below.
Class description:
Summary Attributes ---------- base_dir_url : TYPE Description base_url : TYPE Description date_url_re : TYPE Description product : TYPE Description site : TYPE Description
Method signatures and docstrings:
- def get_native_coordinates(self)... | Implement the Python class `AirMOSS_Site` described below.
Class description:
Summary Attributes ---------- base_dir_url : TYPE Description base_url : TYPE Description date_url_re : TYPE Description product : TYPE Description site : TYPE Description
Method signatures and docstrings:
- def get_native_coordinates(self)... | 0a96a9b3726aee9bb6208244ae96ed685667e16c | <|skeleton|>
class AirMOSS_Site:
"""Summary Attributes ---------- base_dir_url : TYPE Description base_url : TYPE Description date_url_re : TYPE Description product : TYPE Description site : TYPE Description"""
def get_native_coordinates(self):
"""Summary Returns ------- TYPE Description"""
<|b... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AirMOSS_Site:
"""Summary Attributes ---------- base_dir_url : TYPE Description base_url : TYPE Description date_url_re : TYPE Description product : TYPE Description site : TYPE Description"""
def get_native_coordinates(self):
"""Summary Returns ------- TYPE Description"""
try:
... | the_stack_v2_python_sparse | podpac/datalib/airmoss.py | ccuadrado/podpac | train | 0 |
e9c21be15e811cdaed551b974b9cc3bfa994ad37 | [
"step = 0\nmedian_p = (len(nums1) + len(nums2) - 1) / 2\nmedian = []\nwhile True:\n if step - median_p >= 1:\n break\n if abs(step - median_p) <= 0.5:\n if len(nums1) == 0:\n median.append(nums2[0])\n elif len(nums2) == 0:\n median.append(nums1[0])\n else:\n ... | <|body_start_0|>
step = 0
median_p = (len(nums1) + len(nums2) - 1) / 2
median = []
while True:
if step - median_p >= 1:
break
if abs(step - median_p) <= 0.5:
if len(nums1) == 0:
median.append(nums2[0])
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def findMedianSortedArrays(self, nums1: List[int], nums2: List[int]) -> float:
"""Set two pointer, get rid of min of two arrays each step. [1, 2] [_, 2] ^ => ^ [3, 4] [3, 4] ^ ^"""
<|body_0|>
def findMedianSortedArrays_2(self, nums1: List[int], nums2: List[int]) ->... | stack_v2_sparse_classes_36k_train_011332 | 2,905 | no_license | [
{
"docstring": "Set two pointer, get rid of min of two arrays each step. [1, 2] [_, 2] ^ => ^ [3, 4] [3, 4] ^ ^",
"name": "findMedianSortedArrays",
"signature": "def findMedianSortedArrays(self, nums1: List[int], nums2: List[int]) -> float"
},
{
"docstring": "Step 4 pointer, get rid of a min and... | 2 | stack_v2_sparse_classes_30k_train_004411 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def findMedianSortedArrays(self, nums1: List[int], nums2: List[int]) -> float: Set two pointer, get rid of min of two arrays each step. [1, 2] [_, 2] ^ => ^ [3, 4] [3, 4] ^ ^
- d... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def findMedianSortedArrays(self, nums1: List[int], nums2: List[int]) -> float: Set two pointer, get rid of min of two arrays each step. [1, 2] [_, 2] ^ => ^ [3, 4] [3, 4] ^ ^
- d... | d679a06a72e6dc18aed95c7e79e25de87e9c18c2 | <|skeleton|>
class Solution:
def findMedianSortedArrays(self, nums1: List[int], nums2: List[int]) -> float:
"""Set two pointer, get rid of min of two arrays each step. [1, 2] [_, 2] ^ => ^ [3, 4] [3, 4] ^ ^"""
<|body_0|>
def findMedianSortedArrays_2(self, nums1: List[int], nums2: List[int]) ->... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def findMedianSortedArrays(self, nums1: List[int], nums2: List[int]) -> float:
"""Set two pointer, get rid of min of two arrays each step. [1, 2] [_, 2] ^ => ^ [3, 4] [3, 4] ^ ^"""
step = 0
median_p = (len(nums1) + len(nums2) - 1) / 2
median = []
while True:
... | the_stack_v2_python_sparse | leetcode/4-median-of-two-sorted-arrays.py | ninjaboynaru/my-python-demo | train | 0 | |
55d8d994b233f6c49b75223bd00d1bed58c514cc | [
"context.set_code(grpc.StatusCode.UNIMPLEMENTED)\ncontext.set_details('Method not implemented!')\nraise NotImplementedError('Method not implemented!')",
"context.set_code(grpc.StatusCode.UNIMPLEMENTED)\ncontext.set_details('Method not implemented!')\nraise NotImplementedError('Method not implemented!')"
] | <|body_start_0|>
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
<|end_body_0|>
<|body_start_1|>
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not im... | A set of methods for managing MongoDB Backup resources. | BackupServiceServicer | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BackupServiceServicer:
"""A set of methods for managing MongoDB Backup resources."""
def Get(self, request, context):
"""Returns the specified MongoDB Backup resource. To get the list of available MongoDB Backup resources, make a [List] request."""
<|body_0|>
def List(se... | stack_v2_sparse_classes_36k_train_011333 | 2,939 | permissive | [
{
"docstring": "Returns the specified MongoDB Backup resource. To get the list of available MongoDB Backup resources, make a [List] request.",
"name": "Get",
"signature": "def Get(self, request, context)"
},
{
"docstring": "Retrieves the list of Backup resources available for the specified folde... | 2 | stack_v2_sparse_classes_30k_train_011429 | Implement the Python class `BackupServiceServicer` described below.
Class description:
A set of methods for managing MongoDB Backup resources.
Method signatures and docstrings:
- def Get(self, request, context): Returns the specified MongoDB Backup resource. To get the list of available MongoDB Backup resources, make... | Implement the Python class `BackupServiceServicer` described below.
Class description:
A set of methods for managing MongoDB Backup resources.
Method signatures and docstrings:
- def Get(self, request, context): Returns the specified MongoDB Backup resource. To get the list of available MongoDB Backup resources, make... | 980e2c5d848eadb42799132b35a9f58ab7b27157 | <|skeleton|>
class BackupServiceServicer:
"""A set of methods for managing MongoDB Backup resources."""
def Get(self, request, context):
"""Returns the specified MongoDB Backup resource. To get the list of available MongoDB Backup resources, make a [List] request."""
<|body_0|>
def List(se... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class BackupServiceServicer:
"""A set of methods for managing MongoDB Backup resources."""
def Get(self, request, context):
"""Returns the specified MongoDB Backup resource. To get the list of available MongoDB Backup resources, make a [List] request."""
context.set_code(grpc.StatusCode.UNIMPLE... | the_stack_v2_python_sparse | yandex/cloud/mdb/mongodb/v1/backup_service_pb2_grpc.py | IIKovalenko/python-sdk | train | 1 |
d5fbdd708af2e1f898f7292a6cb75a06613b18d4 | [
"from aha.widget.field import TextField, RichText\nfrom aha.widget.form import Form\nfrom formencode import validators as v\n\nclass AddForm(Form):\n multipart = True\n form_title = u'Add New Folder'\n button_title = u'Add'\n submit = u'Save'\n name = TextField(title=u'ID', args={'size': 40}, validat... | <|body_start_0|>
from aha.widget.field import TextField, RichText
from aha.widget.form import Form
from formencode import validators as v
class AddForm(Form):
multipart = True
form_title = u'Add New Folder'
button_title = u'Add'
submit = u... | The controller for Folder. | FolderController | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FolderController:
"""The controller for Folder."""
def get_form(self, kind, ins=None):
"""A method to return form object based on given kind. kind must be one of 'add' or 'edit'"""
<|body_0|>
def add_new_object(cls, v, ins):
"""A method to obtain new object"""
... | stack_v2_sparse_classes_36k_train_011334 | 2,372 | permissive | [
{
"docstring": "A method to return form object based on given kind. kind must be one of 'add' or 'edit'",
"name": "get_form",
"signature": "def get_form(self, kind, ins=None)"
},
{
"docstring": "A method to obtain new object",
"name": "add_new_object",
"signature": "def add_new_object(cl... | 2 | stack_v2_sparse_classes_30k_train_018422 | Implement the Python class `FolderController` described below.
Class description:
The controller for Folder.
Method signatures and docstrings:
- def get_form(self, kind, ins=None): A method to return form object based on given kind. kind must be one of 'add' or 'edit'
- def add_new_object(cls, v, ins): A method to ob... | Implement the Python class `FolderController` described below.
Class description:
The controller for Folder.
Method signatures and docstrings:
- def get_form(self, kind, ins=None): A method to return form object based on given kind. kind must be one of 'add' or 'edit'
- def add_new_object(cls, v, ins): A method to ob... | e1209f7d44d1c59ff9d373b7d89d414f31a9c28b | <|skeleton|>
class FolderController:
"""The controller for Folder."""
def get_form(self, kind, ins=None):
"""A method to return form object based on given kind. kind must be one of 'add' or 'edit'"""
<|body_0|>
def add_new_object(cls, v, ins):
"""A method to obtain new object"""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class FolderController:
"""The controller for Folder."""
def get_form(self, kind, ins=None):
"""A method to return form object based on given kind. kind must be one of 'add' or 'edit'"""
from aha.widget.field import TextField, RichText
from aha.widget.form import Form
from forme... | the_stack_v2_python_sparse | applications/aha.application.coreblog3/application/controller/folder.py | Letractively/aha-gae | train | 0 |
6028785e6d058ad4e98bef60569ca859e5a93533 | [
"def build_list(node, ln):\n if node is None:\n ln.append(self.nullStr)\n return\n ln.append(str(node.val))\n build_list(node.left, ln)\n build_list(node.right, ln)\nlist_node = []\nbuild_list(root, list_node)\nreturn self.splitter.join(list_node)",
"def createTree(list_node):\n try:\... | <|body_start_0|>
def build_list(node, ln):
if node is None:
ln.append(self.nullStr)
return
ln.append(str(node.val))
build_list(node.left, ln)
build_list(node.right, ln)
list_node = []
build_list(root, list_node)
... | Codec | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
<|body_0|>
def deserialize(self, data):
"""Decodes your encoded data to tree. :type data: str :rtype: TreeNode"""
<|body_1|>
<|end_skeleton|>
<|body_... | stack_v2_sparse_classes_36k_train_011335 | 2,121 | no_license | [
{
"docstring": "Encodes a tree to a single string. :type root: TreeNode :rtype: str",
"name": "serialize",
"signature": "def serialize(self, root)"
},
{
"docstring": "Decodes your encoded data to tree. :type data: str :rtype: TreeNode",
"name": "deserialize",
"signature": "def deserializ... | 2 | null | Implement the Python class `Codec` described below.
Class description:
Implement the Codec class.
Method signatures and docstrings:
- def serialize(self, root): Encodes a tree to a single string. :type root: TreeNode :rtype: str
- def deserialize(self, data): Decodes your encoded data to tree. :type data: str :rtype:... | Implement the Python class `Codec` described below.
Class description:
Implement the Codec class.
Method signatures and docstrings:
- def serialize(self, root): Encodes a tree to a single string. :type root: TreeNode :rtype: str
- def deserialize(self, data): Decodes your encoded data to tree. :type data: str :rtype:... | fe3c39e0c1cb689dad5f5305a890f21a3edb2260 | <|skeleton|>
class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
<|body_0|>
def deserialize(self, data):
"""Decodes your encoded data to tree. :type data: str :rtype: TreeNode"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
def build_list(node, ln):
if node is None:
ln.append(self.nullStr)
return
ln.append(str(node.val))
build_list(node.lef... | the_stack_v2_python_sparse | leetcode/LC297_serialize_deserialize_binarytree.py | gauravtatke/codetinkering | train | 0 | |
f3f1e70b9d4e9388fc3825d698118682e4a64547 | [
"interval = hours * 3600\nkeys = list(sorted(data.keys()))\nts_start = min(keys)\nts_stop = max(keys)\nself._data_dict = {}\nboundaries = []\nbuckets = list(range(ts_start, ts_stop, interval))\nfor pointer in range(1, len(buckets)):\n boundaries.append((buckets[pointer - 1], buckets[pointer]))\nfor boundary in b... | <|body_start_0|>
interval = hours * 3600
keys = list(sorted(data.keys()))
ts_start = min(keys)
ts_stop = max(keys)
self._data_dict = {}
boundaries = []
buckets = list(range(ts_start, ts_stop, interval))
for pointer in range(1, len(buckets)):
bo... | Process data for ingestion. | Data | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Data:
"""Process data for ingestion."""
def __init__(self, data, hours=1, rrd_step=300):
"""Method that instantiates the class. Args: data: Dict of values keyed by timestamp hours: rrd_step: Returns: None"""
<|body_0|>
def load(self, width=5, height=6, training_lookahead... | stack_v2_sparse_classes_36k_train_011336 | 8,937 | no_license | [
{
"docstring": "Method that instantiates the class. Args: data: Dict of values keyed by timestamp hours: rrd_step: Returns: None",
"name": "__init__",
"signature": "def __init__(self, data, hours=1, rrd_step=300)"
},
{
"docstring": "Create histogram of data. Args: vector_length: Lenth of the vec... | 2 | stack_v2_sparse_classes_30k_train_020879 | Implement the Python class `Data` described below.
Class description:
Process data for ingestion.
Method signatures and docstrings:
- def __init__(self, data, hours=1, rrd_step=300): Method that instantiates the class. Args: data: Dict of values keyed by timestamp hours: rrd_step: Returns: None
- def load(self, width... | Implement the Python class `Data` described below.
Class description:
Process data for ingestion.
Method signatures and docstrings:
- def __init__(self, data, hours=1, rrd_step=300): Method that instantiates the class. Args: data: Dict of values keyed by timestamp hours: rrd_step: Returns: None
- def load(self, width... | 36a7996b140cccb9003cba8367364645e2d65d85 | <|skeleton|>
class Data:
"""Process data for ingestion."""
def __init__(self, data, hours=1, rrd_step=300):
"""Method that instantiates the class. Args: data: Dict of values keyed by timestamp hours: rrd_step: Returns: None"""
<|body_0|>
def load(self, width=5, height=6, training_lookahead... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Data:
"""Process data for ingestion."""
def __init__(self, data, hours=1, rrd_step=300):
"""Method that instantiates the class. Args: data: Dict of values keyed by timestamp hours: rrd_step: Returns: None"""
interval = hours * 3600
keys = list(sorted(data.keys()))
ts_start... | the_stack_v2_python_sparse | timeseries/forecast/_archive/forecast-cnn-1543954179.py | palisadoes/AI | train | 1 |
8449739dd0f1e7fe6916a3dcc5d2939d5042bd6d | [
"Animal.__init__(self, habitat)\nMammal.__init__(self, food, fur)\nself.age = age\nself.sex = sex\nprint('---A Human---')",
"print(f'\\nAge: {self.age}')\nprint(f'Sex: {self.sex}')\nprint(f'Habitat: {self.habitat}')\nprint(f'Food: {self.food}')\nprint(f'Fur: {self.fur}\\n')"
] | <|body_start_0|>
Animal.__init__(self, habitat)
Mammal.__init__(self, food, fur)
self.age = age
self.sex = sex
print('---A Human---')
<|end_body_0|>
<|body_start_1|>
print(f'\nAge: {self.age}')
print(f'Sex: {self.sex}')
print(f'Habitat: {self.habitat}')
... | Human class. Args: age (int): Age of the human. sex (str): Sex of the human. | Human | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Human:
"""Human class. Args: age (int): Age of the human. sex (str): Sex of the human."""
def __init__(self, age, sex, habitat, food, fur=False):
"""Constructor of Human class."""
<|body_0|>
def display(self):
"""Display method to print the different characterist... | stack_v2_sparse_classes_36k_train_011337 | 2,135 | no_license | [
{
"docstring": "Constructor of Human class.",
"name": "__init__",
"signature": "def __init__(self, age, sex, habitat, food, fur=False)"
},
{
"docstring": "Display method to print the different characteristics.",
"name": "display",
"signature": "def display(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_017176 | Implement the Python class `Human` described below.
Class description:
Human class. Args: age (int): Age of the human. sex (str): Sex of the human.
Method signatures and docstrings:
- def __init__(self, age, sex, habitat, food, fur=False): Constructor of Human class.
- def display(self): Display method to print the d... | Implement the Python class `Human` described below.
Class description:
Human class. Args: age (int): Age of the human. sex (str): Sex of the human.
Method signatures and docstrings:
- def __init__(self, age, sex, habitat, food, fur=False): Constructor of Human class.
- def display(self): Display method to print the d... | 892d9c25b9712bf3bbfd7f29529eca8b47fb8039 | <|skeleton|>
class Human:
"""Human class. Args: age (int): Age of the human. sex (str): Sex of the human."""
def __init__(self, age, sex, habitat, food, fur=False):
"""Constructor of Human class."""
<|body_0|>
def display(self):
"""Display method to print the different characterist... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Human:
"""Human class. Args: age (int): Age of the human. sex (str): Sex of the human."""
def __init__(self, age, sex, habitat, food, fur=False):
"""Constructor of Human class."""
Animal.__init__(self, habitat)
Mammal.__init__(self, food, fur)
self.age = age
self.s... | the_stack_v2_python_sparse | sem-3/practical_26_Nov.py | B-Tech-AI-Python/Class-assignments | train | 0 |
89d62bb864bebe3b231123be90f670189b4baa12 | [
"m_arit = ArithmeticUtil.arithmetic_mean(dat)\nn = ArithmeticUtil.number_of_elements(dat)\nreturn np.math.fsum((dat - m_arit) ** 2) / (n - 1)",
"m_arit = ArithmeticUtil.arithmetic_mean(dat)\nn = ArithmeticUtil.number_of_elements(dat)\nreturn np.math.sqrt(np.math.fsum((dat - m_arit) ** 2) / (n - 1))",
"m_arit = ... | <|body_start_0|>
m_arit = ArithmeticUtil.arithmetic_mean(dat)
n = ArithmeticUtil.number_of_elements(dat)
return np.math.fsum((dat - m_arit) ** 2) / (n - 1)
<|end_body_0|>
<|body_start_1|>
m_arit = ArithmeticUtil.arithmetic_mean(dat)
n = ArithmeticUtil.number_of_elements(dat)
... | ModuleStatisticsManager | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ModuleStatisticsManager:
def module_variance(self, dat):
"""TEST SMALL ERROR"""
<|body_0|>
def module_standard_deviation(self, dat):
"""TEST SMALL ERROR"""
<|body_1|>
def module_population_variance(self, dat):
"""TEST SMALL ERROR"""
<|bod... | stack_v2_sparse_classes_36k_train_011338 | 1,949 | no_license | [
{
"docstring": "TEST SMALL ERROR",
"name": "module_variance",
"signature": "def module_variance(self, dat)"
},
{
"docstring": "TEST SMALL ERROR",
"name": "module_standard_deviation",
"signature": "def module_standard_deviation(self, dat)"
},
{
"docstring": "TEST SMALL ERROR",
... | 6 | stack_v2_sparse_classes_30k_train_021155 | Implement the Python class `ModuleStatisticsManager` described below.
Class description:
Implement the ModuleStatisticsManager class.
Method signatures and docstrings:
- def module_variance(self, dat): TEST SMALL ERROR
- def module_standard_deviation(self, dat): TEST SMALL ERROR
- def module_population_variance(self,... | Implement the Python class `ModuleStatisticsManager` described below.
Class description:
Implement the ModuleStatisticsManager class.
Method signatures and docstrings:
- def module_variance(self, dat): TEST SMALL ERROR
- def module_standard_deviation(self, dat): TEST SMALL ERROR
- def module_population_variance(self,... | 59c327a0ef80740e1c6967729d9472aac2afd1b5 | <|skeleton|>
class ModuleStatisticsManager:
def module_variance(self, dat):
"""TEST SMALL ERROR"""
<|body_0|>
def module_standard_deviation(self, dat):
"""TEST SMALL ERROR"""
<|body_1|>
def module_population_variance(self, dat):
"""TEST SMALL ERROR"""
<|bod... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ModuleStatisticsManager:
def module_variance(self, dat):
"""TEST SMALL ERROR"""
m_arit = ArithmeticUtil.arithmetic_mean(dat)
n = ArithmeticUtil.number_of_elements(dat)
return np.math.fsum((dat - m_arit) ** 2) / (n - 1)
def module_standard_deviation(self, dat):
"""T... | the_stack_v2_python_sparse | VecStatsGraph/manager/ModuleStatisticsManager.py | IvanDragoJr/VecStatsGraph3d | train | 0 | |
415b2a62f03336d840b79820e5dc8608762fefc1 | [
"super().__init__()\nself.embedder = embedder\nself.output_layer = output_layer\nself.drop = nn.Dropout(dropout)",
"outputs = self.embedder(data)\nif isinstance(outputs, tuple):\n encoding = outputs[0]\nelse:\n encoding = outputs\npred = self.output_layer(self.drop(encoding))\nreturn (pred, target) if targe... | <|body_start_0|>
super().__init__()
self.embedder = embedder
self.output_layer = output_layer
self.drop = nn.Dropout(dropout)
<|end_body_0|>
<|body_start_1|>
outputs = self.embedder(data)
if isinstance(outputs, tuple):
encoding = outputs[0]
else:
... | Implements a standard classifier. The classifier is composed of an encoder module, followed by a fully connected output layer, with a dropout layer in between. Attributes ---------- embedder: Embedder The embedder layer output_layer : Module The output layer, yields a probability distribution over targets drop: nn.Drop... | TextClassifier | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TextClassifier:
"""Implements a standard classifier. The classifier is composed of an encoder module, followed by a fully connected output layer, with a dropout layer in between. Attributes ---------- embedder: Embedder The embedder layer output_layer : Module The output layer, yields a probabili... | stack_v2_sparse_classes_36k_train_011339 | 2,138 | permissive | [
{
"docstring": "Initialize the TextClassifier model. Parameters ---------- embedder: Embedder The embedder layer output_layer : Module The output layer, yields a probability distribution dropout : float, optional Amount of dropout to include between layers (defaults to 0)",
"name": "__init__",
"signatur... | 2 | stack_v2_sparse_classes_30k_val_000586 | Implement the Python class `TextClassifier` described below.
Class description:
Implements a standard classifier. The classifier is composed of an encoder module, followed by a fully connected output layer, with a dropout layer in between. Attributes ---------- embedder: Embedder The embedder layer output_layer : Modu... | Implement the Python class `TextClassifier` described below.
Class description:
Implements a standard classifier. The classifier is composed of an encoder module, followed by a fully connected output layer, with a dropout layer in between. Attributes ---------- embedder: Embedder The embedder layer output_layer : Modu... | 0dc2f5b2b286694defe8abf450fe5be9ae12c097 | <|skeleton|>
class TextClassifier:
"""Implements a standard classifier. The classifier is composed of an encoder module, followed by a fully connected output layer, with a dropout layer in between. Attributes ---------- embedder: Embedder The embedder layer output_layer : Module The output layer, yields a probabili... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TextClassifier:
"""Implements a standard classifier. The classifier is composed of an encoder module, followed by a fully connected output layer, with a dropout layer in between. Attributes ---------- embedder: Embedder The embedder layer output_layer : Module The output layer, yields a probability distributi... | the_stack_v2_python_sparse | flambe/nlp/classification/model.py | cle-ros/flambe | train | 1 |
b81e79a69d51c92a95221f4c12bc04e93dfa55b8 | [
"if not root:\n return None\nres = []\nl = [root, '*']\nwhile l:\n ll = []\n for n in l:\n if n == '*':\n res.append('*')\n else:\n res.append(str(n.val))\n for nn in n.children:\n ll.append(nn)\n ll.append('*')\n l = ll\nreturn ',... | <|body_start_0|>
if not root:
return None
res = []
l = [root, '*']
while l:
ll = []
for n in l:
if n == '*':
res.append('*')
else:
res.append(str(n.val))
for nn... | Codec | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: Node :rtype: str"""
<|body_0|>
def deserialize(self, data):
"""Decodes your encoded data to tree. :type data: str :rtype: Node"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|... | stack_v2_sparse_classes_36k_train_011340 | 1,548 | no_license | [
{
"docstring": "Encodes a tree to a single string. :type root: Node :rtype: str",
"name": "serialize",
"signature": "def serialize(self, root)"
},
{
"docstring": "Decodes your encoded data to tree. :type data: str :rtype: Node",
"name": "deserialize",
"signature": "def deserialize(self, ... | 2 | null | Implement the Python class `Codec` described below.
Class description:
Implement the Codec class.
Method signatures and docstrings:
- def serialize(self, root): Encodes a tree to a single string. :type root: Node :rtype: str
- def deserialize(self, data): Decodes your encoded data to tree. :type data: str :rtype: Nod... | 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: Node :rtype: str
- def deserialize(self, data): Decodes your encoded data to tree. :type data: str :rtype: Nod... | 36d7f9e967a62db77622e0888f61999d7f37579a | <|skeleton|>
class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: Node :rtype: str"""
<|body_0|>
def deserialize(self, data):
"""Decodes your encoded data to tree. :type data: str :rtype: Node"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: Node :rtype: str"""
if not root:
return None
res = []
l = [root, '*']
while l:
ll = []
for n in l:
if n == '*':
re... | the_stack_v2_python_sparse | apple/P0428_apple.py | westgate458/LeetCode | train | 0 | |
fdfd7dc52761826c35d4c8da97ca1ea76aed43aa | [
"query = self.table.query(g.db_session)\nadditional_modifiers = None\nif id is not None:\n additional_modifiers = add_filter(additional_modifiers, RPMComment.id == id)\nif username is not None:\n additional_modifiers = add_filter(additional_modifiers, User.name == username)\nif id_comp is not None:\n addit... | <|body_start_0|>
query = self.table.query(g.db_session)
additional_modifiers = None
if id is not None:
additional_modifiers = add_filter(additional_modifiers, RPMComment.id == id)
if username is not None:
additional_modifiers = add_filter(additional_modifiers, Use... | List of rpm comments. | RPMCommentsList | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RPMCommentsList:
"""List of rpm comments."""
def get(self, id=None, username=None, id_comp=None, id_diff=None):
"""Get list. :param int id: id to optionally filter by :return list: list of the resulting query"""
<|body_0|>
def post(self):
"""Add new comment."""
... | stack_v2_sparse_classes_36k_train_011341 | 22,524 | permissive | [
{
"docstring": "Get list. :param int id: id to optionally filter by :return list: list of the resulting query",
"name": "get",
"signature": "def get(self, id=None, username=None, id_comp=None, id_diff=None)"
},
{
"docstring": "Add new comment.",
"name": "post",
"signature": "def post(sel... | 2 | stack_v2_sparse_classes_30k_train_018312 | Implement the Python class `RPMCommentsList` described below.
Class description:
List of rpm comments.
Method signatures and docstrings:
- def get(self, id=None, username=None, id_comp=None, id_diff=None): Get list. :param int id: id to optionally filter by :return list: list of the resulting query
- def post(self): ... | Implement the Python class `RPMCommentsList` described below.
Class description:
List of rpm comments.
Method signatures and docstrings:
- def get(self, id=None, username=None, id_comp=None, id_diff=None): Get list. :param int id: id to optionally filter by :return list: list of the resulting query
- def post(self): ... | 06f2ef0bb232b1ffe46e9d50575c4b79b1cff191 | <|skeleton|>
class RPMCommentsList:
"""List of rpm comments."""
def get(self, id=None, username=None, id_comp=None, id_diff=None):
"""Get list. :param int id: id to optionally filter by :return list: list of the resulting query"""
<|body_0|>
def post(self):
"""Add new comment."""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RPMCommentsList:
"""List of rpm comments."""
def get(self, id=None, username=None, id_comp=None, id_diff=None):
"""Get list. :param int id: id to optionally filter by :return list: list of the resulting query"""
query = self.table.query(g.db_session)
additional_modifiers = None
... | the_stack_v2_python_sparse | archdiffer/plugins/rpmdiff/flask_frontend/bp.py | pkratoch/archdiffer | train | 0 |
36a0f05f217afe060d51c293981652bf44e86ff1 | [
"os.umask(0)\nif config is None:\n config = {}\nself.config: Dict[str, str] = config\nself.sessions: Dict[int, Session] = {}\nself.service_errors: List[str] = []\nself.load_services()\nself.service_manager: ConfigServiceManager = ConfigServiceManager()\nconfig_services_path = os.path.abspath(os.path.dirname(conf... | <|body_start_0|>
os.umask(0)
if config is None:
config = {}
self.config: Dict[str, str] = config
self.sessions: Dict[int, Session] = {}
self.service_errors: List[str] = []
self.load_services()
self.service_manager: ConfigServiceManager = ConfigServiceM... | Provides logic for creating and configuring CORE sessions and the nodes within them. | CoreEmu | [
"BSD-2-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CoreEmu:
"""Provides logic for creating and configuring CORE sessions and the nodes within them."""
def __init__(self, config: Dict[str, str]=None) -> None:
"""Create a CoreEmu object. :param config: configuration options"""
<|body_0|>
def _validate_env(self) -> None:
... | stack_v2_sparse_classes_36k_train_011342 | 4,589 | permissive | [
{
"docstring": "Create a CoreEmu object. :param config: configuration options",
"name": "__init__",
"signature": "def __init__(self, config: Dict[str, str]=None) -> None"
},
{
"docstring": "Validates executables CORE depends on exist on path. :return: nothing :raises core.errors.CoreError: when ... | 6 | stack_v2_sparse_classes_30k_train_010432 | Implement the Python class `CoreEmu` described below.
Class description:
Provides logic for creating and configuring CORE sessions and the nodes within them.
Method signatures and docstrings:
- def __init__(self, config: Dict[str, str]=None) -> None: Create a CoreEmu object. :param config: configuration options
- def... | Implement the Python class `CoreEmu` described below.
Class description:
Provides logic for creating and configuring CORE sessions and the nodes within them.
Method signatures and docstrings:
- def __init__(self, config: Dict[str, str]=None) -> None: Create a CoreEmu object. :param config: configuration options
- def... | 87ca431e73fec22faeaebd6b25fc76e0b165c639 | <|skeleton|>
class CoreEmu:
"""Provides logic for creating and configuring CORE sessions and the nodes within them."""
def __init__(self, config: Dict[str, str]=None) -> None:
"""Create a CoreEmu object. :param config: configuration options"""
<|body_0|>
def _validate_env(self) -> None:
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CoreEmu:
"""Provides logic for creating and configuring CORE sessions and the nodes within them."""
def __init__(self, config: Dict[str, str]=None) -> None:
"""Create a CoreEmu object. :param config: configuration options"""
os.umask(0)
if config is None:
config = {}
... | the_stack_v2_python_sparse | daemon/core/emulator/coreemu.py | kleberango/core | train | 0 |
e0fe56975cc73becdc5b8364b51699dd7c60ce9c | [
"if self.request.user.is_superuser:\n return models.Workflow.objects.all()\nreturn models.Workflow.objects.filter(Q(user=self.request.user) | Q(shared=self.request.user)).distinct()",
"if self.request.user.is_superuser:\n serializer.save()\nelse:\n serializer.save(user=self.request.user)"
] | <|body_start_0|>
if self.request.user.is_superuser:
return models.Workflow.objects.all()
return models.Workflow.objects.filter(Q(user=self.request.user) | Q(shared=self.request.user)).distinct()
<|end_body_0|>
<|body_start_1|>
if self.request.user.is_superuser:
serialize... | Access the workflow. get: Return a list of available workflows post: Create a new workflow given name, description and attributes | WorkflowAPIListCreate | [
"LGPL-2.0-or-later",
"BSD-3-Clause",
"MIT",
"Apache-2.0",
"LGPL-2.1-only",
"Python-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class WorkflowAPIListCreate:
"""Access the workflow. get: Return a list of available workflows post: Create a new workflow given name, description and attributes"""
def get_queryset(self):
"""Access the required workflow."""
<|body_0|>
def perform_create(self, serializer):
... | stack_v2_sparse_classes_36k_train_011343 | 4,435 | permissive | [
{
"docstring": "Access the required workflow.",
"name": "get_queryset",
"signature": "def get_queryset(self)"
},
{
"docstring": "Create the new workflow.",
"name": "perform_create",
"signature": "def perform_create(self, serializer)"
}
] | 2 | stack_v2_sparse_classes_30k_val_000031 | Implement the Python class `WorkflowAPIListCreate` described below.
Class description:
Access the workflow. get: Return a list of available workflows post: Create a new workflow given name, description and attributes
Method signatures and docstrings:
- def get_queryset(self): Access the required workflow.
- def perfo... | Implement the Python class `WorkflowAPIListCreate` described below.
Class description:
Access the workflow. get: Return a list of available workflows post: Create a new workflow given name, description and attributes
Method signatures and docstrings:
- def get_queryset(self): Access the required workflow.
- def perfo... | c432745dfff932cbe7397100422d49df78f0a882 | <|skeleton|>
class WorkflowAPIListCreate:
"""Access the workflow. get: Return a list of available workflows post: Create a new workflow given name, description and attributes"""
def get_queryset(self):
"""Access the required workflow."""
<|body_0|>
def perform_create(self, serializer):
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class WorkflowAPIListCreate:
"""Access the workflow. get: Return a list of available workflows post: Create a new workflow given name, description and attributes"""
def get_queryset(self):
"""Access the required workflow."""
if self.request.user.is_superuser:
return models.Workflow.... | the_stack_v2_python_sparse | ontask/workflow/api.py | abelardopardo/ontask_b | train | 43 |
7f2ea98b2ea076c5bb3095e170b2be7a3a708d90 | [
"self.N_PIX = N_PIX\nself.create_zernike_matrix(N_levels=N_levels)\nself.create_least_squares_matrix()\nself.aberrations = ['Obliq. Astig.', 'Defocus', 'Horiz. Astig.', 'Obliq. Trefoil', 'Horiz. Coma', 'Vert. Coma', 'Horiz. Trefoil', 'Obliq. Quadref.', '2nd Obliq. Coma', 'Spherical', '2nd Horiz. Coma', 'Horiz. Quad... | <|body_start_0|>
self.N_PIX = N_PIX
self.create_zernike_matrix(N_levels=N_levels)
self.create_least_squares_matrix()
self.aberrations = ['Obliq. Astig.', 'Defocus', 'Horiz. Astig.', 'Obliq. Trefoil', 'Horiz. Coma', 'Vert. Coma', 'Horiz. Trefoil', 'Obliq. Quadref.', '2nd Obliq. Coma', 'Sp... | Object in charge of receiving some wavefront maps and fitting them to Zernike polynomials (1 - 3) Piston, Tilts (4) Oblique Astigmatism (5) Defocus (6) Horizontal Astigmatism (7) Oblique Trefoil (8) Horizontal Coma (9) Vertical Coma (10) Horizontal Trefoil (11) Oblique Quadrefoil | ZernikeFit | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ZernikeFit:
"""Object in charge of receiving some wavefront maps and fitting them to Zernike polynomials (1 - 3) Piston, Tilts (4) Oblique Astigmatism (5) Defocus (6) Horizontal Astigmatism (7) Oblique Trefoil (8) Horizontal Coma (9) Vertical Coma (10) Horizontal Trefoil (11) Oblique Quadrefoil""... | stack_v2_sparse_classes_36k_train_011344 | 17,758 | no_license | [
{
"docstring": ":param N_PIX: Number of pixels in the wavefront maps :param N_levels: How many Zernike radial levels to consider",
"name": "__init__",
"signature": "def __init__(self, N_PIX, N_levels)"
},
{
"docstring": "Initialize a Zernike matrix containing the Zernike polynomials up to radial... | 4 | stack_v2_sparse_classes_30k_train_000855 | Implement the Python class `ZernikeFit` described below.
Class description:
Object in charge of receiving some wavefront maps and fitting them to Zernike polynomials (1 - 3) Piston, Tilts (4) Oblique Astigmatism (5) Defocus (6) Horizontal Astigmatism (7) Oblique Trefoil (8) Horizontal Coma (9) Vertical Coma (10) Horiz... | Implement the Python class `ZernikeFit` described below.
Class description:
Object in charge of receiving some wavefront maps and fitting them to Zernike polynomials (1 - 3) Piston, Tilts (4) Oblique Astigmatism (5) Defocus (6) Horizontal Astigmatism (7) Oblique Trefoil (8) Horizontal Coma (9) Vertical Coma (10) Horiz... | 7eb3fdbf2e648f215ebb9054014602424131f295 | <|skeleton|>
class ZernikeFit:
"""Object in charge of receiving some wavefront maps and fitting them to Zernike polynomials (1 - 3) Piston, Tilts (4) Oblique Astigmatism (5) Defocus (6) Horizontal Astigmatism (7) Oblique Trefoil (8) Horizontal Coma (9) Vertical Coma (10) Horizontal Trefoil (11) Oblique Quadrefoil""... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ZernikeFit:
"""Object in charge of receiving some wavefront maps and fitting them to Zernike polynomials (1 - 3) Piston, Tilts (4) Oblique Astigmatism (5) Defocus (6) Horizontal Astigmatism (7) Oblique Trefoil (8) Horizontal Coma (9) Vertical Coma (10) Horizontal Trefoil (11) Oblique Quadrefoil"""
def __... | the_stack_v2_python_sparse | old bits/e2e_wavefronts.py | AlvaroMenduina/E2E | train | 0 |
1e611737a52ac21b9885d3d4a26c8c3de1a43cd7 | [
"global _SESSIONS\nif not _SESSIONS:\n from evennia.server.sessionhandler import SESSIONS as _SESSIONS\nif irc_botname:\n self.db.irc_botname = irc_botname\nelif not self.db.irc_botname:\n self.db.irc_botname = self.key\nif ev_channel:\n channel = search.channel_search(ev_channel)\n if not channel:\n... | <|body_start_0|>
global _SESSIONS
if not _SESSIONS:
from evennia.server.sessionhandler import SESSIONS as _SESSIONS
if irc_botname:
self.db.irc_botname = irc_botname
elif not self.db.irc_botname:
self.db.irc_botname = self.key
if ev_channel:
... | Bot for handling IRC connections. | IRCBot | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class IRCBot:
"""Bot for handling IRC connections."""
def start(self, ev_channel=None, irc_botname=None, irc_channel=None, irc_network=None, irc_port=None, irc_ssl=None):
"""Start by telling the portal to start a new session. Args: ev_channel (str): Key of the Evennia channel to connect to... | stack_v2_sparse_classes_36k_train_011345 | 13,744 | permissive | [
{
"docstring": "Start by telling the portal to start a new session. Args: ev_channel (str): Key of the Evennia channel to connect to. irc_botname (str): Name of bot to connect to irc channel. If not set, use `self.key`. irc_channel (str): Name of channel on the form `#channelname`. irc_network (str): URL of the... | 3 | stack_v2_sparse_classes_30k_train_005504 | Implement the Python class `IRCBot` described below.
Class description:
Bot for handling IRC connections.
Method signatures and docstrings:
- def start(self, ev_channel=None, irc_botname=None, irc_channel=None, irc_network=None, irc_port=None, irc_ssl=None): Start by telling the portal to start a new session. Args: e... | Implement the Python class `IRCBot` described below.
Class description:
Bot for handling IRC connections.
Method signatures and docstrings:
- def start(self, ev_channel=None, irc_botname=None, irc_channel=None, irc_network=None, irc_port=None, irc_ssl=None): Start by telling the portal to start a new session. Args: e... | 384d08f9d877c7ad758292822e6f04292fdad047 | <|skeleton|>
class IRCBot:
"""Bot for handling IRC connections."""
def start(self, ev_channel=None, irc_botname=None, irc_channel=None, irc_network=None, irc_port=None, irc_ssl=None):
"""Start by telling the portal to start a new session. Args: ev_channel (str): Key of the Evennia channel to connect to... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class IRCBot:
"""Bot for handling IRC connections."""
def start(self, ev_channel=None, irc_botname=None, irc_channel=None, irc_network=None, irc_port=None, irc_ssl=None):
"""Start by telling the portal to start a new session. Args: ev_channel (str): Key of the Evennia channel to connect to. irc_botname... | the_stack_v2_python_sparse | evennia/players/bots.py | robbintt/evennia | train | 1 |
dd9a7deeea03078ef9e1cc69ab4574de8bedf06d | [
"ctx.save_for_backward(x, w, b)\ny = x.mm(w) + b.T\nreturn y",
"x, w, b = ctx.saved_tensors\ndzdx = dz_dy.mm(w.T)\ndzdw = x.T.mm(dz_dy)\nsize_m = dz_dy.size(0)\ndydb = torch.ones((1, size_m))\ndzdb = dydb.mm(dz_dy.float())\nreturn (dzdx, dzdw, dzdb)"
] | <|body_start_0|>
ctx.save_for_backward(x, w, b)
y = x.mm(w) + b.T
return y
<|end_body_0|>
<|body_start_1|>
x, w, b = ctx.saved_tensors
dzdx = dz_dy.mm(w.T)
dzdw = x.T.mm(dz_dy)
size_m = dz_dy.size(0)
dydb = torch.ones((1, size_m))
dzdb = dydb.mm(d... | FullyConnected | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FullyConnected:
def forward(ctx, x, w, b):
"""Computes the output of the fully_connected function given in the assignment Arguments --------- ctx: a PyTorch context object x (Tensor): of size (T x n), the input features w (Tensor): of size (n x m), the weights b (Tensor): of size (m), th... | stack_v2_sparse_classes_36k_train_011346 | 1,416 | no_license | [
{
"docstring": "Computes the output of the fully_connected function given in the assignment Arguments --------- ctx: a PyTorch context object x (Tensor): of size (T x n), the input features w (Tensor): of size (n x m), the weights b (Tensor): of size (m), the biases Returns ----- y (Tensor): of size (T x m), th... | 2 | stack_v2_sparse_classes_30k_val_000920 | Implement the Python class `FullyConnected` described below.
Class description:
Implement the FullyConnected class.
Method signatures and docstrings:
- def forward(ctx, x, w, b): Computes the output of the fully_connected function given in the assignment Arguments --------- ctx: a PyTorch context object x (Tensor): o... | Implement the Python class `FullyConnected` described below.
Class description:
Implement the FullyConnected class.
Method signatures and docstrings:
- def forward(ctx, x, w, b): Computes the output of the fully_connected function given in the assignment Arguments --------- ctx: a PyTorch context object x (Tensor): o... | 07703e76f49d9be86b39d7c2af1ee1e0efe07031 | <|skeleton|>
class FullyConnected:
def forward(ctx, x, w, b):
"""Computes the output of the fully_connected function given in the assignment Arguments --------- ctx: a PyTorch context object x (Tensor): of size (T x n), the input features w (Tensor): of size (n x m), the weights b (Tensor): of size (m), th... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class FullyConnected:
def forward(ctx, x, w, b):
"""Computes the output of the fully_connected function given in the assignment Arguments --------- ctx: a PyTorch context object x (Tensor): of size (T x n), the input features w (Tensor): of size (n x m), the weights b (Tensor): of size (m), the biases Retur... | the_stack_v2_python_sparse | DL_HW/Jialing_Wu_HW1/fully_connected.py | muzhiBryn/ML_DL_at-Dartmouth | train | 0 | |
48fc8d2094aca0fb68753a3f46488d3f72a486d2 | [
"res = []\nsetNums = set(nums)\nfor i in range(1, len(nums) + 1):\n if i not in setNums:\n res.append(i)\nreturn res",
"for i in xrange(len(nums)):\n index = abs(nums[i]) - 1\n nums[index] = -abs(nums[index])\nres = [i + 1 for i, x in enumerate(nums) if x > 0]\nreturn res",
"ret = []\nnums = [0]... | <|body_start_0|>
res = []
setNums = set(nums)
for i in range(1, len(nums) + 1):
if i not in setNums:
res.append(i)
return res
<|end_body_0|>
<|body_start_1|>
for i in xrange(len(nums)):
index = abs(nums[i]) - 1
nums[index] = -a... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def findDisappearedNumbers(self, nums):
""":type nums: List[int] :rtype: List[int]"""
<|body_0|>
def findDisappearedNumbers2(self, nums):
""":type nums: List[int] :rtype: List[int]"""
<|body_1|>
def findDisappearedNumbers3(self, nums):
... | stack_v2_sparse_classes_36k_train_011347 | 1,369 | no_license | [
{
"docstring": ":type nums: List[int] :rtype: List[int]",
"name": "findDisappearedNumbers",
"signature": "def findDisappearedNumbers(self, nums)"
},
{
"docstring": ":type nums: List[int] :rtype: List[int]",
"name": "findDisappearedNumbers2",
"signature": "def findDisappearedNumbers2(self... | 3 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def findDisappearedNumbers(self, nums): :type nums: List[int] :rtype: List[int]
- def findDisappearedNumbers2(self, nums): :type nums: List[int] :rtype: List[int]
- def findDisap... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def findDisappearedNumbers(self, nums): :type nums: List[int] :rtype: List[int]
- def findDisappearedNumbers2(self, nums): :type nums: List[int] :rtype: List[int]
- def findDisap... | b925bb22d1daa4a56c5a238a5758a926905559b4 | <|skeleton|>
class Solution:
def findDisappearedNumbers(self, nums):
""":type nums: List[int] :rtype: List[int]"""
<|body_0|>
def findDisappearedNumbers2(self, nums):
""":type nums: List[int] :rtype: List[int]"""
<|body_1|>
def findDisappearedNumbers3(self, nums):
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def findDisappearedNumbers(self, nums):
""":type nums: List[int] :rtype: List[int]"""
res = []
setNums = set(nums)
for i in range(1, len(nums) + 1):
if i not in setNums:
res.append(i)
return res
def findDisappearedNumbers2(self... | the_stack_v2_python_sparse | 448. Find All Numbers Disappeared in an Array.py | beninghton/notGivenUpToG | train | 0 | |
ed009c01180f071657fb1680dff2aaf765af10d5 | [
"super(MultiHeadedAttention, self).__init__()\nassert d_model % h == 0\nself.d_k = d_model // h\nself.h = h\nself.linears = clones(nn.Linear(d_model, d_model), 4)\nself.attn = None\nself.dropout = nn.Dropout(p=dropout)",
"d_k = query.size(-1)\nscores = torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(d_k)\n... | <|body_start_0|>
super(MultiHeadedAttention, self).__init__()
assert d_model % h == 0
self.d_k = d_model // h
self.h = h
self.linears = clones(nn.Linear(d_model, d_model), 4)
self.attn = None
self.dropout = nn.Dropout(p=dropout)
<|end_body_0|>
<|body_start_1|>
... | MultiHeadedAttention | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MultiHeadedAttention:
def __init__(self, h, d_model, dropout=0.1):
"""Take in model size and number of heads."""
<|body_0|>
def attention(self, query, key, value, mask=None, dropout=None):
"""Compute 'Scaled Dot Product Attention'"""
<|body_1|>
def forwa... | stack_v2_sparse_classes_36k_train_011348 | 14,213 | no_license | [
{
"docstring": "Take in model size and number of heads.",
"name": "__init__",
"signature": "def __init__(self, h, d_model, dropout=0.1)"
},
{
"docstring": "Compute 'Scaled Dot Product Attention'",
"name": "attention",
"signature": "def attention(self, query, key, value, mask=None, dropou... | 3 | stack_v2_sparse_classes_30k_train_017730 | Implement the Python class `MultiHeadedAttention` described below.
Class description:
Implement the MultiHeadedAttention class.
Method signatures and docstrings:
- def __init__(self, h, d_model, dropout=0.1): Take in model size and number of heads.
- def attention(self, query, key, value, mask=None, dropout=None): Co... | Implement the Python class `MultiHeadedAttention` described below.
Class description:
Implement the MultiHeadedAttention class.
Method signatures and docstrings:
- def __init__(self, h, d_model, dropout=0.1): Take in model size and number of heads.
- def attention(self, query, key, value, mask=None, dropout=None): Co... | 7c389dd416c67f382c9a5d3e1661a5bd89aecc47 | <|skeleton|>
class MultiHeadedAttention:
def __init__(self, h, d_model, dropout=0.1):
"""Take in model size and number of heads."""
<|body_0|>
def attention(self, query, key, value, mask=None, dropout=None):
"""Compute 'Scaled Dot Product Attention'"""
<|body_1|>
def forwa... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MultiHeadedAttention:
def __init__(self, h, d_model, dropout=0.1):
"""Take in model size and number of heads."""
super(MultiHeadedAttention, self).__init__()
assert d_model % h == 0
self.d_k = d_model // h
self.h = h
self.linears = clones(nn.Linear(d_model, d_mo... | the_stack_v2_python_sparse | models/AttModel.py | Shiyang-Yan/ParaCNN | train | 2 | |
f92f8b236bcd75998b1dae4c067488c894c72664 | [
"def climb(n: int):\n if n <= 3:\n return n\n else:\n return climb(n - 1) + climb(n - 2)\nreturn climb(n)",
"if len(self.steps) < n:\n for i in range(n):\n if i <= 2:\n if len(self.steps) <= i:\n self.steps.append(i + 1)\n elif len(self.steps) <= i:\n... | <|body_start_0|>
def climb(n: int):
if n <= 3:
return n
else:
return climb(n - 1) + climb(n - 2)
return climb(n)
<|end_body_0|>
<|body_start_1|>
if len(self.steps) < n:
for i in range(n):
if i <= 2:
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def climbStairs(self, n: int) -> int:
"""n = 38 超时"""
<|body_0|>
def climbStairs1(self, n: int) -> int:
"""执行用时 : 76 ms, 在Climbing Stairs的Python3提交中击败了17.58% 的用户 内存消耗 : 13 MB, 在Climbing Stairs的Python3提交中击败了97.24% 的用户"""
<|body_1|>
def climbStai... | stack_v2_sparse_classes_36k_train_011349 | 2,752 | no_license | [
{
"docstring": "n = 38 超时",
"name": "climbStairs",
"signature": "def climbStairs(self, n: int) -> int"
},
{
"docstring": "执行用时 : 76 ms, 在Climbing Stairs的Python3提交中击败了17.58% 的用户 内存消耗 : 13 MB, 在Climbing Stairs的Python3提交中击败了97.24% 的用户",
"name": "climbStairs1",
"signature": "def climbStairs1... | 3 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def climbStairs(self, n: int) -> int: n = 38 超时
- def climbStairs1(self, n: int) -> int: 执行用时 : 76 ms, 在Climbing Stairs的Python3提交中击败了17.58% 的用户 内存消耗 : 13 MB, 在Climbing Stairs的Pyt... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def climbStairs(self, n: int) -> int: n = 38 超时
- def climbStairs1(self, n: int) -> int: 执行用时 : 76 ms, 在Climbing Stairs的Python3提交中击败了17.58% 的用户 内存消耗 : 13 MB, 在Climbing Stairs的Pyt... | 7bca9dc8ec211be15c12f89bffbb680d639f87bf | <|skeleton|>
class Solution:
def climbStairs(self, n: int) -> int:
"""n = 38 超时"""
<|body_0|>
def climbStairs1(self, n: int) -> int:
"""执行用时 : 76 ms, 在Climbing Stairs的Python3提交中击败了17.58% 的用户 内存消耗 : 13 MB, 在Climbing Stairs的Python3提交中击败了97.24% 的用户"""
<|body_1|>
def climbStai... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def climbStairs(self, n: int) -> int:
"""n = 38 超时"""
def climb(n: int):
if n <= 3:
return n
else:
return climb(n - 1) + climb(n - 2)
return climb(n)
def climbStairs1(self, n: int) -> int:
"""执行用时 : 76 ms, 在... | the_stack_v2_python_sparse | python/leetcode/70-climbing-stairs.py | wxnacy/study | train | 18 | |
0c0f4716c28d4b5cd1c39c7e0b96df0a9a0bef43 | [
"strings = tf.string_split([string_line], delimiter=' ').values\nimage_path = tf.string_join([root_path, strings[0]], separator=os.sep)\nlabels = tf.string_to_number(strings[1:], out_type=tf.float32)\nreturn (image_path, labels)",
"image = tf.read_file(image_path)\nimage = tf.image.decode_jpeg(image)\nsrc_size_hw... | <|body_start_0|>
strings = tf.string_split([string_line], delimiter=' ').values
image_path = tf.string_join([root_path, strings[0]], separator=os.sep)
labels = tf.string_to_number(strings[1:], out_type=tf.float32)
return (image_path, labels)
<|end_body_0|>
<|body_start_1|>
image... | 从标签文件中，构造返回(image, label)的tf.data.Dataset数据集 标签文件内容如下： image_name label0,label1,label2,... | FileUtil | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FileUtil:
"""从标签文件中，构造返回(image, label)的tf.data.Dataset数据集 标签文件内容如下： image_name label0,label1,label2,..."""
def _parse_string_line(string_line, root_path):
"""解析文本中的一行字符串行，得到图片路径（拼接图片根目录）和标签 :param string_line: 文本中的一行字符串，image_name label0 label1 label2 label3 ... :param root_path: 图片根... | stack_v2_sparse_classes_36k_train_011350 | 7,637 | permissive | [
{
"docstring": "解析文本中的一行字符串行，得到图片路径（拼接图片根目录）和标签 :param string_line: 文本中的一行字符串，image_name label0 label1 label2 label3 ... :param root_path: 图片根目录 :return: DatasetV1Adapter<(图片路径Tensor(shape=(), dtype=string)，标签Tensor(shape=(?,), dtype=float32))>",
"name": "_parse_string_line",
"signature": "def _parse_st... | 3 | stack_v2_sparse_classes_30k_train_010075 | Implement the Python class `FileUtil` described below.
Class description:
从标签文件中，构造返回(image, label)的tf.data.Dataset数据集 标签文件内容如下： image_name label0,label1,label2,...
Method signatures and docstrings:
- def _parse_string_line(string_line, root_path): 解析文本中的一行字符串行，得到图片路径（拼接图片根目录）和标签 :param string_line: 文本中的一行字符串，image_n... | Implement the Python class `FileUtil` described below.
Class description:
从标签文件中，构造返回(image, label)的tf.data.Dataset数据集 标签文件内容如下： image_name label0,label1,label2,...
Method signatures and docstrings:
- def _parse_string_line(string_line, root_path): 解析文本中的一行字符串行，得到图片路径（拼接图片根目录）和标签 :param string_line: 文本中的一行字符串，image_n... | 8f2d722e4067aef0c8a9cc29f76d958c0f97fb9f | <|skeleton|>
class FileUtil:
"""从标签文件中，构造返回(image, label)的tf.data.Dataset数据集 标签文件内容如下： image_name label0,label1,label2,..."""
def _parse_string_line(string_line, root_path):
"""解析文本中的一行字符串行，得到图片路径（拼接图片根目录）和标签 :param string_line: 文本中的一行字符串，image_name label0 label1 label2 label3 ... :param root_path: 图片根... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class FileUtil:
"""从标签文件中，构造返回(image, label)的tf.data.Dataset数据集 标签文件内容如下： image_name label0,label1,label2,..."""
def _parse_string_line(string_line, root_path):
"""解析文本中的一行字符串行，得到图片路径（拼接图片根目录）和标签 :param string_line: 文本中的一行字符串，image_name label0 label1 label2 label3 ... :param root_path: 图片根目录 :return: D... | the_stack_v2_python_sparse | dataset/file_util.py | zheng-yuwei/YOLOv3-tensorflow | train | 5 |
700b9dfcf44c2bf2aa43cdad2a3fb54f4b8c4bff | [
"super().__init__(self.PROBLEM_NAME)\nself.input_list = input_list\nself.min_value = min_value\nself.max_value = max_value",
"print('Solving {} problem ...'.format(self.PROBLEM_NAME))\nif len(self.input_list) == 0:\n return range(0, self.max_value + 1)\nrange_list = []\nfor i in range(len(self.input_list)):\n ... | <|body_start_0|>
super().__init__(self.PROBLEM_NAME)
self.input_list = input_list
self.min_value = min_value
self.max_value = max_value
<|end_body_0|>
<|body_start_1|>
print('Solving {} problem ...'.format(self.PROBLEM_NAME))
if len(self.input_list) == 0:
ret... | MissingRanges | MissingRanges | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MissingRanges:
"""MissingRanges"""
def __init__(self, input_list, min_value, max_value):
"""Missing Ranges Args: input_list: Contains a list of integers Returns: None Raises: None"""
<|body_0|>
def solve(self):
"""Solve the problem Note: O(n) (runtime) and O(1) (... | stack_v2_sparse_classes_36k_train_011351 | 3,054 | no_license | [
{
"docstring": "Missing Ranges Args: input_list: Contains a list of integers Returns: None Raises: None",
"name": "__init__",
"signature": "def __init__(self, input_list, min_value, max_value)"
},
{
"docstring": "Solve the problem Note: O(n) (runtime) and O(1) (space) works by iterating the inpu... | 2 | stack_v2_sparse_classes_30k_train_020996 | Implement the Python class `MissingRanges` described below.
Class description:
MissingRanges
Method signatures and docstrings:
- def __init__(self, input_list, min_value, max_value): Missing Ranges Args: input_list: Contains a list of integers Returns: None Raises: None
- def solve(self): Solve the problem Note: O(n)... | Implement the Python class `MissingRanges` described below.
Class description:
MissingRanges
Method signatures and docstrings:
- def __init__(self, input_list, min_value, max_value): Missing Ranges Args: input_list: Contains a list of integers Returns: None Raises: None
- def solve(self): Solve the problem Note: O(n)... | 11f4d25cb211740514c119a60962d075a0817abd | <|skeleton|>
class MissingRanges:
"""MissingRanges"""
def __init__(self, input_list, min_value, max_value):
"""Missing Ranges Args: input_list: Contains a list of integers Returns: None Raises: None"""
<|body_0|>
def solve(self):
"""Solve the problem Note: O(n) (runtime) and O(1) (... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MissingRanges:
"""MissingRanges"""
def __init__(self, input_list, min_value, max_value):
"""Missing Ranges Args: input_list: Contains a list of integers Returns: None Raises: None"""
super().__init__(self.PROBLEM_NAME)
self.input_list = input_list
self.min_value = min_valu... | the_stack_v2_python_sparse | python/problems/array/missing_ranges.py | santhosh-kumar/AlgorithmsAndDataStructures | train | 2 |
4013a6981598931ed2e189781fd80d2e6351ce9c | [
"agg = {'sum': 0, 'avg': 1, 'max': 2}\nB, N, M, out_dim = point_features.size()\n_, npoint, K, _ = scores.size()\noutput = point_features.new_zeros((B, out_dim, npoint, K))\next_module.assign_score_withk_forward(point_features.contiguous(), center_features.contiguous(), scores.contiguous(), knn_idx.contiguous(), ou... | <|body_start_0|>
agg = {'sum': 0, 'avg': 1, 'max': 2}
B, N, M, out_dim = point_features.size()
_, npoint, K, _ = scores.size()
output = point_features.new_zeros((B, out_dim, npoint, K))
ext_module.assign_score_withk_forward(point_features.contiguous(), center_features.contiguous(... | Perform weighted sum to generate output features according to scores. Modified from `PAConv <https://github.com/CVMI-Lab/PAConv/tree/main/ scene_seg/lib/paconv_lib/src/gpu>`_. This is a memory-efficient CUDA implementation of assign_scores operation, which first transform all point features with weight bank, then assem... | AssignScoreWithK | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AssignScoreWithK:
"""Perform weighted sum to generate output features according to scores. Modified from `PAConv <https://github.com/CVMI-Lab/PAConv/tree/main/ scene_seg/lib/paconv_lib/src/gpu>`_. This is a memory-efficient CUDA implementation of assign_scores operation, which first transform all... | stack_v2_sparse_classes_36k_train_011352 | 4,761 | permissive | [
{
"docstring": "Args: scores (torch.Tensor): (B, npoint, K, M), predicted scores to aggregate weight matrices in the weight bank. ``npoint`` is the number of sampled centers. ``K`` is the number of queried neighbors. ``M`` is the number of weight matrices in the weight bank. point_features (torch.Tensor): (B, N... | 2 | null | Implement the Python class `AssignScoreWithK` described below.
Class description:
Perform weighted sum to generate output features according to scores. Modified from `PAConv <https://github.com/CVMI-Lab/PAConv/tree/main/ scene_seg/lib/paconv_lib/src/gpu>`_. This is a memory-efficient CUDA implementation of assign_scor... | Implement the Python class `AssignScoreWithK` described below.
Class description:
Perform weighted sum to generate output features according to scores. Modified from `PAConv <https://github.com/CVMI-Lab/PAConv/tree/main/ scene_seg/lib/paconv_lib/src/gpu>`_. This is a memory-efficient CUDA implementation of assign_scor... | 6e9ee26718b22961d5c34caca4108413b1b7b3af | <|skeleton|>
class AssignScoreWithK:
"""Perform weighted sum to generate output features according to scores. Modified from `PAConv <https://github.com/CVMI-Lab/PAConv/tree/main/ scene_seg/lib/paconv_lib/src/gpu>`_. This is a memory-efficient CUDA implementation of assign_scores operation, which first transform all... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AssignScoreWithK:
"""Perform weighted sum to generate output features according to scores. Modified from `PAConv <https://github.com/CVMI-Lab/PAConv/tree/main/ scene_seg/lib/paconv_lib/src/gpu>`_. This is a memory-efficient CUDA implementation of assign_scores operation, which first transform all point featur... | the_stack_v2_python_sparse | mmcv/ops/assign_score_withk.py | open-mmlab/mmcv | train | 5,319 |
2bfd235494d5c9710a4c67cd610b5bced84ba325 | [
"LOG.debug('Setting register {:#0x} to value {}'.format(p_register, p_values))\nBuildCommand.write_register_command(p_controller_obj, p_register, p_values)\npass",
"l_val = bytearray(1)\nl_val[0] = 3\nself.set_register_value(255, 112, l_val)"
] | <|body_start_0|>
LOG.debug('Setting register {:#0x} to value {}'.format(p_register, p_values))
BuildCommand.write_register_command(p_controller_obj, p_register, p_values)
pass
<|end_body_0|>
<|body_start_1|>
l_val = bytearray(1)
l_val[0] = 3
self.set_register_value(255, ... | CreateCommands | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CreateCommands:
def set_register_value(self, p_controller_obj, p_register, p_values):
"""Set one of the device's registers."""
<|body_0|>
def set_pim_mode(self):
"""Set the PIM operating mode: Page 6 of UPB Powerline Interface Module (PIM) Description Version 1.6 The... | stack_v2_sparse_classes_36k_train_011353 | 17,549 | permissive | [
{
"docstring": "Set one of the device's registers.",
"name": "set_register_value",
"signature": "def set_register_value(self, p_controller_obj, p_register, p_values)"
},
{
"docstring": "Set the PIM operating mode: Page 6 of UPB Powerline Interface Module (PIM) Description Version 1.6 The PIM mod... | 2 | stack_v2_sparse_classes_30k_train_018413 | Implement the Python class `CreateCommands` described below.
Class description:
Implement the CreateCommands class.
Method signatures and docstrings:
- def set_register_value(self, p_controller_obj, p_register, p_values): Set one of the device's registers.
- def set_pim_mode(self): Set the PIM operating mode: Page 6 ... | Implement the Python class `CreateCommands` described below.
Class description:
Implement the CreateCommands class.
Method signatures and docstrings:
- def set_register_value(self, p_controller_obj, p_register, p_values): Set one of the device's registers.
- def set_pim_mode(self): Set the PIM operating mode: Page 6 ... | a100fc67761a22ae47ed6f21f3c9464e2de5d54f | <|skeleton|>
class CreateCommands:
def set_register_value(self, p_controller_obj, p_register, p_values):
"""Set one of the device's registers."""
<|body_0|>
def set_pim_mode(self):
"""Set the PIM operating mode: Page 6 of UPB Powerline Interface Module (PIM) Description Version 1.6 The... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CreateCommands:
def set_register_value(self, p_controller_obj, p_register, p_values):
"""Set one of the device's registers."""
LOG.debug('Setting register {:#0x} to value {}'.format(p_register, p_values))
BuildCommand.write_register_command(p_controller_obj, p_register, p_values)
... | the_stack_v2_python_sparse | Project/src/Modules/House/Family/Upb/upb_pim.py | DBrianKimmel/PyHouse | train | 3 | |
4b33d93571bf4e476b3ab0841d2d1887877a1af7 | [
"y = model.layers[logits_layer_index].output[0, class_index]\ntarget_conv_layer_output = model.layers[target_conv_layer_index].output\ngrads = normalize(K.gradients(y, target_conv_layer_output)[0])\ngradient_function = K.function([model.input, K.learning_phase()], [target_conv_layer_output, grads])\n'\\n The... | <|body_start_0|>
y = model.layers[logits_layer_index].output[0, class_index]
target_conv_layer_output = model.layers[target_conv_layer_index].output
grads = normalize(K.gradients(y, target_conv_layer_output)[0])
gradient_function = K.function([model.input, K.learning_phase()], [target_co... | GradCAM | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GradCAM:
def __init__(self, model, logits_layer_index, target_conv_layer_index, class_index, number_of_classes):
"""Parameters ---------- model : keras.model.Model CNN model. logits_layer_index : int Index of layer outputting logits. target_conv_layer_index : int Index of last convolutio... | stack_v2_sparse_classes_36k_train_011354 | 11,820 | permissive | [
{
"docstring": "Parameters ---------- model : keras.model.Model CNN model. logits_layer_index : int Index of layer outputting logits. target_conv_layer_index : int Index of last convolutional layer. class_index : int Targeted class index to visualize. number_of_classes : int Number of all classes output by mode... | 2 | stack_v2_sparse_classes_30k_train_014761 | Implement the Python class `GradCAM` described below.
Class description:
Implement the GradCAM class.
Method signatures and docstrings:
- def __init__(self, model, logits_layer_index, target_conv_layer_index, class_index, number_of_classes): Parameters ---------- model : keras.model.Model CNN model. logits_layer_inde... | Implement the Python class `GradCAM` described below.
Class description:
Implement the GradCAM class.
Method signatures and docstrings:
- def __init__(self, model, logits_layer_index, target_conv_layer_index, class_index, number_of_classes): Parameters ---------- model : keras.model.Model CNN model. logits_layer_inde... | 5d976f11f7487d2c76eec030c15dd52e73d6a48b | <|skeleton|>
class GradCAM:
def __init__(self, model, logits_layer_index, target_conv_layer_index, class_index, number_of_classes):
"""Parameters ---------- model : keras.model.Model CNN model. logits_layer_index : int Index of layer outputting logits. target_conv_layer_index : int Index of last convolutio... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class GradCAM:
def __init__(self, model, logits_layer_index, target_conv_layer_index, class_index, number_of_classes):
"""Parameters ---------- model : keras.model.Model CNN model. logits_layer_index : int Index of layer outputting logits. target_conv_layer_index : int Index of last convolutional layer. cla... | the_stack_v2_python_sparse | drl_lab/gcam.py | chavdim/drl_lab | train | 1 | |
c77258afa463e66c019dbf252722c2311f6aa680 | [
"if use_active_config:\n return os.path.join(os.environ['HOME'], '.active')\nreturn os.path.join(find_pdaq_config(), '.config')",
"cluster_file = cls.__get_cached_name_path(use_active_config)\ntry:\n with open(cluster_file, 'r') as fin:\n ret = fin.readline()\n if ret is not None:\n ... | <|body_start_0|>
if use_active_config:
return os.path.join(os.environ['HOME'], '.active')
return os.path.join(find_pdaq_config(), '.config')
<|end_body_0|>
<|body_start_1|>
cluster_file = cls.__get_cached_name_path(use_active_config)
try:
with open(cluster_file, ... | Manage pDAQ's run configuration name, in either ~/.active or in $PDAQ_CONFIG/.config | CachedFile | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CachedFile:
"""Manage pDAQ's run configuration name, in either ~/.active or in $PDAQ_CONFIG/.config"""
def __get_cached_name_path(cls, use_active_config):
"""get the active or default cluster configuration"""
<|body_0|>
def __read_cache_file(cls, use_active_config):
... | stack_v2_sparse_classes_36k_train_011355 | 2,960 | no_license | [
{
"docstring": "get the active or default cluster configuration",
"name": "__get_cached_name_path",
"signature": "def __get_cached_name_path(cls, use_active_config)"
},
{
"docstring": "read the single line of cached text",
"name": "__read_cache_file",
"signature": "def __read_cache_file(... | 5 | null | Implement the Python class `CachedFile` described below.
Class description:
Manage pDAQ's run configuration name, in either ~/.active or in $PDAQ_CONFIG/.config
Method signatures and docstrings:
- def __get_cached_name_path(cls, use_active_config): get the active or default cluster configuration
- def __read_cache_fi... | Implement the Python class `CachedFile` described below.
Class description:
Manage pDAQ's run configuration name, in either ~/.active or in $PDAQ_CONFIG/.config
Method signatures and docstrings:
- def __get_cached_name_path(cls, use_active_config): get the active or default cluster configuration
- def __read_cache_fi... | 718189be62907a6a8031980fe0c41fa7e06b898d | <|skeleton|>
class CachedFile:
"""Manage pDAQ's run configuration name, in either ~/.active or in $PDAQ_CONFIG/.config"""
def __get_cached_name_path(cls, use_active_config):
"""get the active or default cluster configuration"""
<|body_0|>
def __read_cache_file(cls, use_active_config):
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CachedFile:
"""Manage pDAQ's run configuration name, in either ~/.active or in $PDAQ_CONFIG/.config"""
def __get_cached_name_path(cls, use_active_config):
"""get the active or default cluster configuration"""
if use_active_config:
return os.path.join(os.environ['HOME'], '.acti... | the_stack_v2_python_sparse | CachedConfigName.py | dglo/dash | train | 0 |
7bfb41f23d8f7fc12966d1af1d3013a2ccb78fee | [
"if size < 3:\n raise Exception('Cells must be at least three pixels wide')\nself.height = height\nself.width = width\nself.size = size\nself.numrows = height / size\nself.numcols = width / size\nself.construct()",
"master = Tkinter.Tk()\nw = Tkinter.Canvas(master, width=self.width + 10, height=self.height + 1... | <|body_start_0|>
if size < 3:
raise Exception('Cells must be at least three pixels wide')
self.height = height
self.width = width
self.size = size
self.numrows = height / size
self.numcols = width / size
self.construct()
<|end_body_0|>
<|body_start_1|... | Maze | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Maze:
def __init__(self, height, width, size):
"""initialize maze"""
<|body_0|>
def view(self):
"""Show window with maze"""
<|body_1|>
def clearWall(self, fromCell, toCell):
"""Remove wall between two cells"""
<|body_2|>
def dfsVisit... | stack_v2_sparse_classes_36k_train_011356 | 4,512 | no_license | [
{
"docstring": "initialize maze",
"name": "__init__",
"signature": "def __init__(self, height, width, size)"
},
{
"docstring": "Show window with maze",
"name": "view",
"signature": "def view(self)"
},
{
"docstring": "Remove wall between two cells",
"name": "clearWall",
"s... | 6 | null | Implement the Python class `Maze` described below.
Class description:
Implement the Maze class.
Method signatures and docstrings:
- def __init__(self, height, width, size): initialize maze
- def view(self): Show window with maze
- def clearWall(self, fromCell, toCell): Remove wall between two cells
- def dfsVisit(sel... | Implement the Python class `Maze` described below.
Class description:
Implement the Maze class.
Method signatures and docstrings:
- def __init__(self, height, width, size): initialize maze
- def view(self): Show window with maze
- def clearWall(self, fromCell, toCell): Remove wall between two cells
- def dfsVisit(sel... | 929dde1723fb2f54870c8a9badc80fc23e8400d3 | <|skeleton|>
class Maze:
def __init__(self, height, width, size):
"""initialize maze"""
<|body_0|>
def view(self):
"""Show window with maze"""
<|body_1|>
def clearWall(self, fromCell, toCell):
"""Remove wall between two cells"""
<|body_2|>
def dfsVisit... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Maze:
def __init__(self, height, width, size):
"""initialize maze"""
if size < 3:
raise Exception('Cells must be at least three pixels wide')
self.height = height
self.width = width
self.size = size
self.numrows = height / size
self.numcols =... | the_stack_v2_python_sparse | Working with Algorithms in Python/6. DepthFirstSearch/maze.py | syurskyi/Algorithms_and_Data_Structure | train | 4 | |
a92414f009d71ad0531650a70f661a186c18c476 | [
"super().__init__()\nself.generator = generator_cls(img_shape[0], img_shape[0], num_residuals)\nself.feature_extractor = feature_extractor_cls()\nself.discriminator = discriminator_cls(img_shape[0])",
"gen_hr = self.generator(imgs_lr)\ngen_features = self.feature_extractor(gen_hr)\nreal_features = self.feature_ex... | <|body_start_0|>
super().__init__()
self.generator = generator_cls(img_shape[0], img_shape[0], num_residuals)
self.feature_extractor = feature_extractor_cls()
self.discriminator = discriminator_cls(img_shape[0])
<|end_body_0|>
<|body_start_1|>
gen_hr = self.generator(imgs_lr)
... | Class implementing Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network References ---------- `Paper <https://arxiv.org/abs/1609.04802>`_ Warnings -------- This Network is designed for training only; if you want to predict from an already trained network, it might be best, to split this ... | SuperResolutionGAN | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SuperResolutionGAN:
"""Class implementing Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network References ---------- `Paper <https://arxiv.org/abs/1609.04802>`_ Warnings -------- This Network is designed for training only; if you want to predict from an already tra... | stack_v2_sparse_classes_36k_train_011357 | 4,479 | permissive | [
{
"docstring": "Parameters ---------- img_shape : tuple the shape of the input/generated images (including channels, excluding batch dimension) num_residuals : int number of residual blocks inside the generator generator_cls : class implementing the actual generator topology feature_extractor_cls : class implem... | 2 | null | Implement the Python class `SuperResolutionGAN` described below.
Class description:
Class implementing Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network References ---------- `Paper <https://arxiv.org/abs/1609.04802>`_ Warnings -------- This Network is designed for training only; if ... | Implement the Python class `SuperResolutionGAN` described below.
Class description:
Class implementing Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network References ---------- `Paper <https://arxiv.org/abs/1609.04802>`_ Warnings -------- This Network is designed for training only; if ... | 1078f5030b8aac2bf022daf5fa14d66f74c3c893 | <|skeleton|>
class SuperResolutionGAN:
"""Class implementing Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network References ---------- `Paper <https://arxiv.org/abs/1609.04802>`_ Warnings -------- This Network is designed for training only; if you want to predict from an already tra... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SuperResolutionGAN:
"""Class implementing Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network References ---------- `Paper <https://arxiv.org/abs/1609.04802>`_ Warnings -------- This Network is designed for training only; if you want to predict from an already trained network,... | the_stack_v2_python_sparse | dlutils/models/gans/super_resolution/sr_gan.py | justusschock/dl-utils | train | 15 |
f0a583739c9777f0b9d30126a71a4df0d12ddcd4 | [
"if not quota_max_calls:\n use_rate_limiter = False\nself._projects_locations = None\nself._projects_zones = None\nself._projects_zones_clusters = None\nsuper(ContainerRepositoryClient, self).__init__(API_NAME, versions=['v1', 'v1beta1'], quota_max_calls=quota_max_calls, quota_period=quota_period, use_rate_limit... | <|body_start_0|>
if not quota_max_calls:
use_rate_limiter = False
self._projects_locations = None
self._projects_zones = None
self._projects_zones_clusters = None
super(ContainerRepositoryClient, self).__init__(API_NAME, versions=['v1', 'v1beta1'], quota_max_calls=quo... | Cloud Kubernetes Engine API Respository. | ContainerRepositoryClient | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ContainerRepositoryClient:
"""Cloud Kubernetes Engine API Respository."""
def __init__(self, quota_max_calls=None, quota_period=100.0, use_rate_limiter=True):
"""Constructor. Args: quota_max_calls (int): Allowed requests per <quota_period> for the API. quota_period (float): The time ... | stack_v2_sparse_classes_36k_train_011358 | 10,586 | permissive | [
{
"docstring": "Constructor. Args: quota_max_calls (int): Allowed requests per <quota_period> for the API. quota_period (float): The time period to track requests over. use_rate_limiter (bool): Set to false to disable the use of a rate limiter for this service.",
"name": "__init__",
"signature": "def __... | 4 | null | Implement the Python class `ContainerRepositoryClient` described below.
Class description:
Cloud Kubernetes Engine API Respository.
Method signatures and docstrings:
- def __init__(self, quota_max_calls=None, quota_period=100.0, use_rate_limiter=True): Constructor. Args: quota_max_calls (int): Allowed requests per <q... | Implement the Python class `ContainerRepositoryClient` described below.
Class description:
Cloud Kubernetes Engine API Respository.
Method signatures and docstrings:
- def __init__(self, quota_max_calls=None, quota_period=100.0, use_rate_limiter=True): Constructor. Args: quota_max_calls (int): Allowed requests per <q... | d4421afa50a17ed47cbebe942044ebab3720e0f5 | <|skeleton|>
class ContainerRepositoryClient:
"""Cloud Kubernetes Engine API Respository."""
def __init__(self, quota_max_calls=None, quota_period=100.0, use_rate_limiter=True):
"""Constructor. Args: quota_max_calls (int): Allowed requests per <quota_period> for the API. quota_period (float): The time ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ContainerRepositoryClient:
"""Cloud Kubernetes Engine API Respository."""
def __init__(self, quota_max_calls=None, quota_period=100.0, use_rate_limiter=True):
"""Constructor. Args: quota_max_calls (int): Allowed requests per <quota_period> for the API. quota_period (float): The time period to tra... | the_stack_v2_python_sparse | google/cloud/forseti/common/gcp_api/container.py | kevensen/forseti-security | train | 1 |
a9cebafc33846c8daeaa7bd9981d61d1fffa2021 | [
"try:\n data = open('addRequest_schema.json', 'rU').read()\nexcept IOError as e:\n print('Error loading input schema: ' + e.strerror)\n sys.exit(1)\ntry:\n Handler.requestSchema = loads(data)\nexcept Exception as e:\n print('Invalid JSON content in input schema')\n sys.exit(1)",
"print('Request ... | <|body_start_0|>
try:
data = open('addRequest_schema.json', 'rU').read()
except IOError as e:
print('Error loading input schema: ' + e.strerror)
sys.exit(1)
try:
Handler.requestSchema = loads(data)
except Exception as e:
print('... | HTTP request handler | Handler | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Handler:
"""HTTP request handler"""
def loadRequestSchema(self):
"""load request schema from file (once only)"""
<|body_0|>
def do_POST(self):
"""process POST, generate response"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
try:
da... | stack_v2_sparse_classes_36k_train_011359 | 3,656 | permissive | [
{
"docstring": "load request schema from file (once only)",
"name": "loadRequestSchema",
"signature": "def loadRequestSchema(self)"
},
{
"docstring": "process POST, generate response",
"name": "do_POST",
"signature": "def do_POST(self)"
}
] | 2 | stack_v2_sparse_classes_30k_test_000807 | Implement the Python class `Handler` described below.
Class description:
HTTP request handler
Method signatures and docstrings:
- def loadRequestSchema(self): load request schema from file (once only)
- def do_POST(self): process POST, generate response | Implement the Python class `Handler` described below.
Class description:
HTTP request handler
Method signatures and docstrings:
- def loadRequestSchema(self): load request schema from file (once only)
- def do_POST(self): process POST, generate response
<|skeleton|>
class Handler:
"""HTTP request handler"""
... | d6e8ca06c70e31bff0e56f7d94bfa0bd835bf61c | <|skeleton|>
class Handler:
"""HTTP request handler"""
def loadRequestSchema(self):
"""load request schema from file (once only)"""
<|body_0|>
def do_POST(self):
"""process POST, generate response"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Handler:
"""HTTP request handler"""
def loadRequestSchema(self):
"""load request schema from file (once only)"""
try:
data = open('addRequest_schema.json', 'rU').read()
except IOError as e:
print('Error loading input schema: ' + e.strerror)
sys.... | the_stack_v2_python_sparse | chapter7/additionService.py | MikeBeaulieu/ujs-book-materials | train | 0 |
d5c862540a2610ae9af0d3b464a01424a13aa362 | [
"circuit = ic.IBMCircuit(10)\nw1 = iw.IBMInputWire('w1', circuit)\ng = igr.IBMRotateGate('g', w1, 5, circuit)\nself.assertEquals('g:LROTATE(w1,5)', g.get_full_display_string())",
"circuit1 = ic.IBMCircuit(600)\nw1 = iw.IBMInputWire('w1', circuit1)\ng1 = igr.IBMRotateGate('g1', w1, 3, circuit1)\nself.assertEqual(0... | <|body_start_0|>
circuit = ic.IBMCircuit(10)
w1 = iw.IBMInputWire('w1', circuit)
g = igr.IBMRotateGate('g', w1, 5, circuit)
self.assertEquals('g:LROTATE(w1,5)', g.get_full_display_string())
<|end_body_0|>
<|body_start_1|>
circuit1 = ic.IBMCircuit(600)
w1 = iw.IBMInputWir... | TestRotateGate | [
"BSD-2-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestRotateGate:
def test_get_full_display_string(self):
"""Tests that the method get_full_display_string returns the correct string."""
<|body_0|>
def test_get_depth(self):
"""Tests that the get_depth method returns the correct depth, as defined by IBM."""
<|... | stack_v2_sparse_classes_36k_train_011360 | 2,354 | permissive | [
{
"docstring": "Tests that the method get_full_display_string returns the correct string.",
"name": "test_get_full_display_string",
"signature": "def test_get_full_display_string(self)"
},
{
"docstring": "Tests that the get_depth method returns the correct depth, as defined by IBM.",
"name":... | 2 | stack_v2_sparse_classes_30k_train_005673 | Implement the Python class `TestRotateGate` described below.
Class description:
Implement the TestRotateGate class.
Method signatures and docstrings:
- def test_get_full_display_string(self): Tests that the method get_full_display_string returns the correct string.
- def test_get_depth(self): Tests that the get_depth... | Implement the Python class `TestRotateGate` described below.
Class description:
Implement the TestRotateGate class.
Method signatures and docstrings:
- def test_get_full_display_string(self): Tests that the method get_full_display_string returns the correct string.
- def test_get_depth(self): Tests that the get_depth... | 264459a8fa1480c7b65d946f88d94af1a038fbf1 | <|skeleton|>
class TestRotateGate:
def test_get_full_display_string(self):
"""Tests that the method get_full_display_string returns the correct string."""
<|body_0|>
def test_get_depth(self):
"""Tests that the get_depth method returns the correct depth, as defined by IBM."""
<|... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TestRotateGate:
def test_get_full_display_string(self):
"""Tests that the method get_full_display_string returns the correct string."""
circuit = ic.IBMCircuit(10)
w1 = iw.IBMInputWire('w1', circuit)
g = igr.IBMRotateGate('g', w1, 5, circuit)
self.assertEquals('g:LROTAT... | the_stack_v2_python_sparse | hetest/python/circuit_generation/ibm/ibm_gate_rotate_test.py | y4n9squared/HEtest | train | 7 | |
c42548783cf725937afdc9c2d8c90be62fa27cae | [
"self.project_path = project_path\nself.package_path = package_path\nself.filename_path = filename_path\nself.project = self.package = self.filename = None\nif self.project_path is not None:\n self.project = wc_read_project(self.project_path)\nif self.package_path is not None:\n if wc_is_package(self.package_... | <|body_start_0|>
self.project_path = project_path
self.package_path = package_path
self.filename_path = filename_path
self.project = self.package = self.filename = None
if self.project_path is not None:
self.project = wc_read_project(self.project_path)
if self... | Represents a wc path. | WCPath | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class WCPath:
"""Represents a wc path."""
def __init__(self, project_path, package_path, filename_path):
"""Constructs a new WCPath object. project_path is the path to the project wc. package_path is the path to the package wc. filename_path is the path to the wc filename. Either project_p... | stack_v2_sparse_classes_36k_train_011361 | 19,804 | no_license | [
{
"docstring": "Constructs a new WCPath object. project_path is the path to the project wc. package_path is the path to the package wc. filename_path is the path to the wc filename. Either project_path or package_path or both aren't None.",
"name": "__init__",
"signature": "def __init__(self, project_pa... | 3 | stack_v2_sparse_classes_30k_train_011161 | Implement the Python class `WCPath` described below.
Class description:
Represents a wc path.
Method signatures and docstrings:
- def __init__(self, project_path, package_path, filename_path): Constructs a new WCPath object. project_path is the path to the project wc. package_path is the path to the package wc. filen... | Implement the Python class `WCPath` described below.
Class description:
Represents a wc path.
Method signatures and docstrings:
- def __init__(self, project_path, package_path, filename_path): Constructs a new WCPath object. project_path is the path to the project wc. package_path is the path to the package wc. filen... | fd75a75371ae33740a68913ca8ab64a9e8e6654a | <|skeleton|>
class WCPath:
"""Represents a wc path."""
def __init__(self, project_path, package_path, filename_path):
"""Constructs a new WCPath object. project_path is the path to the project wc. package_path is the path to the package wc. filename_path is the path to the wc filename. Either project_p... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class WCPath:
"""Represents a wc path."""
def __init__(self, project_path, package_path, filename_path):
"""Constructs a new WCPath object. project_path is the path to the project wc. package_path is the path to the package wc. filename_path is the path to the wc filename. Either project_path or packag... | the_stack_v2_python_sparse | osc2/oscargs.py | openSUSE/osc2 | train | 16 |
e25ebfa45bac614b5b7a12b4c6d3bf5802f775c3 | [
"from Acquisition import aq_inner\nfrom zope.component import getUtility\nfrom zope.intid.interfaces import IIntIds\nfrom zope.security import checkPermission\nfrom zc.relation.interfaces import ICatalog\ncatalog = getUtility(ICatalog)\nintids = getUtility(IIntIds)\nresult = []\nfor rel in catalog.findRelations(dic... | <|body_start_0|>
from Acquisition import aq_inner
from zope.component import getUtility
from zope.intid.interfaces import IIntIds
from zope.security import checkPermission
from zc.relation.interfaces import ICatalog
catalog = getUtility(ICatalog)
intids = getUtili... | BorrowableItem | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BorrowableItem:
def getBorrowRequests(self):
"""Return all borrow requests referencing the curret item"""
<|body_0|>
def isBorrowable(self, start=None, end=None):
"""Is current item borrowable from start-end ?"""
<|body_1|>
def getBookingDates(self):
... | stack_v2_sparse_classes_36k_train_011362 | 5,740 | no_license | [
{
"docstring": "Return all borrow requests referencing the curret item",
"name": "getBorrowRequests",
"signature": "def getBorrowRequests(self)"
},
{
"docstring": "Is current item borrowable from start-end ?",
"name": "isBorrowable",
"signature": "def isBorrowable(self, start=None, end=N... | 4 | null | Implement the Python class `BorrowableItem` described below.
Class description:
Implement the BorrowableItem class.
Method signatures and docstrings:
- def getBorrowRequests(self): Return all borrow requests referencing the curret item
- def isBorrowable(self, start=None, end=None): Is current item borrowable from st... | Implement the Python class `BorrowableItem` described below.
Class description:
Implement the BorrowableItem class.
Method signatures and docstrings:
- def getBorrowRequests(self): Return all borrow requests referencing the curret item
- def isBorrowable(self, start=None, end=None): Is current item borrowable from st... | 62203ae995bd708dc81809cc8698c0b24208735e | <|skeleton|>
class BorrowableItem:
def getBorrowRequests(self):
"""Return all borrow requests referencing the curret item"""
<|body_0|>
def isBorrowable(self, start=None, end=None):
"""Is current item borrowable from start-end ?"""
<|body_1|>
def getBookingDates(self):
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class BorrowableItem:
def getBorrowRequests(self):
"""Return all borrow requests referencing the curret item"""
from Acquisition import aq_inner
from zope.component import getUtility
from zope.intid.interfaces import IIntIds
from zope.security import checkPermission
f... | the_stack_v2_python_sparse | nva.borrow/tags/before-refactoring/nva/borrow/borrowableitem.py | witsch/novareto | train | 0 | |
eb5e92fbe994625d45178f8d38ffce78e54b3ca6 | [
"if root == None:\n return False\nelif root.left == None and root.right == None and (root.val == sum):\n return True\nreturn self.hasPathSum(root.left, sum - root.val) or self.hasPathSum(root.right, sum - root.val)",
"if root == None:\n return False\nnode_array = [root]\npath_sum_array = [root.val]\nwhil... | <|body_start_0|>
if root == None:
return False
elif root.left == None and root.right == None and (root.val == sum):
return True
return self.hasPathSum(root.left, sum - root.val) or self.hasPathSum(root.right, sum - root.val)
<|end_body_0|>
<|body_start_1|>
if roo... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def hasPathSum(self, root, sum):
""":type root: TreeNode :type sum: int :rtype: bool"""
<|body_0|>
def hasPathSumIterate(self, root, sum):
"""层序遍历"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
if root == None:
return False
... | stack_v2_sparse_classes_36k_train_011363 | 1,828 | no_license | [
{
"docstring": ":type root: TreeNode :type sum: int :rtype: bool",
"name": "hasPathSum",
"signature": "def hasPathSum(self, root, sum)"
},
{
"docstring": "层序遍历",
"name": "hasPathSumIterate",
"signature": "def hasPathSumIterate(self, root, sum)"
}
] | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def hasPathSum(self, root, sum): :type root: TreeNode :type sum: int :rtype: bool
- def hasPathSumIterate(self, root, sum): 层序遍历 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def hasPathSum(self, root, sum): :type root: TreeNode :type sum: int :rtype: bool
- def hasPathSumIterate(self, root, sum): 层序遍历
<|skeleton|>
class Solution:
def hasPathSum... | 8853f85214ac88db024d26e228f1848dd5acd933 | <|skeleton|>
class Solution:
def hasPathSum(self, root, sum):
""":type root: TreeNode :type sum: int :rtype: bool"""
<|body_0|>
def hasPathSumIterate(self, root, sum):
"""层序遍历"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def hasPathSum(self, root, sum):
""":type root: TreeNode :type sum: int :rtype: bool"""
if root == None:
return False
elif root.left == None and root.right == None and (root.val == sum):
return True
return self.hasPathSum(root.left, sum - root.... | the_stack_v2_python_sparse | 112-PathSum/PathSum.py | cqxmzhc/my_leetcode_solutions | train | 2 | |
9f3cac440f4bcc03cc02f5ab837e182d6d69c7d0 | [
"SpeciesOrderedKey.__init__(self, species)\nself.opponents = opponents\nself.feed_type = get_species_feed_type(species, owning_player, opponents)",
"if self.feed_type is not other.feed_type:\n return self.feed_type.value > other.feed_type.value\nif self.feed_type is SpeciesFeedType.STORE_FAT:\n return FatOr... | <|body_start_0|>
SpeciesOrderedKey.__init__(self, species)
self.opponents = opponents
self.feed_type = get_species_feed_type(species, owning_player, opponents)
<|end_body_0|>
<|body_start_1|>
if self.feed_type is not other.feed_type:
return self.feed_type.value > other.feed_... | Representing a way of comparing Species according to the Simple Player specifications The Key Prioritizes Feed Type then for Store Foot then Prioritizes the fat food need Then Prioritizes by lexicographical by population, food already fed, and body size | SimpleSpeciesOrderedKey | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SimpleSpeciesOrderedKey:
"""Representing a way of comparing Species according to the Simple Player specifications The Key Prioritizes Feed Type then for Store Foot then Prioritizes the fat food need Then Prioritizes by lexicographical by population, food already fed, and body size"""
def __i... | stack_v2_sparse_classes_36k_train_011364 | 6,072 | no_license | [
{
"docstring": "Construct a Species Pick Key :param species: The Species :param owning_player: The PlayerState that owns the Species :param opponents: The Opponents' PlayerStates",
"name": "__init__",
"signature": "def __init__(self, species: Species, owning_player: IPlayer, opponents: List[IPlayer])"
... | 3 | null | Implement the Python class `SimpleSpeciesOrderedKey` described below.
Class description:
Representing a way of comparing Species according to the Simple Player specifications The Key Prioritizes Feed Type then for Store Foot then Prioritizes the fat food need Then Prioritizes by lexicographical by population, food alr... | Implement the Python class `SimpleSpeciesOrderedKey` described below.
Class description:
Representing a way of comparing Species according to the Simple Player specifications The Key Prioritizes Feed Type then for Store Foot then Prioritizes the fat food need Then Prioritizes by lexicographical by population, food alr... | a59252a7d55a474bcb2b469414902c585bc89641 | <|skeleton|>
class SimpleSpeciesOrderedKey:
"""Representing a way of comparing Species according to the Simple Player specifications The Key Prioritizes Feed Type then for Store Foot then Prioritizes the fat food need Then Prioritizes by lexicographical by population, food already fed, and body size"""
def __i... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SimpleSpeciesOrderedKey:
"""Representing a way of comparing Species according to the Simple Player specifications The Key Prioritizes Feed Type then for Store Foot then Prioritizes the fat food need Then Prioritizes by lexicographical by population, food already fed, and body size"""
def __init__(self, s... | the_stack_v2_python_sparse | evolution/external_players/silly/silly_species_keys.py | escowart/SoftwareDev | train | 0 |
bb170026178c714934d782b7989b93ed3159126d | [
"min_height = float('inf')\nupdated = False\n\ndef traverse(n, height=0):\n nonlocal min_height, updated\n if n == None:\n if height < min_height:\n if updated:\n return False\n else:\n if min_height < float('inf'):\n updated = True... | <|body_start_0|>
min_height = float('inf')
updated = False
def traverse(n, height=0):
nonlocal min_height, updated
if n == None:
if height < min_height:
if updated:
return False
else:
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def isCompleteTree(self, root: TreeNode) -> bool:
"""May 30, 2020 18:23"""
<|body_0|>
def isCompleteTree(self, root: Optional[TreeNode]) -> bool:
"""Apr 23, 2023 17:45"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
min_height = float('inf... | stack_v2_sparse_classes_36k_train_011365 | 3,289 | no_license | [
{
"docstring": "May 30, 2020 18:23",
"name": "isCompleteTree",
"signature": "def isCompleteTree(self, root: TreeNode) -> bool"
},
{
"docstring": "Apr 23, 2023 17:45",
"name": "isCompleteTree",
"signature": "def isCompleteTree(self, root: Optional[TreeNode]) -> bool"
}
] | 2 | stack_v2_sparse_classes_30k_train_009575 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def isCompleteTree(self, root: TreeNode) -> bool: May 30, 2020 18:23
- def isCompleteTree(self, root: Optional[TreeNode]) -> bool: Apr 23, 2023 17:45 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def isCompleteTree(self, root: TreeNode) -> bool: May 30, 2020 18:23
- def isCompleteTree(self, root: Optional[TreeNode]) -> bool: Apr 23, 2023 17:45
<|skeleton|>
class Solution... | 1389a009a02e90e8700a7a00e0b7f797c129cdf4 | <|skeleton|>
class Solution:
def isCompleteTree(self, root: TreeNode) -> bool:
"""May 30, 2020 18:23"""
<|body_0|>
def isCompleteTree(self, root: Optional[TreeNode]) -> bool:
"""Apr 23, 2023 17:45"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def isCompleteTree(self, root: TreeNode) -> bool:
"""May 30, 2020 18:23"""
min_height = float('inf')
updated = False
def traverse(n, height=0):
nonlocal min_height, updated
if n == None:
if height < min_height:
... | the_stack_v2_python_sparse | leetcode/solved/998_Check_Completeness_of_a_Binary_Tree/solution.py | sungminoh/algorithms | train | 0 | |
cafdc7186c6ed19498c8b11081d55c2ad93c96de | [
"if isinstance(key, int):\n return TaggerID(key)\nif key not in TaggerID._member_map_:\n extend_enum(TaggerID, key, default)\nreturn TaggerID[key]",
"if not (isinstance(value, int) and 0 <= value <= 7):\n raise ValueError('%r is not a valid %s' % (value, cls.__name__))\nif 4 <= value <= 7:\n extend_en... | <|body_start_0|>
if isinstance(key, int):
return TaggerID(key)
if key not in TaggerID._member_map_:
extend_enum(TaggerID, key, default)
return TaggerID[key]
<|end_body_0|>
<|body_start_1|>
if not (isinstance(value, int) and 0 <= value <= 7):
raise Val... | [TaggerID] TaggerID Types | TaggerID | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TaggerID:
"""[TaggerID] TaggerID Types"""
def get(key, default=-1):
"""Backport support for original codes."""
<|body_0|>
def _missing_(cls, value):
"""Lookup function used when value is not found."""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
... | stack_v2_sparse_classes_36k_train_011366 | 1,119 | permissive | [
{
"docstring": "Backport support for original codes.",
"name": "get",
"signature": "def get(key, default=-1)"
},
{
"docstring": "Lookup function used when value is not found.",
"name": "_missing_",
"signature": "def _missing_(cls, value)"
}
] | 2 | stack_v2_sparse_classes_30k_train_018905 | Implement the Python class `TaggerID` described below.
Class description:
[TaggerID] TaggerID Types
Method signatures and docstrings:
- def get(key, default=-1): Backport support for original codes.
- def _missing_(cls, value): Lookup function used when value is not found. | Implement the Python class `TaggerID` described below.
Class description:
[TaggerID] TaggerID Types
Method signatures and docstrings:
- def get(key, default=-1): Backport support for original codes.
- def _missing_(cls, value): Lookup function used when value is not found.
<|skeleton|>
class TaggerID:
"""[Tagger... | 90cd07d67df28d5c5ab0585bc60f467a78d9db33 | <|skeleton|>
class TaggerID:
"""[TaggerID] TaggerID Types"""
def get(key, default=-1):
"""Backport support for original codes."""
<|body_0|>
def _missing_(cls, value):
"""Lookup function used when value is not found."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TaggerID:
"""[TaggerID] TaggerID Types"""
def get(key, default=-1):
"""Backport support for original codes."""
if isinstance(key, int):
return TaggerID(key)
if key not in TaggerID._member_map_:
extend_enum(TaggerID, key, default)
return TaggerID[key... | the_stack_v2_python_sparse | pcapkit/const/ipv6/tagger_id.py | stjordanis/PyPCAPKit | train | 0 |
e1feb9f30702a68fa07d73b5a5d57746551de7f3 | [
"self.elapsed = 0.0\nself._func = func\nself._start = None",
"if self._start is not None:\n raise RuntimeError('Already started')\nself._start = self._func()",
"if self._start is None:\n raise RuntimeError('Not started')\nend = self._func()\nself.elapsed += end - self._start\nself._start = None"
] | <|body_start_0|>
self.elapsed = 0.0
self._func = func
self._start = None
<|end_body_0|>
<|body_start_1|>
if self._start is not None:
raise RuntimeError('Already started')
self._start = self._func()
<|end_body_1|>
<|body_start_2|>
if self._start is None:
... | The timer function | Timer | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Timer:
"""The timer function"""
def __init__(self, func=time.perf_counter):
"""Initialize the timer object"""
<|body_0|>
def start(self):
"""Start the timer"""
<|body_1|>
def stop(self):
"""Stop the timer"""
<|body_2|>
<|end_skeleton... | stack_v2_sparse_classes_36k_train_011367 | 10,931 | no_license | [
{
"docstring": "Initialize the timer object",
"name": "__init__",
"signature": "def __init__(self, func=time.perf_counter)"
},
{
"docstring": "Start the timer",
"name": "start",
"signature": "def start(self)"
},
{
"docstring": "Stop the timer",
"name": "stop",
"signature"... | 3 | null | Implement the Python class `Timer` described below.
Class description:
The timer function
Method signatures and docstrings:
- def __init__(self, func=time.perf_counter): Initialize the timer object
- def start(self): Start the timer
- def stop(self): Stop the timer | Implement the Python class `Timer` described below.
Class description:
The timer function
Method signatures and docstrings:
- def __init__(self, func=time.perf_counter): Initialize the timer object
- def start(self): Start the timer
- def stop(self): Stop the timer
<|skeleton|>
class Timer:
"""The timer function... | 5dac60f39e3909ff05b26721d602ed20f14d6be3 | <|skeleton|>
class Timer:
"""The timer function"""
def __init__(self, func=time.perf_counter):
"""Initialize the timer object"""
<|body_0|>
def start(self):
"""Start the timer"""
<|body_1|>
def stop(self):
"""Stop the timer"""
<|body_2|>
<|end_skeleton... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Timer:
"""The timer function"""
def __init__(self, func=time.perf_counter):
"""Initialize the timer object"""
self.elapsed = 0.0
self._func = func
self._start = None
def start(self):
"""Start the timer"""
if self._start is not None:
raise R... | the_stack_v2_python_sparse | students/JoeNunnelley/lesson10/assignment/database.py | JavaRod/SP_Python220B_2019 | train | 1 |
f2ee7f09cd7883b0a77fa7228113203c47f4fb27 | [
"limit = df[CLOSE].min()\nif limit > 0:\n limit_df = df[df[CLOSE] <= limit * (2 - LIMIT_DETECT_LIMIT_FACTOR)]\n if not limit_df.empty:\n tm = limit_df.index[-1]\n return tm",
"limit = df[DIF].min()\nif limit < 0:\n return cls.__get_min_limit_tm(df[DIF], limit)",
"limit = df[MACD].min()\ni... | <|body_start_0|>
limit = df[CLOSE].min()
if limit > 0:
limit_df = df[df[CLOSE] <= limit * (2 - LIMIT_DETECT_LIMIT_FACTOR)]
if not limit_df.empty:
tm = limit_df.index[-1]
return tm
<|end_body_0|>
<|body_start_1|>
limit = df[DIF].min()
... | 检测极值：最小值的时间。用于检测3种极值的时间，3种极值分别是：DIF/CLOSE/MACD | MinLimitDetect | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MinLimitDetect:
"""检测极值：最小值的时间。用于检测3种极值的时间，3种极值分别是：DIF/CLOSE/MACD"""
def get_close_limit_tm_in(cls, df):
"""获取区间内close最小值的时间。 :param df: DataFrame类型， 相邻的死叉和金叉之间或两个死叉之间的所有数据[包含死叉点，不包含金叉点] :return:"""
<|body_0|>
def get_dif_limit_tm_in(cls, df):
"""获取区间内DIF最小值的时间。 ... | stack_v2_sparse_classes_36k_train_011368 | 36,499 | no_license | [
{
"docstring": "获取区间内close最小值的时间。 :param df: DataFrame类型， 相邻的死叉和金叉之间或两个死叉之间的所有数据[包含死叉点，不包含金叉点] :return:",
"name": "get_close_limit_tm_in",
"signature": "def get_close_limit_tm_in(cls, df)"
},
{
"docstring": "获取区间内DIF最小值的时间。 :param df: DataFrame类型， 相邻的死叉和金叉之间或两个死叉之间的所有数据[包含死叉点，不包含金叉点] :return:",
... | 4 | null | Implement the Python class `MinLimitDetect` described below.
Class description:
检测极值：最小值的时间。用于检测3种极值的时间，3种极值分别是：DIF/CLOSE/MACD
Method signatures and docstrings:
- def get_close_limit_tm_in(cls, df): 获取区间内close最小值的时间。 :param df: DataFrame类型， 相邻的死叉和金叉之间或两个死叉之间的所有数据[包含死叉点，不包含金叉点] :return:
- def get_dif_limit_tm_in(cls, ... | Implement the Python class `MinLimitDetect` described below.
Class description:
检测极值：最小值的时间。用于检测3种极值的时间，3种极值分别是：DIF/CLOSE/MACD
Method signatures and docstrings:
- def get_close_limit_tm_in(cls, df): 获取区间内close最小值的时间。 :param df: DataFrame类型， 相邻的死叉和金叉之间或两个死叉之间的所有数据[包含死叉点，不包含金叉点] :return:
- def get_dif_limit_tm_in(cls, ... | 9446d33c0978c325c8b24a876ac2c42fe323dbe6 | <|skeleton|>
class MinLimitDetect:
"""检测极值：最小值的时间。用于检测3种极值的时间，3种极值分别是：DIF/CLOSE/MACD"""
def get_close_limit_tm_in(cls, df):
"""获取区间内close最小值的时间。 :param df: DataFrame类型， 相邻的死叉和金叉之间或两个死叉之间的所有数据[包含死叉点，不包含金叉点] :return:"""
<|body_0|>
def get_dif_limit_tm_in(cls, df):
"""获取区间内DIF最小值的时间。 ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MinLimitDetect:
"""检测极值：最小值的时间。用于检测3种极值的时间，3种极值分别是：DIF/CLOSE/MACD"""
def get_close_limit_tm_in(cls, df):
"""获取区间内close最小值的时间。 :param df: DataFrame类型， 相邻的死叉和金叉之间或两个死叉之间的所有数据[包含死叉点，不包含金叉点] :return:"""
limit = df[CLOSE].min()
if limit > 0:
limit_df = df[df[CLOSE] <= limit... | the_stack_v2_python_sparse | back_forecast/learn_quant/MACD/jukuan_macd_signal.py | lnkyzhang/wayToFreedomOfWealth | train | 3 |
8650dd9b797d519d6ae2091bdcd65f209ab38e43 | [
"self.background_color = background_color\nself.border_color = border_color\nself.border_width = border_width\nself.corner_radius = corner_radius\nself.frame = frame\nself.flex = flex\nself.padding = padding\nself.name = name\nold_add_subview = self.add_subview\n\ndef new_add_subview(subviews):\n if not hasattr(... | <|body_start_0|>
self.background_color = background_color
self.border_color = border_color
self.border_width = border_width
self.corner_radius = corner_radius
self.frame = frame
self.flex = flex
self.padding = padding
self.name = name
old_add_subvi... | a subclass of View that automatically flows subviews in the order they were added. and reflows upon resize. also, set a few sane defaults, and expose some of the commonly midified params in thr constructor | FlowContainer | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FlowContainer:
"""a subclass of View that automatically flows subviews in the order they were added. and reflows upon resize. also, set a few sane defaults, and expose some of the commonly midified params in thr constructor"""
def __init__(self, background_color=(0.9, 0.9, 0.9), border_color... | stack_v2_sparse_classes_36k_train_011369 | 7,019 | no_license | [
{
"docstring": "initialize view. flex settings control layout.",
"name": "__init__",
"signature": "def __init__(self, background_color=(0.9, 0.9, 0.9), border_color=(0.5, 0.5, 0.5), border_width=1, corner_radius=5, frame=(0, 0, 200, 200), flex='WH', padding=5, subviews=None, name=None)"
},
{
"do... | 4 | stack_v2_sparse_classes_30k_train_017618 | Implement the Python class `FlowContainer` described below.
Class description:
a subclass of View that automatically flows subviews in the order they were added. and reflows upon resize. also, set a few sane defaults, and expose some of the commonly midified params in thr constructor
Method signatures and docstrings:... | Implement the Python class `FlowContainer` described below.
Class description:
a subclass of View that automatically flows subviews in the order they were added. and reflows upon resize. also, set a few sane defaults, and expose some of the commonly midified params in thr constructor
Method signatures and docstrings:... | 4caba2d48508eafa2477370923e96132947d7b24 | <|skeleton|>
class FlowContainer:
"""a subclass of View that automatically flows subviews in the order they were added. and reflows upon resize. also, set a few sane defaults, and expose some of the commonly midified params in thr constructor"""
def __init__(self, background_color=(0.9, 0.9, 0.9), border_color... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class FlowContainer:
"""a subclass of View that automatically flows subviews in the order they were added. and reflows upon resize. also, set a few sane defaults, and expose some of the commonly midified params in thr constructor"""
def __init__(self, background_color=(0.9, 0.9, 0.9), border_color=(0.5, 0.5, 0... | the_stack_v2_python_sparse | ui/uicontainer.py | c0ns0le/Pythonista | train | 3 |
d5c23848ed1c82e5906b30d63a3fd6ffda4361b1 | [
"self.vars = vars\nself.annot_body_code = annot_body_code\nself.indent = indent\nself.language = 'c'\npass",
"vname = var.vname\ndims = var.dims[:]\ndims.remove(None)\ns = str(vname)\nif len(dims) > 0:\n s += '[' + ']['.join(map(str, dims)) + ']'\nreturn s",
"indent = self.indent\nextra_indent = ' '\ns = '\... | <|body_start_0|>
self.vars = vars
self.annot_body_code = annot_body_code
self.indent = indent
self.language = 'c'
pass
<|end_body_0|>
<|body_start_1|>
vname = var.vname
dims = var.dims[:]
dims.remove(None)
s = str(vname)
if len(dims) > 0:
... | The code generator for the Blue Gene's memory alignment optimizer | CodeGen_C | [
"MIT",
"LicenseRef-scancode-warranty-disclaimer",
"LicenseRef-scancode-other-permissive"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CodeGen_C:
"""The code generator for the Blue Gene's memory alignment optimizer"""
def __init__(self, vars, annot_body_code, indent):
"""To instantiate a code generator instance"""
<|body_0|>
def __printAddress(self, var):
"""To Return the starting address locati... | stack_v2_sparse_classes_36k_train_011370 | 3,849 | permissive | [
{
"docstring": "To instantiate a code generator instance",
"name": "__init__",
"signature": "def __init__(self, vars, annot_body_code, indent)"
},
{
"docstring": "To Return the starting address location of the given variable (in C/C++)",
"name": "__printAddress",
"signature": "def __prin... | 3 | null | Implement the Python class `CodeGen_C` described below.
Class description:
The code generator for the Blue Gene's memory alignment optimizer
Method signatures and docstrings:
- def __init__(self, vars, annot_body_code, indent): To instantiate a code generator instance
- def __printAddress(self, var): To Return the st... | Implement the Python class `CodeGen_C` described below.
Class description:
The code generator for the Blue Gene's memory alignment optimizer
Method signatures and docstrings:
- def __init__(self, vars, annot_body_code, indent): To instantiate a code generator instance
- def __printAddress(self, var): To Return the st... | 934ba192301cb4e23d98b9f79e91799152bf76b1 | <|skeleton|>
class CodeGen_C:
"""The code generator for the Blue Gene's memory alignment optimizer"""
def __init__(self, vars, annot_body_code, indent):
"""To instantiate a code generator instance"""
<|body_0|>
def __printAddress(self, var):
"""To Return the starting address locati... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CodeGen_C:
"""The code generator for the Blue Gene's memory alignment optimizer"""
def __init__(self, vars, annot_body_code, indent):
"""To instantiate a code generator instance"""
self.vars = vars
self.annot_body_code = annot_body_code
self.indent = indent
self.la... | the_stack_v2_python_sparse | orio/module/align/codegen.py | phrb/orio_experiments | train | 1 |
5fc6c86715e2405076541db45616642c32dc24c5 | [
"self.n_iter = 0\nself.n_steps = n_steps\nself.walkers = np.zeros((2, n_walkers), int)\nself.n_walkers = n_walkers",
"while self.n_iter < self.n_steps:\n x = ran.randint(low=-1, high=2, size=self.n_walkers)\n y = ran.randint(low=-1, high=2, size=self.n_walkers)\n self.walkers[0] += x\n self.walkers[1]... | <|body_start_0|>
self.n_iter = 0
self.n_steps = n_steps
self.walkers = np.zeros((2, n_walkers), int)
self.n_walkers = n_walkers
<|end_body_0|>
<|body_start_1|>
while self.n_iter < self.n_steps:
x = ran.randint(low=-1, high=2, size=self.n_walkers)
y = ran.... | class for random walk simulation parameters: self.n_iter - number of executed iterations self.n_steps: - number of steps to be performed self.walkers: - ndarray that holds x & y coords of each walker self.walkers[0] are x coords. self.walkers[1] are y coords. | random_walk | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class random_walk:
"""class for random walk simulation parameters: self.n_iter - number of executed iterations self.n_steps: - number of steps to be performed self.walkers: - ndarray that holds x & y coords of each walker self.walkers[0] are x coords. self.walkers[1] are y coords."""
def __init__(... | stack_v2_sparse_classes_36k_train_011371 | 1,945 | no_license | [
{
"docstring": "initialize walkers to origin",
"name": "__init__",
"signature": "def __init__(self, n_walkers, n_steps)"
},
{
"docstring": "update the random walk and call plot method each time",
"name": "iterate",
"signature": "def iterate(self)"
},
{
"docstring": "plots the wal... | 3 | null | Implement the Python class `random_walk` described below.
Class description:
class for random walk simulation parameters: self.n_iter - number of executed iterations self.n_steps: - number of steps to be performed self.walkers: - ndarray that holds x & y coords of each walker self.walkers[0] are x coords. self.walkers... | Implement the Python class `random_walk` described below.
Class description:
class for random walk simulation parameters: self.n_iter - number of executed iterations self.n_steps: - number of steps to be performed self.walkers: - ndarray that holds x & y coords of each walker self.walkers[0] are x coords. self.walkers... | 516b74c57a1c7db7fb5097e833582744c92adf62 | <|skeleton|>
class random_walk:
"""class for random walk simulation parameters: self.n_iter - number of executed iterations self.n_steps: - number of steps to be performed self.walkers: - ndarray that holds x & y coords of each walker self.walkers[0] are x coords. self.walkers[1] are y coords."""
def __init__(... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class random_walk:
"""class for random walk simulation parameters: self.n_iter - number of executed iterations self.n_steps: - number of steps to be performed self.walkers: - ndarray that holds x & y coords of each walker self.walkers[0] are x coords. self.walkers[1] are y coords."""
def __init__(self, n_walke... | the_stack_v2_python_sparse | PythonProgramming/Homework 08/wood_10.3.py | Computational-Physics-Research/Computational-Physics-1 | train | 0 |
72e220dba94ce928aff89c419162c71cc19d3402 | [
"def dfs(node, last_val, tmp_len):\n if node is None:\n return\n if node.val == last_val + 1:\n tmp_len += 1\n max_len[0] = max(max_len[0], tmp_len)\n else:\n tmp_len = 1\n for child in [node.left, node.right]:\n dfs(child, node.val, tmp_len)\nif root is None:\n ret... | <|body_start_0|>
def dfs(node, last_val, tmp_len):
if node is None:
return
if node.val == last_val + 1:
tmp_len += 1
max_len[0] = max(max_len[0], tmp_len)
else:
tmp_len = 1
for child in [node.left, no... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def longestConsecutive(self, root):
""":type root: TreeNode :rtype: int"""
<|body_0|>
def longestConsecutive(self, root):
""":type root: TreeNode :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
def dfs(node, last_val, tmp_len):... | stack_v2_sparse_classes_36k_train_011372 | 2,202 | no_license | [
{
"docstring": ":type root: TreeNode :rtype: int",
"name": "longestConsecutive",
"signature": "def longestConsecutive(self, root)"
},
{
"docstring": ":type root: TreeNode :rtype: int",
"name": "longestConsecutive",
"signature": "def longestConsecutive(self, root)"
}
] | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def longestConsecutive(self, root): :type root: TreeNode :rtype: int
- def longestConsecutive(self, root): :type root: TreeNode :rtype: int | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def longestConsecutive(self, root): :type root: TreeNode :rtype: int
- def longestConsecutive(self, root): :type root: TreeNode :rtype: int
<|skeleton|>
class Solution:
def... | 18ed31a3edf20a3e5a0b7a0b56acca5b98939693 | <|skeleton|>
class Solution:
def longestConsecutive(self, root):
""":type root: TreeNode :rtype: int"""
<|body_0|>
def longestConsecutive(self, root):
""":type root: TreeNode :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def longestConsecutive(self, root):
""":type root: TreeNode :rtype: int"""
def dfs(node, last_val, tmp_len):
if node is None:
return
if node.val == last_val + 1:
tmp_len += 1
max_len[0] = max(max_len[0], tmp_len)... | the_stack_v2_python_sparse | exercises/binary-tree/binary_tree_longest_consective_sequence.py | nahgnaw/data-structure | train | 0 | |
4e9d8a0b09eba4d2e126876ae1307596d589a959 | [
"if not self._root:\n self._root = self._Node(key)\nelse:\n node = self._root\n parent = None\n while node:\n parent = node\n if key == node.key:\n break\n node = node.left if key < node.key else node.right\n if key == parent.key:\n parent._count += 1\n elif ... | <|body_start_0|>
if not self._root:
self._root = self._Node(key)
else:
node = self._root
parent = None
while node:
parent = node
if key == node.key:
break
node = node.left if key < node.ke... | Represents a Binary Search Tree with duplicate keys. | BinarySearchTreeDupKeys | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BinarySearchTreeDupKeys:
"""Represents a Binary Search Tree with duplicate keys."""
def insert(self, key):
"""Inserts ``key`` in the tree."""
<|body_0|>
def delete(self, key, delete_all=False):
"""Deletes key ``key`` from the tree. :param delete_all: If true, del... | stack_v2_sparse_classes_36k_train_011373 | 1,832 | permissive | [
{
"docstring": "Inserts ``key`` in the tree.",
"name": "insert",
"signature": "def insert(self, key)"
},
{
"docstring": "Deletes key ``key`` from the tree. :param delete_all: If true, deletes all the duplicate keys. Else deletes only a single occurrence of the key.",
"name": "delete",
"s... | 2 | stack_v2_sparse_classes_30k_train_018110 | Implement the Python class `BinarySearchTreeDupKeys` described below.
Class description:
Represents a Binary Search Tree with duplicate keys.
Method signatures and docstrings:
- def insert(self, key): Inserts ``key`` in the tree.
- def delete(self, key, delete_all=False): Deletes key ``key`` from the tree. :param del... | Implement the Python class `BinarySearchTreeDupKeys` described below.
Class description:
Represents a Binary Search Tree with duplicate keys.
Method signatures and docstrings:
- def insert(self, key): Inserts ``key`` in the tree.
- def delete(self, key, delete_all=False): Deletes key ``key`` from the tree. :param del... | 7e2e024cc55d7eb2ee2d205c912e184f1d32cfdf | <|skeleton|>
class BinarySearchTreeDupKeys:
"""Represents a Binary Search Tree with duplicate keys."""
def insert(self, key):
"""Inserts ``key`` in the tree."""
<|body_0|>
def delete(self, key, delete_all=False):
"""Deletes key ``key`` from the tree. :param delete_all: If true, del... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class BinarySearchTreeDupKeys:
"""Represents a Binary Search Tree with duplicate keys."""
def insert(self, key):
"""Inserts ``key`` in the tree."""
if not self._root:
self._root = self._Node(key)
else:
node = self._root
parent = None
while... | the_stack_v2_python_sparse | ds/tree/binary_search_tree_with_dup.py | farnasirim/zahlen | train | 0 |
a07e42c6c0007850394658455f3565be67e0fe20 | [
"creator = kwargs.pop('creator', None)\nrecipients = kwargs.pop('recipients', None)\nalias = super().create(*args, **kwargs)\nif creator:\n alias.post_create(creator)\nif recipients:\n alias.set_recipients(recipients)\nreturn alias",
"alias = self.get_queryset().filter(address=address, internal=False)\nif a... | <|body_start_0|>
creator = kwargs.pop('creator', None)
recipients = kwargs.pop('recipients', None)
alias = super().create(*args, **kwargs)
if creator:
alias.post_create(creator)
if recipients:
alias.set_recipients(recipients)
return alias
<|end_bod... | Custom manager for Alias. | AliasManager | [
"ISC"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AliasManager:
"""Custom manager for Alias."""
def create(self, *args, **kwargs):
"""Custom create method."""
<|body_0|>
def modify_or_create(self, address, recipients, creator, domain):
"""Add recipient if the alias already exists or create it."""
<|body_... | stack_v2_sparse_classes_36k_train_011374 | 10,712 | permissive | [
{
"docstring": "Custom create method.",
"name": "create",
"signature": "def create(self, *args, **kwargs)"
},
{
"docstring": "Add recipient if the alias already exists or create it.",
"name": "modify_or_create",
"signature": "def modify_or_create(self, address, recipients, creator, domai... | 2 | null | Implement the Python class `AliasManager` described below.
Class description:
Custom manager for Alias.
Method signatures and docstrings:
- def create(self, *args, **kwargs): Custom create method.
- def modify_or_create(self, address, recipients, creator, domain): Add recipient if the alias already exists or create i... | Implement the Python class `AliasManager` described below.
Class description:
Custom manager for Alias.
Method signatures and docstrings:
- def create(self, *args, **kwargs): Custom create method.
- def modify_or_create(self, address, recipients, creator, domain): Add recipient if the alias already exists or create i... | df699aab0799ec1725b6b89be38e56285821c889 | <|skeleton|>
class AliasManager:
"""Custom manager for Alias."""
def create(self, *args, **kwargs):
"""Custom create method."""
<|body_0|>
def modify_or_create(self, address, recipients, creator, domain):
"""Add recipient if the alias already exists or create it."""
<|body_... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AliasManager:
"""Custom manager for Alias."""
def create(self, *args, **kwargs):
"""Custom create method."""
creator = kwargs.pop('creator', None)
recipients = kwargs.pop('recipients', None)
alias = super().create(*args, **kwargs)
if creator:
alias.post... | the_stack_v2_python_sparse | modoboa/admin/models/alias.py | modoboa/modoboa | train | 2,201 |
3397315fe61aa64f683b1822e89cca8fcc20d0e5 | [
"value = super(TaggitSelect2, self).value_from_datadict(data, files, name)\nif ',' not in value:\n value = '%s,' % value\nreturn value",
"selected_choices_arg = 1 if VERSION < (1, 10) else 0\nselected_choices = args[selected_choices_arg]\nif isinstance(selected_choices, six.text_type):\n choices = [c.strip(... | <|body_start_0|>
value = super(TaggitSelect2, self).value_from_datadict(data, files, name)
if ',' not in value:
value = '%s,' % value
return value
<|end_body_0|>
<|body_start_1|>
selected_choices_arg = 1 if VERSION < (1, 10) else 0
selected_choices = args[selected_ch... | Select2 tag widget for taggit's TagField. | TaggitSelect2 | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TaggitSelect2:
"""Select2 tag widget for taggit's TagField."""
def value_from_datadict(self, data, files, name):
"""Handle multi-word tag. Insure there's a comma when there's only a single multi-word tag, or tag "Multi word" would end up as "Multi" and "word"."""
<|body_0|>
... | stack_v2_sparse_classes_36k_train_011375 | 1,412 | permissive | [
{
"docstring": "Handle multi-word tag. Insure there's a comma when there's only a single multi-word tag, or tag \"Multi word\" would end up as \"Multi\" and \"word\".",
"name": "value_from_datadict",
"signature": "def value_from_datadict(self, data, files, name)"
},
{
"docstring": "Render only s... | 2 | null | Implement the Python class `TaggitSelect2` described below.
Class description:
Select2 tag widget for taggit's TagField.
Method signatures and docstrings:
- def value_from_datadict(self, data, files, name): Handle multi-word tag. Insure there's a comma when there's only a single multi-word tag, or tag "Multi word" wo... | Implement the Python class `TaggitSelect2` described below.
Class description:
Select2 tag widget for taggit's TagField.
Method signatures and docstrings:
- def value_from_datadict(self, data, files, name): Handle multi-word tag. Insure there's a comma when there's only a single multi-word tag, or tag "Multi word" wo... | 30121cb565fbc1b1dc844378a68cb38ca8bbf3cf | <|skeleton|>
class TaggitSelect2:
"""Select2 tag widget for taggit's TagField."""
def value_from_datadict(self, data, files, name):
"""Handle multi-word tag. Insure there's a comma when there's only a single multi-word tag, or tag "Multi word" would end up as "Multi" and "word"."""
<|body_0|>
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TaggitSelect2:
"""Select2 tag widget for taggit's TagField."""
def value_from_datadict(self, data, files, name):
"""Handle multi-word tag. Insure there's a comma when there's only a single multi-word tag, or tag "Multi word" would end up as "Multi" and "word"."""
value = super(TaggitSelec... | the_stack_v2_python_sparse | annexes/django-autocomplete-light/src/dal_select2_taggit/widgets.py | pvergain/django-test-autocomplete | train | 1 |
2edc20ef8018c6ad89c72a6a2684da90895a7860 | [
"def post_order(root):\n return post_order(root.left) + post_order(root.right) + [root.val] if root else []\nreturn ' '.join(map(str, post_order(root)))",
"post_order = [int(each) for each in data.split(' ') if data]\nprint(post_order)\n\ndef helper(lower=float('-inf'), upper=float('inf')):\n if not post_or... | <|body_start_0|>
def post_order(root):
return post_order(root.left) + post_order(root.right) + [root.val] if root else []
return ' '.join(map(str, post_order(root)))
<|end_body_0|>
<|body_start_1|>
post_order = [int(each) for each in data.split(' ') if data]
print(post_order... | Codec | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
<|body_0|>
def deserialize(self, data):
"""Decodes your encoded data to tree. :type data: str :rtype: TreeNode"""
<|body_1|>
<|end_skeleton|>
<|body_... | stack_v2_sparse_classes_36k_train_011376 | 2,184 | 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_019547 | 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:... | 80cca595dc688ca67c1ebb45b339e724ec09c374 | <|skeleton|>
class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
<|body_0|>
def deserialize(self, data):
"""Decodes your encoded data to tree. :type data: str :rtype: TreeNode"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
def post_order(root):
return post_order(root.left) + post_order(root.right) + [root.val] if root else []
return ' '.join(map(str, post_order(root)))
def deserial... | the_stack_v2_python_sparse | Companies/Amazon/449.py | Dinesh94Singh/PythonArchivedSolutions | train | 0 | |
a50f4271fc20e2a88e1e5e78c85e2d6ad27dae22 | [
"if server is None:\n self.server = 'https://archive.gemini.edu'\nelif not server.lower().startswith('http:') and (not server.lower().startswith('https:')):\n self.server = 'https://%s' % server\nelse:\n self.server = server",
"qa_parm = ''\neng_parm = ''\nqa_parameters = ('NotFail', 'AnyQA', 'Pass', 'Lu... | <|body_start_0|>
if server is None:
self.server = 'https://archive.gemini.edu'
elif not server.lower().startswith('http:') and (not server.lower().startswith('https:')):
self.server = 'https://%s' % server
else:
self.server = server
<|end_body_0|>
<|body_star... | URLHelper | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class URLHelper:
def __init__(self, server='https://archive.gemini.edu'):
"""Make a URL Helper for building URLs to the Gemini Archive REST service."""
<|body_0|>
def build_url(self, *args, **kwargs):
"""Build a URL with the given args and kwargs as the query parameters. P... | stack_v2_sparse_classes_36k_train_011377 | 3,666 | permissive | [
{
"docstring": "Make a URL Helper for building URLs to the Gemini Archive REST service.",
"name": "__init__",
"signature": "def __init__(self, server='https://archive.gemini.edu')"
},
{
"docstring": "Build a URL with the given args and kwargs as the query parameters. Parameters ---------- args :... | 2 | null | Implement the Python class `URLHelper` described below.
Class description:
Implement the URLHelper class.
Method signatures and docstrings:
- def __init__(self, server='https://archive.gemini.edu'): Make a URL Helper for building URLs to the Gemini Archive REST service.
- def build_url(self, *args, **kwargs): Build a... | Implement the Python class `URLHelper` described below.
Class description:
Implement the URLHelper class.
Method signatures and docstrings:
- def __init__(self, server='https://archive.gemini.edu'): Make a URL Helper for building URLs to the Gemini Archive REST service.
- def build_url(self, *args, **kwargs): Build a... | 51316d7417d7daf01a8b29d1df99037b9227c2bc | <|skeleton|>
class URLHelper:
def __init__(self, server='https://archive.gemini.edu'):
"""Make a URL Helper for building URLs to the Gemini Archive REST service."""
<|body_0|>
def build_url(self, *args, **kwargs):
"""Build a URL with the given args and kwargs as the query parameters. P... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class URLHelper:
def __init__(self, server='https://archive.gemini.edu'):
"""Make a URL Helper for building URLs to the Gemini Archive REST service."""
if server is None:
self.server = 'https://archive.gemini.edu'
elif not server.lower().startswith('http:') and (not server.lower(... | the_stack_v2_python_sparse | astroquery/gemini/urlhelper.py | astropy/astroquery | train | 636 | |
a55dc6594227a004dd2dff0d2226e071150cc0fe | [
"print('Loading model')\npath = Models.modelPath('stackexchange')\ndbfile = os.path.join(path, 'questions.db')\ndb = sqlite3.connect(dbfile)\nembeddings = Embeddings()\nembeddings.load(path)\nreturn (db, embeddings)",
"db, embeddings = StackExchange.load()\ncur = db.cursor()\nmrr = []\nwith open(Models.testPath('... | <|body_start_0|>
print('Loading model')
path = Models.modelPath('stackexchange')
dbfile = os.path.join(path, 'questions.db')
db = sqlite3.connect(dbfile)
embeddings = Embeddings()
embeddings.load(path)
return (db, embeddings)
<|end_body_0|>
<|body_start_1|>
... | StackExchange query-answer dataset. | StackExchange | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class StackExchange:
"""StackExchange query-answer dataset."""
def load():
"""Loads a questions database and pre-trained embeddings model Returns: (db, embeddings)"""
<|body_0|>
def run(args):
"""Evaluates a pre-trained model against the StackExchange query-answer data... | stack_v2_sparse_classes_36k_train_011378 | 6,859 | permissive | [
{
"docstring": "Loads a questions database and pre-trained embeddings model Returns: (db, embeddings)",
"name": "load",
"signature": "def load()"
},
{
"docstring": "Evaluates a pre-trained model against the StackExchange query-answer dataset. Args: args: command line arguments",
"name": "run... | 2 | stack_v2_sparse_classes_30k_train_012541 | Implement the Python class `StackExchange` described below.
Class description:
StackExchange query-answer dataset.
Method signatures and docstrings:
- def load(): Loads a questions database and pre-trained embeddings model Returns: (db, embeddings)
- def run(args): Evaluates a pre-trained model against the StackExcha... | Implement the Python class `StackExchange` described below.
Class description:
StackExchange query-answer dataset.
Method signatures and docstrings:
- def load(): Loads a questions database and pre-trained embeddings model Returns: (db, embeddings)
- def run(args): Evaluates a pre-trained model against the StackExcha... | c1fde2fcb3cf830247131385ec5340e6a148e70a | <|skeleton|>
class StackExchange:
"""StackExchange query-answer dataset."""
def load():
"""Loads a questions database and pre-trained embeddings model Returns: (db, embeddings)"""
<|body_0|>
def run(args):
"""Evaluates a pre-trained model against the StackExchange query-answer data... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class StackExchange:
"""StackExchange query-answer dataset."""
def load():
"""Loads a questions database and pre-trained embeddings model Returns: (db, embeddings)"""
print('Loading model')
path = Models.modelPath('stackexchange')
dbfile = os.path.join(path, 'questions.db')
... | the_stack_v2_python_sparse | src/python/codequestion/evaluate.py | spreck/codequestion | train | 0 |
ba4a872bb84d469d6ab3cb06db8f31c7bd36808c | [
"_action_class = City()\nif 'ibi_cities' in params and 'ibi_districts' in params:\n _city = params['ibi_cities'].lower()\n _district = params['ibi_districts']\n return getattr(_action_class, _city)(_district)\nelse:\n return getattr(_action_class, 'guaiba')('centro')",
"_address = Address()\napp.logge... | <|body_start_0|>
_action_class = City()
if 'ibi_cities' in params and 'ibi_districts' in params:
_city = params['ibi_cities'].lower()
_district = params['ibi_districts']
return getattr(_action_class, _city)(_district)
else:
return getattr(_action_c... | Handle the intents resposes by action names | Actions | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Actions:
"""Handle the intents resposes by action names"""
def ask_cells_cities(self, params, outputContexts, IntentRequest):
"""Return the list of Cell addresses by city and district"""
<|body_0|>
def ask_cells_cities_select_address(self, params, outputContexts, IntentR... | stack_v2_sparse_classes_36k_train_011379 | 1,284 | no_license | [
{
"docstring": "Return the list of Cell addresses by city and district",
"name": "ask_cells_cities",
"signature": "def ask_cells_cities(self, params, outputContexts, IntentRequest)"
},
{
"docstring": "Return the map link for the selected address",
"name": "ask_cells_cities_select_address",
... | 2 | stack_v2_sparse_classes_30k_train_008782 | Implement the Python class `Actions` described below.
Class description:
Handle the intents resposes by action names
Method signatures and docstrings:
- def ask_cells_cities(self, params, outputContexts, IntentRequest): Return the list of Cell addresses by city and district
- def ask_cells_cities_select_address(self,... | Implement the Python class `Actions` described below.
Class description:
Handle the intents resposes by action names
Method signatures and docstrings:
- def ask_cells_cities(self, params, outputContexts, IntentRequest): Return the list of Cell addresses by city and district
- def ask_cells_cities_select_address(self,... | 29da21469c8e78b3374f5e5237067a97107e02d2 | <|skeleton|>
class Actions:
"""Handle the intents resposes by action names"""
def ask_cells_cities(self, params, outputContexts, IntentRequest):
"""Return the list of Cell addresses by city and district"""
<|body_0|>
def ask_cells_cities_select_address(self, params, outputContexts, IntentR... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Actions:
"""Handle the intents resposes by action names"""
def ask_cells_cities(self, params, outputContexts, IntentRequest):
"""Return the list of Cell addresses by city and district"""
_action_class = City()
if 'ibi_cities' in params and 'ibi_districts' in params:
_c... | the_stack_v2_python_sparse | actions/actions.py | douglasmoraisdev/ibi_chatbot | train | 0 |
cd6e3a9d90c76280ad510d9320df2e8d4bb86b88 | [
"self._process = None\nself._nm = PortScanner()\nreturn",
"if self._process is not None:\n try:\n if self._process.is_alive():\n self._process.terminate()\n except AssertionError:\n pass\nself._process = None\nreturn",
"if sys.version_info[0] == 2:\n assert type(hosts) in (str,... | <|body_start_0|>
self._process = None
self._nm = PortScanner()
return
<|end_body_0|>
<|body_start_1|>
if self._process is not None:
try:
if self._process.is_alive():
self._process.terminate()
except AssertionError:
... | PortScannerAsync allows to use nmap from python asynchronously for each host scanned, callback is called with scan result for the host | PortScannerAsync | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PortScannerAsync:
"""PortScannerAsync allows to use nmap from python asynchronously for each host scanned, callback is called with scan result for the host"""
def __init__(self):
"""Initialize the module * detects nmap on the system and nmap version * may raise PortScannerError excep... | stack_v2_sparse_classes_36k_train_011380 | 40,862 | permissive | [
{
"docstring": "Initialize the module * detects nmap on the system and nmap version * may raise PortScannerError exception if nmap is not found in the path",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Cleanup when deleted",
"name": "__del__",
"signature": "d... | 6 | null | Implement the Python class `PortScannerAsync` described below.
Class description:
PortScannerAsync allows to use nmap from python asynchronously for each host scanned, callback is called with scan result for the host
Method signatures and docstrings:
- def __init__(self): Initialize the module * detects nmap on the s... | Implement the Python class `PortScannerAsync` described below.
Class description:
PortScannerAsync allows to use nmap from python asynchronously for each host scanned, callback is called with scan result for the host
Method signatures and docstrings:
- def __init__(self): Initialize the module * detects nmap on the s... | fadb1136b8896fe2a0f7783627bda867d5e6fd98 | <|skeleton|>
class PortScannerAsync:
"""PortScannerAsync allows to use nmap from python asynchronously for each host scanned, callback is called with scan result for the host"""
def __init__(self):
"""Initialize the module * detects nmap on the system and nmap version * may raise PortScannerError excep... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PortScannerAsync:
"""PortScannerAsync allows to use nmap from python asynchronously for each host scanned, callback is called with scan result for the host"""
def __init__(self):
"""Initialize the module * detects nmap on the system and nmap version * may raise PortScannerError exception if nmap ... | the_stack_v2_python_sparse | fuxi/common/libs/nmap.py | Solotov/fuxi | train | 0 |
0e691ac7febb18c3510ed79ef05ee2592ef4e926 | [
"super(CBHG, self).__init__()\nself.hidden_size = hidden_size\nself.projection_size = projection_size\nself.convbank_list = nn.ModuleList()\nself.convbank_list.append(nn.Conv1d(in_channels=projection_size, out_channels=hidden_size, kernel_size=1, padding=int(np.floor(1 / 2))))\nfor i in range(2, K + 1):\n self.c... | <|body_start_0|>
super(CBHG, self).__init__()
self.hidden_size = hidden_size
self.projection_size = projection_size
self.convbank_list = nn.ModuleList()
self.convbank_list.append(nn.Conv1d(in_channels=projection_size, out_channels=hidden_size, kernel_size=1, padding=int(np.floor(... | CBHG Module. | CBHG | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CBHG:
"""CBHG Module."""
def __init__(self, hidden_size, K=16, projection_size=256, num_gru_layers=2, max_pool_kernel_size=2, is_post=False):
"""init."""
<|body_0|>
def _conv_fit_dim(self, x, kernel_size=3):
"""_conv_fit_dim."""
<|body_1|>
def forwar... | stack_v2_sparse_classes_36k_train_011381 | 17,934 | permissive | [
{
"docstring": "init.",
"name": "__init__",
"signature": "def __init__(self, hidden_size, K=16, projection_size=256, num_gru_layers=2, max_pool_kernel_size=2, is_post=False)"
},
{
"docstring": "_conv_fit_dim.",
"name": "_conv_fit_dim",
"signature": "def _conv_fit_dim(self, x, kernel_size... | 3 | null | Implement the Python class `CBHG` described below.
Class description:
CBHG Module.
Method signatures and docstrings:
- def __init__(self, hidden_size, K=16, projection_size=256, num_gru_layers=2, max_pool_kernel_size=2, is_post=False): init.
- def _conv_fit_dim(self, x, kernel_size=3): _conv_fit_dim.
- def forward(se... | Implement the Python class `CBHG` described below.
Class description:
CBHG Module.
Method signatures and docstrings:
- def __init__(self, hidden_size, K=16, projection_size=256, num_gru_layers=2, max_pool_kernel_size=2, is_post=False): init.
- def _conv_fit_dim(self, x, kernel_size=3): _conv_fit_dim.
- def forward(se... | 31d50b1ea1dea92f4182c5b2b6fe9fe4c981ae39 | <|skeleton|>
class CBHG:
"""CBHG Module."""
def __init__(self, hidden_size, K=16, projection_size=256, num_gru_layers=2, max_pool_kernel_size=2, is_post=False):
"""init."""
<|body_0|>
def _conv_fit_dim(self, x, kernel_size=3):
"""_conv_fit_dim."""
<|body_1|>
def forwar... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CBHG:
"""CBHG Module."""
def __init__(self, hidden_size, K=16, projection_size=256, num_gru_layers=2, max_pool_kernel_size=2, is_post=False):
"""init."""
super(CBHG, self).__init__()
self.hidden_size = hidden_size
self.projection_size = projection_size
self.convban... | the_stack_v2_python_sparse | SVS/model/layers/pretrain_module.py | SJTMusicTeam/SVS_system | train | 85 |
0c537649fc89f3a6db7c05b8ef1c75265fe7524d | [
"reflection_table = SumIntensityReducer.reduce_on_intensities(reflection_table)\nreflection_table = ScaleIntensityReducer.reduce_on_intensities(reflection_table)\nreturn reflection_table",
"reflection_table = SumIntensityReducer.apply_scaling_factors(reflection_table)\nreflection_table = ScaleIntensityReducer.app... | <|body_start_0|>
reflection_table = SumIntensityReducer.reduce_on_intensities(reflection_table)
reflection_table = ScaleIntensityReducer.reduce_on_intensities(reflection_table)
return reflection_table
<|end_body_0|>
<|body_start_1|>
reflection_table = SumIntensityReducer.apply_scaling_f... | Reduction methods for data with sum and scale intensities. Only reflections with valid values for all intensity types are retained. | SumAndScaleIntensityReducer | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SumAndScaleIntensityReducer:
"""Reduction methods for data with sum and scale intensities. Only reflections with valid values for all intensity types are retained."""
def reduce_on_intensities(reflection_table):
"""Select those with valid reflections for all values."""
<|body... | stack_v2_sparse_classes_36k_train_011382 | 38,270 | permissive | [
{
"docstring": "Select those with valid reflections for all values.",
"name": "reduce_on_intensities",
"signature": "def reduce_on_intensities(reflection_table)"
},
{
"docstring": "Apply corrections to the intensities and variances.",
"name": "apply_scaling_factors",
"signature": "def ap... | 2 | null | Implement the Python class `SumAndScaleIntensityReducer` described below.
Class description:
Reduction methods for data with sum and scale intensities. Only reflections with valid values for all intensity types are retained.
Method signatures and docstrings:
- def reduce_on_intensities(reflection_table): Select those... | Implement the Python class `SumAndScaleIntensityReducer` described below.
Class description:
Reduction methods for data with sum and scale intensities. Only reflections with valid values for all intensity types are retained.
Method signatures and docstrings:
- def reduce_on_intensities(reflection_table): Select those... | 88bf7f7c5ac44defc046ebf0719cde748092cfff | <|skeleton|>
class SumAndScaleIntensityReducer:
"""Reduction methods for data with sum and scale intensities. Only reflections with valid values for all intensity types are retained."""
def reduce_on_intensities(reflection_table):
"""Select those with valid reflections for all values."""
<|body... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SumAndScaleIntensityReducer:
"""Reduction methods for data with sum and scale intensities. Only reflections with valid values for all intensity types are retained."""
def reduce_on_intensities(reflection_table):
"""Select those with valid reflections for all values."""
reflection_table = ... | the_stack_v2_python_sparse | src/dials/util/filter_reflections.py | dials/dials | train | 71 |
dbf4c6bb8984a5fb75df28f25a3c3cdad35c4ce1 | [
"super(Res18, self).__init__(name=name)\nself._output_size = num_outputs\nself._conv_channels = 64\nself._conv_kernel_shape = [7, 7]\nself._conv_stride = 2\nself._pooling_kernel_shape = [2, 2]\nself._pooling_stride = 2\nself._resunit_channels = [64, 64, 128, 128, 256, 256, 512, 512]\nself._num_resunits = len(self._... | <|body_start_0|>
super(Res18, self).__init__(name=name)
self._output_size = num_outputs
self._conv_channels = 64
self._conv_kernel_shape = [7, 7]
self._conv_stride = 2
self._pooling_kernel_shape = [2, 2]
self._pooling_stride = 2
self._resunit_channels = [6... | Res18 | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Res18:
def __init__(self, num_outputs, name='res18', activation=tf.nn.relu, **extra_params):
"""Args: num_outputs (int): the number of outputs of the module. name (str): module name. activation (tf function): activation used for the internal layers. **extra_params: all the additional key... | stack_v2_sparse_classes_36k_train_011383 | 48,282 | permissive | [
{
"docstring": "Args: num_outputs (int): the number of outputs of the module. name (str): module name. activation (tf function): activation used for the internal layers. **extra_params: all the additional keyword arguments will be passed to all the snt.Conv2D and to the ResUnit layers.",
"name": "__init__",... | 2 | stack_v2_sparse_classes_30k_train_008104 | Implement the Python class `Res18` described below.
Class description:
Implement the Res18 class.
Method signatures and docstrings:
- def __init__(self, num_outputs, name='res18', activation=tf.nn.relu, **extra_params): Args: num_outputs (int): the number of outputs of the module. name (str): module name. activation ... | Implement the Python class `Res18` described below.
Class description:
Implement the Res18 class.
Method signatures and docstrings:
- def __init__(self, num_outputs, name='res18', activation=tf.nn.relu, **extra_params): Args: num_outputs (int): the number of outputs of the module. name (str): module name. activation ... | a10c33346803239db8a64c104db7f22ec4e05bef | <|skeleton|>
class Res18:
def __init__(self, num_outputs, name='res18', activation=tf.nn.relu, **extra_params):
"""Args: num_outputs (int): the number of outputs of the module. name (str): module name. activation (tf function): activation used for the internal layers. **extra_params: all the additional key... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Res18:
def __init__(self, num_outputs, name='res18', activation=tf.nn.relu, **extra_params):
"""Args: num_outputs (int): the number of outputs of the module. name (str): module name. activation (tf function): activation used for the internal layers. **extra_params: all the additional keyword arguments... | the_stack_v2_python_sparse | argo/core/utils/utils_modules.py | ricvo/argo | train | 0 | |
fc7185e1a1f2145302ffafc4bffe2b5cb5a15e44 | [
"if not self.base_directory.exists():\n raise TestConnectionError(f'Path: {self.base_directory.resolve()} does not exist.')\nif self.assets and test_assets:\n for asset in self.assets:\n asset.test_connection()",
"if kwargs:\n raise TypeError(f'_build_data_connector() got unexpected keyword argume... | <|body_start_0|>
if not self.base_directory.exists():
raise TestConnectionError(f'Path: {self.base_directory.resolve()} does not exist.')
if self.assets and test_assets:
for asset in self.assets:
asset.test_connection()
<|end_body_0|>
<|body_start_1|>
if ... | Pandas based Datasource for filesystem based data assets. | PandasFilesystemDatasource | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PandasFilesystemDatasource:
"""Pandas based Datasource for filesystem based data assets."""
def test_connection(self, test_assets: bool=True) -> None:
"""Test the connection for the PandasFilesystemDatasource. Args: test_assets: If assets have been passed to the PandasFilesystemDatas... | stack_v2_sparse_classes_36k_train_011384 | 3,249 | permissive | [
{
"docstring": "Test the connection for the PandasFilesystemDatasource. Args: test_assets: If assets have been passed to the PandasFilesystemDatasource, whether to test them as well. Raises: TestConnectionError: If the connection test fails.",
"name": "test_connection",
"signature": "def test_connection... | 2 | stack_v2_sparse_classes_30k_train_012021 | Implement the Python class `PandasFilesystemDatasource` described below.
Class description:
Pandas based Datasource for filesystem based data assets.
Method signatures and docstrings:
- def test_connection(self, test_assets: bool=True) -> None: Test the connection for the PandasFilesystemDatasource. Args: test_assets... | Implement the Python class `PandasFilesystemDatasource` described below.
Class description:
Pandas based Datasource for filesystem based data assets.
Method signatures and docstrings:
- def test_connection(self, test_assets: bool=True) -> None: Test the connection for the PandasFilesystemDatasource. Args: test_assets... | b0290e2fd2aa05aec6d7d8871b91cb4478e9501d | <|skeleton|>
class PandasFilesystemDatasource:
"""Pandas based Datasource for filesystem based data assets."""
def test_connection(self, test_assets: bool=True) -> None:
"""Test the connection for the PandasFilesystemDatasource. Args: test_assets: If assets have been passed to the PandasFilesystemDatas... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PandasFilesystemDatasource:
"""Pandas based Datasource for filesystem based data assets."""
def test_connection(self, test_assets: bool=True) -> None:
"""Test the connection for the PandasFilesystemDatasource. Args: test_assets: If assets have been passed to the PandasFilesystemDatasource, whethe... | the_stack_v2_python_sparse | great_expectations/datasource/fluent/pandas_filesystem_datasource.py | great-expectations/great_expectations | train | 8,931 |
0769599800cc91095a265ff51bddb05899892966 | [
"ans = []\n\ndef dfs(root):\n if not root:\n ans.append('#')\n return\n ans.append(str(root.val))\n dfs(root.left)\n dfs(root.right)\ndfs(root)\nreturn ' '.join(ans)",
"def convert_str_to_iterator(source, seperator):\n start, seperator_size, index = (0, len(seperator), 0)\n while T... | <|body_start_0|>
ans = []
def dfs(root):
if not root:
ans.append('#')
return
ans.append(str(root.val))
dfs(root.left)
dfs(root.right)
dfs(root)
return ' '.join(ans)
<|end_body_0|>
<|body_start_1|>
d... | Codec | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
<|body_0|>
def deserialize(self, data):
"""Decodes your encoded data to tree. :type data: str :rtype: TreeNode"""
<|body_1|>
<|end_skeleton|>
<|body_... | stack_v2_sparse_classes_36k_train_011385 | 1,573 | 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_010376 | 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:... | d66be3a8f002875097754df6138c704e28b79810 | <|skeleton|>
class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
<|body_0|>
def deserialize(self, data):
"""Decodes your encoded data to tree. :type data: str :rtype: TreeNode"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
ans = []
def dfs(root):
if not root:
ans.append('#')
return
ans.append(str(root.val))
dfs(root.left)
... | the_stack_v2_python_sparse | 449_Serialize_and_Deserialize_BST/serialize_and_deserialize_bst.py | Brady31027/leetcode | train | 4 | |
5f3f187ddf31b186bbf5170a857490adf1d2370a | [
"bootstrap_exp_count = 1000\nresample_count = 1000\nbound_limits = list()\nfor N in xrange(2, self.N + 1):\n samples = self.sample_generator.generate_samples(N, self.T)\n for T in range(self.T):\n m_l, m_u = self.compute_boostrap_experiment(N, samples[:, T], resample_count, bootstrap_exp_count)\n ... | <|body_start_0|>
bootstrap_exp_count = 1000
resample_count = 1000
bound_limits = list()
for N in xrange(2, self.N + 1):
samples = self.sample_generator.generate_samples(N, self.T)
for T in range(self.T):
m_l, m_u = self.compute_boostrap_experiment(... | Class for Bootstrap Bounds The Confidence is set to 95* for this bound. Might mae it configurable in the future. | Bootstrap | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Bootstrap:
"""Class for Bootstrap Bounds The Confidence is set to 95* for this bound. Might mae it configurable in the future."""
def compute_mean(self):
"""Computes mean with the DELTA method of Bootstrapping. For Bootstrap T(Trails) is equivalent to number Bootstrap sampling experi... | stack_v2_sparse_classes_36k_train_011386 | 2,348 | no_license | [
{
"docstring": "Computes mean with the DELTA method of Bootstrapping. For Bootstrap T(Trails) is equivalent to number Bootstrap sampling experiment.",
"name": "compute_mean",
"signature": "def compute_mean(self)"
},
{
"docstring": "Performs one Bootstrap experiment. :param N: Number of samples, ... | 2 | stack_v2_sparse_classes_30k_train_002345 | Implement the Python class `Bootstrap` described below.
Class description:
Class for Bootstrap Bounds The Confidence is set to 95* for this bound. Might mae it configurable in the future.
Method signatures and docstrings:
- def compute_mean(self): Computes mean with the DELTA method of Bootstrapping. For Bootstrap T(... | Implement the Python class `Bootstrap` described below.
Class description:
Class for Bootstrap Bounds The Confidence is set to 95* for this bound. Might mae it configurable in the future.
Method signatures and docstrings:
- def compute_mean(self): Computes mean with the DELTA method of Bootstrapping. For Bootstrap T(... | 582db28c4f3251486f940f4054418cc7290f4d45 | <|skeleton|>
class Bootstrap:
"""Class for Bootstrap Bounds The Confidence is set to 95* for this bound. Might mae it configurable in the future."""
def compute_mean(self):
"""Computes mean with the DELTA method of Bootstrapping. For Bootstrap T(Trails) is equivalent to number Bootstrap sampling experi... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Bootstrap:
"""Class for Bootstrap Bounds The Confidence is set to 95* for this bound. Might mae it configurable in the future."""
def compute_mean(self):
"""Computes mean with the DELTA method of Bootstrapping. For Bootstrap T(Trails) is equivalent to number Bootstrap sampling experiment."""
... | the_stack_v2_python_sparse | src/main/algorithm/bootstrap.py | abhinavshaw1993/SampleBounds | train | 4 |
f5ef94c60d4a8e75ab09155905dd41f3d1ec9754 | [
"if _cfg.server_backend == 'cassandra':\n clear_graph()\nelse:\n Gremlin().gremlin_post('graph.truncateBackend();')\nInsertData(gremlin='gremlin_alg_03.txt').gremlin_graph()",
"body = {}\ncode, res = Algorithm().post_count_vertex(body, auth=auth)\nid = res['task_id']\nif id > 0:\n result = get_task_res(i... | <|body_start_0|>
if _cfg.server_backend == 'cassandra':
clear_graph()
else:
Gremlin().gremlin_post('graph.truncateBackend();')
InsertData(gremlin='gremlin_alg_03.txt').gremlin_graph()
<|end_body_0|>
<|body_start_1|>
body = {}
code, res = Algorithm().post_... | 接口count_vertex：统计顶点信息，包括图中顶点数量、各类型的顶点数量 | TestCountVertex | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestCountVertex:
"""接口count_vertex：统计顶点信息，包括图中顶点数量、各类型的顶点数量"""
def setup_class(self):
"""测试类开始"""
<|body_0|>
def test_count_vertex(self):
"""统计顶点信息接口 :return:"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
if _cfg.server_backend == 'cassandra':... | stack_v2_sparse_classes_36k_train_011387 | 1,518 | no_license | [
{
"docstring": "测试类开始",
"name": "setup_class",
"signature": "def setup_class(self)"
},
{
"docstring": "统计顶点信息接口 :return:",
"name": "test_count_vertex",
"signature": "def test_count_vertex(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_012708 | Implement the Python class `TestCountVertex` described below.
Class description:
接口count_vertex：统计顶点信息，包括图中顶点数量、各类型的顶点数量
Method signatures and docstrings:
- def setup_class(self): 测试类开始
- def test_count_vertex(self): 统计顶点信息接口 :return: | Implement the Python class `TestCountVertex` described below.
Class description:
接口count_vertex：统计顶点信息，包括图中顶点数量、各类型的顶点数量
Method signatures and docstrings:
- def setup_class(self): 测试类开始
- def test_count_vertex(self): 统计顶点信息接口 :return:
<|skeleton|>
class TestCountVertex:
"""接口count_vertex：统计顶点信息，包括图中顶点数量、各类型的顶点数量... | 89e5b34ab925bcc0bbc4ad63302e96c62a420399 | <|skeleton|>
class TestCountVertex:
"""接口count_vertex：统计顶点信息，包括图中顶点数量、各类型的顶点数量"""
def setup_class(self):
"""测试类开始"""
<|body_0|>
def test_count_vertex(self):
"""统计顶点信息接口 :return:"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TestCountVertex:
"""接口count_vertex：统计顶点信息，包括图中顶点数量、各类型的顶点数量"""
def setup_class(self):
"""测试类开始"""
if _cfg.server_backend == 'cassandra':
clear_graph()
else:
Gremlin().gremlin_post('graph.truncateBackend();')
InsertData(gremlin='gremlin_alg_03.txt').... | the_stack_v2_python_sparse | src/graph_function_test/server/algorithm_olap/test_countVertex.py | hugegraph/hugegraph-test | train | 1 |
cf65cddbaf6ce41b97ee54d624d8a2053aa1df90 | [
"self.pyr_scale = pyr_scale\nself.levels = levels\nself.winsize = winsize\nself.iterations = iterations\nself.poly_n = poly_n\nself.poly_sigma = poly_sigma\nself.motion_image = None\nself.magnitude_image = None\nself.direction_image = None",
"hsv = np.zeros((prev.shape[0], prev.shape[1], 3))\nhsv[..., 1] = 255\nf... | <|body_start_0|>
self.pyr_scale = pyr_scale
self.levels = levels
self.winsize = winsize
self.iterations = iterations
self.poly_n = poly_n
self.poly_sigma = poly_sigma
self.motion_image = None
self.magnitude_image = None
self.direction_image = None
... | Farneback | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Farneback:
def __init__(self, pyr_scale=0.5, levels=3, winsize=15, iterations=3, poly_n=1.2, poly_sigma=0):
"""Function definition +++++++++++++++++++ .. py:function:: __init__(pyr_scale=0.5, levels=3, winsize=15, iterations=3, poly_n=1.2, poly_sigma=0) Initializes the object that comput... | stack_v2_sparse_classes_36k_train_011388 | 3,952 | no_license | [
{
"docstring": "Function definition +++++++++++++++++++ .. py:function:: __init__(pyr_scale=0.5, levels=3, winsize=15, iterations=3, poly_n=1.2, poly_sigma=0) Initializes the object that computes a dense optical flow using the Gunnar Farneback’s algorithm. :param float pyr_scale: scaling factor between images i... | 2 | stack_v2_sparse_classes_30k_train_003880 | Implement the Python class `Farneback` described below.
Class description:
Implement the Farneback class.
Method signatures and docstrings:
- def __init__(self, pyr_scale=0.5, levels=3, winsize=15, iterations=3, poly_n=1.2, poly_sigma=0): Function definition +++++++++++++++++++ .. py:function:: __init__(pyr_scale=0.5... | Implement the Python class `Farneback` described below.
Class description:
Implement the Farneback class.
Method signatures and docstrings:
- def __init__(self, pyr_scale=0.5, levels=3, winsize=15, iterations=3, poly_n=1.2, poly_sigma=0): Function definition +++++++++++++++++++ .. py:function:: __init__(pyr_scale=0.5... | 90531055691a094dd271966b53c40b7a097df375 | <|skeleton|>
class Farneback:
def __init__(self, pyr_scale=0.5, levels=3, winsize=15, iterations=3, poly_n=1.2, poly_sigma=0):
"""Function definition +++++++++++++++++++ .. py:function:: __init__(pyr_scale=0.5, levels=3, winsize=15, iterations=3, poly_n=1.2, poly_sigma=0) Initializes the object that comput... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Farneback:
def __init__(self, pyr_scale=0.5, levels=3, winsize=15, iterations=3, poly_n=1.2, poly_sigma=0):
"""Function definition +++++++++++++++++++ .. py:function:: __init__(pyr_scale=0.5, levels=3, winsize=15, iterations=3, poly_n=1.2, poly_sigma=0) Initializes the object that computes a dense opt... | the_stack_v2_python_sparse | OpticalFlow/Dense/Farneback.py | kmakantasis/CV-Tools | train | 0 | |
750fb9e3e815e77fcb53e48ce80482bc61ee3088 | [
"try:\n time.sleep(5)\n tv_vips = driver.find_elements_by_id('com.chtwm.mall:id/tv_vip')\n for i in tv_vips:\n if i.text == vipnames:\n with allure.step('点击\"%s\"按钮' % vipnames):\n i.click()\n break\nexcept Exception as e:\n print('%s报错:' % vipnames, e)",
"n... | <|body_start_0|>
try:
time.sleep(5)
tv_vips = driver.find_elements_by_id('com.chtwm.mall:id/tv_vip')
for i in tv_vips:
if i.text == vipnames:
with allure.step('点击"%s"按钮' % vipnames):
i.click()
bre... | AllEquitys | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AllEquitys:
def EveryEquitys(driver, vipnames):
""":param vipnames: 传入权益内容名称 :return:"""
<|body_0|>
def EveryEquitys_CheckPoint(driver, vipnames):
""":param vipnames: 传入权益内容名称 :return: pytest结果"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
try:
... | stack_v2_sparse_classes_36k_train_011389 | 1,553 | no_license | [
{
"docstring": ":param vipnames: 传入权益内容名称 :return:",
"name": "EveryEquitys",
"signature": "def EveryEquitys(driver, vipnames)"
},
{
"docstring": ":param vipnames: 传入权益内容名称 :return: pytest结果",
"name": "EveryEquitys_CheckPoint",
"signature": "def EveryEquitys_CheckPoint(driver, vipnames)"
... | 2 | null | Implement the Python class `AllEquitys` described below.
Class description:
Implement the AllEquitys class.
Method signatures and docstrings:
- def EveryEquitys(driver, vipnames): :param vipnames: 传入权益内容名称 :return:
- def EveryEquitys_CheckPoint(driver, vipnames): :param vipnames: 传入权益内容名称 :return: pytest结果 | Implement the Python class `AllEquitys` described below.
Class description:
Implement the AllEquitys class.
Method signatures and docstrings:
- def EveryEquitys(driver, vipnames): :param vipnames: 传入权益内容名称 :return:
- def EveryEquitys_CheckPoint(driver, vipnames): :param vipnames: 传入权益内容名称 :return: pytest结果
<|skeleto... | 618a47ea572f8fccfbf10f5f50aff1dfffb7b0e3 | <|skeleton|>
class AllEquitys:
def EveryEquitys(driver, vipnames):
""":param vipnames: 传入权益内容名称 :return:"""
<|body_0|>
def EveryEquitys_CheckPoint(driver, vipnames):
""":param vipnames: 传入权益内容名称 :return: pytest结果"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AllEquitys:
def EveryEquitys(driver, vipnames):
""":param vipnames: 传入权益内容名称 :return:"""
try:
time.sleep(5)
tv_vips = driver.find_elements_by_id('com.chtwm.mall:id/tv_vip')
for i in tv_vips:
if i.text == vipnames:
with all... | the_stack_v2_python_sparse | Appium/HengTianCaiFu_Android/ReleasePage/Case013_AllEquitys.py | Fengyongming0311/TANUKI | train | 0 | |
0699626f2e02e98e80728c051a0a7a4d7c081d86 | [
"super().__init__()\nself.w1 = Parameter(shape=[out_channels, in_channels], dtype=dtype)\nself.w2 = Parameter(shape=[out_channels, in_channels], dtype=dtype)\nif fast_gelu:\n self.op = ops.dual_gemm_rcr_fast_gelu()\nelse:\n self.op = ops.dual_gemm_rcr_silu()",
"assert len(args) == 1\nx = args[0]\nreturn sel... | <|body_start_0|>
super().__init__()
self.w1 = Parameter(shape=[out_channels, in_channels], dtype=dtype)
self.w2 = Parameter(shape=[out_channels, in_channels], dtype=dtype)
if fast_gelu:
self.op = ops.dual_gemm_rcr_fast_gelu()
else:
self.op = ops.dual_gemm_... | DualGemm frontend | DualGemm | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DualGemm:
"""DualGemm frontend"""
def __init__(self, in_channels, out_channels, fast_gelu=True, dtype='float16'):
"""Initialize dual gemm module, create a tensor for weights"""
<|body_0|>
def forward(self, *args):
"""forward pass for calling attention op"""
... | stack_v2_sparse_classes_36k_train_011390 | 2,504 | permissive | [
{
"docstring": "Initialize dual gemm module, create a tensor for weights",
"name": "__init__",
"signature": "def __init__(self, in_channels, out_channels, fast_gelu=True, dtype='float16')"
},
{
"docstring": "forward pass for calling attention op",
"name": "forward",
"signature": "def for... | 2 | null | Implement the Python class `DualGemm` described below.
Class description:
DualGemm frontend
Method signatures and docstrings:
- def __init__(self, in_channels, out_channels, fast_gelu=True, dtype='float16'): Initialize dual gemm module, create a tensor for weights
- def forward(self, *args): forward pass for calling ... | Implement the Python class `DualGemm` described below.
Class description:
DualGemm frontend
Method signatures and docstrings:
- def __init__(self, in_channels, out_channels, fast_gelu=True, dtype='float16'): Initialize dual gemm module, create a tensor for weights
- def forward(self, *args): forward pass for calling ... | c60dc19788217556ba12ea378c02b9fd0aea9ffe | <|skeleton|>
class DualGemm:
"""DualGemm frontend"""
def __init__(self, in_channels, out_channels, fast_gelu=True, dtype='float16'):
"""Initialize dual gemm module, create a tensor for weights"""
<|body_0|>
def forward(self, *args):
"""forward pass for calling attention op"""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DualGemm:
"""DualGemm frontend"""
def __init__(self, in_channels, out_channels, fast_gelu=True, dtype='float16'):
"""Initialize dual gemm module, create a tensor for weights"""
super().__init__()
self.w1 = Parameter(shape=[out_channels, in_channels], dtype=dtype)
self.w2 =... | the_stack_v2_python_sparse | python/aitemplate/frontend/nn/dual_gemm.py | facebookincubator/AITemplate | train | 4,065 |
bf9e32eb8dcede249c2534219c0c72518eea1c2f | [
"Fruit.__init__(self)\nself._flower_duration = flower_duration\nself._max_relative_growth_rate = max_relative_growth_rate\nself._lost_time = lost_time\nself._max_age = max_age\nself._probability = probability\nself._max_absolute_growth_rate = max_absolute_growth_rate\nself._r = self._max_absolute_growth_rate / self... | <|body_start_0|>
Fruit.__init__(self)
self._flower_duration = flower_duration
self._max_relative_growth_rate = max_relative_growth_rate
self._lost_time = lost_time
self._max_age = max_age
self._probability = probability
self._max_absolute_growth_rate = max_absolut... | A specialised fruit class for apple trees This class inherits methods and attributes from :class:`~openalea.stocatree.fruit.Fruit` and specialises the :meth:`compute_mass` method. >>> fruit = AppleFruit(max_age=100., flower_duration=12.) >>> fruit.state 'flower' >>> fruit.age 0 >>> fruit.mass 0.0 >>> fruit._flower_dura... | AppleFruit | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AppleFruit:
"""A specialised fruit class for apple trees This class inherits methods and attributes from :class:`~openalea.stocatree.fruit.Fruit` and specialises the :meth:`compute_mass` method. >>> fruit = AppleFruit(max_age=100., flower_duration=12.) >>> fruit.state 'flower' >>> fruit.age 0 >>>... | stack_v2_sparse_classes_36k_train_011391 | 6,591 | no_license | [
{
"docstring": "**Construtor** Inherits :meth:`get_state`, :meth:`set_state` from :class:`Fruit` class. The method :meth:`compute_mass` is redefined. The following arguments may be provided and are specific to apple trees. There are mainly used to compute the mass of the fruit as a function of its age except fo... | 2 | stack_v2_sparse_classes_30k_train_001497 | Implement the Python class `AppleFruit` described below.
Class description:
A specialised fruit class for apple trees This class inherits methods and attributes from :class:`~openalea.stocatree.fruit.Fruit` and specialises the :meth:`compute_mass` method. >>> fruit = AppleFruit(max_age=100., flower_duration=12.) >>> f... | Implement the Python class `AppleFruit` described below.
Class description:
A specialised fruit class for apple trees This class inherits methods and attributes from :class:`~openalea.stocatree.fruit.Fruit` and specialises the :meth:`compute_mass` method. >>> fruit = AppleFruit(max_age=100., flower_duration=12.) >>> f... | 090370f08271455f6c1b89592a0b7eb18212a6c9 | <|skeleton|>
class AppleFruit:
"""A specialised fruit class for apple trees This class inherits methods and attributes from :class:`~openalea.stocatree.fruit.Fruit` and specialises the :meth:`compute_mass` method. >>> fruit = AppleFruit(max_age=100., flower_duration=12.) >>> fruit.state 'flower' >>> fruit.age 0 >>>... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AppleFruit:
"""A specialised fruit class for apple trees This class inherits methods and attributes from :class:`~openalea.stocatree.fruit.Fruit` and specialises the :meth:`compute_mass` method. >>> fruit = AppleFruit(max_age=100., flower_duration=12.) >>> fruit.state 'flower' >>> fruit.age 0 >>> fruit.mass 0... | the_stack_v2_python_sparse | src/openalea/stocatree/fruit.py | junqi108/MAppleT | train | 0 |
e3f2aa4ce039371e682022384151e6cd098e0813 | [
"try:\n json_data = json.loads(request.data.decode())\n firewallController.add_whitelist_url(json_data)\n return Response(status=202)\nexcept Exception as err:\n return Response(json.dumps(str(err)), status=500, mimetype='application/json')",
"try:\n json_data = json.dumps(firewallController.get_wh... | <|body_start_0|>
try:
json_data = json.loads(request.data.decode())
firewallController.add_whitelist_url(json_data)
return Response(status=202)
except Exception as err:
return Response(json.dumps(str(err)), status=500, mimetype='application/json')
<|end_bo... | Whitelist | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Whitelist:
def post(self):
"""Add an url to the whitelist"""
<|body_0|>
def get(self):
"""Get all the urls from the whitelist"""
<|body_1|>
def delete(self, id):
"""Remove an url from the whitelist"""
<|body_2|>
<|end_skeleton|>
<|body_... | stack_v2_sparse_classes_36k_train_011392 | 2,162 | no_license | [
{
"docstring": "Add an url to the whitelist",
"name": "post",
"signature": "def post(self)"
},
{
"docstring": "Get all the urls from the whitelist",
"name": "get",
"signature": "def get(self)"
},
{
"docstring": "Remove an url from the whitelist",
"name": "delete",
"signat... | 3 | null | Implement the Python class `Whitelist` described below.
Class description:
Implement the Whitelist class.
Method signatures and docstrings:
- def post(self): Add an url to the whitelist
- def get(self): Get all the urls from the whitelist
- def delete(self, id): Remove an url from the whitelist | Implement the Python class `Whitelist` described below.
Class description:
Implement the Whitelist class.
Method signatures and docstrings:
- def post(self): Add an url to the whitelist
- def get(self): Get all the urls from the whitelist
- def delete(self, id): Remove an url from the whitelist
<|skeleton|>
class Wh... | 6070e3cb6bf957e04f5d8267db11f3296410e18e | <|skeleton|>
class Whitelist:
def post(self):
"""Add an url to the whitelist"""
<|body_0|>
def get(self):
"""Get all the urls from the whitelist"""
<|body_1|>
def delete(self, id):
"""Remove an url from the whitelist"""
<|body_2|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Whitelist:
def post(self):
"""Add an url to the whitelist"""
try:
json_data = json.loads(request.data.decode())
firewallController.add_whitelist_url(json_data)
return Response(status=202)
except Exception as err:
return Response(json.dump... | the_stack_v2_python_sparse | configuration-agent/firewall/rest_api/resources/whitelist.py | ReliableLion/frog4-configurable-vnf | train | 0 | |
90e91b39492215f3cb8f4f64052bf392ef086be5 | [
"if not prices:\n return 0\ndp = [[0] * (2 + 1) for _ in range(len(prices))]\nfor k in range(1, 3):\n for i in range(1, len(prices)):\n temp = prices[i] - prices[0]\n for j in range(1, i):\n temp = max(temp, prices[i] - prices[j] + dp[j - 1][k - 1])\n dp[i][k] = max(dp[i - 1][k... | <|body_start_0|>
if not prices:
return 0
dp = [[0] * (2 + 1) for _ in range(len(prices))]
for k in range(1, 3):
for i in range(1, len(prices)):
temp = prices[i] - prices[0]
for j in range(1, i):
temp = max(temp, prices[i... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def maxProfit_1(self, prices: List[int]) -> int:
"""FIXME: 超时 动态规划，辅助数组： dp[i][1] 表示前 i 天内 1 笔交易的最大利润 dp[i][2] 表示前 i 天内 2 笔交易的最大利润 - 第 i 天无动作， 则 dp[i][2] = dp[i-1][2] - 第 i 天卖出，则必须在第 j 天买入，此时利润 prices[i] - prices[j] + dp[j - 1][1]"""
<|body_0|>
def maxProfit(self, ... | stack_v2_sparse_classes_36k_train_011393 | 3,489 | no_license | [
{
"docstring": "FIXME: 超时 动态规划，辅助数组： dp[i][1] 表示前 i 天内 1 笔交易的最大利润 dp[i][2] 表示前 i 天内 2 笔交易的最大利润 - 第 i 天无动作， 则 dp[i][2] = dp[i-1][2] - 第 i 天卖出，则必须在第 j 天买入，此时利润 prices[i] - prices[j] + dp[j - 1][1]",
"name": "maxProfit_1",
"signature": "def maxProfit_1(self, prices: List[int]) -> int"
},
{
"docstri... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def maxProfit_1(self, prices: List[int]) -> int: FIXME: 超时 动态规划，辅助数组： dp[i][1] 表示前 i 天内 1 笔交易的最大利润 dp[i][2] 表示前 i 天内 2 笔交易的最大利润 - 第 i 天无动作， 则 dp[i][2] = dp[i-1][2] - 第 i 天卖出，则必须在... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def maxProfit_1(self, prices: List[int]) -> int: FIXME: 超时 动态规划，辅助数组： dp[i][1] 表示前 i 天内 1 笔交易的最大利润 dp[i][2] 表示前 i 天内 2 笔交易的最大利润 - 第 i 天无动作， 则 dp[i][2] = dp[i-1][2] - 第 i 天卖出，则必须在... | 4732fb80710a08a715c3e7080c394f5298b8326d | <|skeleton|>
class Solution:
def maxProfit_1(self, prices: List[int]) -> int:
"""FIXME: 超时 动态规划，辅助数组： dp[i][1] 表示前 i 天内 1 笔交易的最大利润 dp[i][2] 表示前 i 天内 2 笔交易的最大利润 - 第 i 天无动作， 则 dp[i][2] = dp[i-1][2] - 第 i 天卖出，则必须在第 j 天买入，此时利润 prices[i] - prices[j] + dp[j - 1][1]"""
<|body_0|>
def maxProfit(self, ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def maxProfit_1(self, prices: List[int]) -> int:
"""FIXME: 超时 动态规划，辅助数组： dp[i][1] 表示前 i 天内 1 笔交易的最大利润 dp[i][2] 表示前 i 天内 2 笔交易的最大利润 - 第 i 天无动作， 则 dp[i][2] = dp[i-1][2] - 第 i 天卖出，则必须在第 j 天买入，此时利润 prices[i] - prices[j] + dp[j - 1][1]"""
if not prices:
return 0
dp = [... | the_stack_v2_python_sparse | .leetcode/123.买卖股票的最佳时机-iii.py | xiaoruijiang/algorithm | train | 0 | |
20280acddbb2d2e90906b0e7982b797cde17bf1b | [
"self.__object2MatrixFunction = object2MatrixFunction\nself.__distanceBetween2Objects = distanceBetween2Objects\nself.__embedding_dim = embedding_dim\nself.__feature_dim = feature_dim\nself.corpus = None",
"n = len(batch1)\nscores = np.zeros(n * (n - 1) / 2, dtype=np.float32)\ndata = np.zeros((n * (n - 1) / 2, se... | <|body_start_0|>
self.__object2MatrixFunction = object2MatrixFunction
self.__distanceBetween2Objects = distanceBetween2Objects
self.__embedding_dim = embedding_dim
self.__feature_dim = feature_dim
self.corpus = None
<|end_body_0|>
<|body_start_1|>
n = len(batch1)
... | For processing one batch of objects that are stored in memory | ProcessInMemoryBatch | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ProcessInMemoryBatch:
"""For processing one batch of objects that are stored in memory"""
def __init__(self, object2MatrixFunction, distanceBetween2Objects, embedding_dim, feature_dim):
""":param object2MatrixFunction: function that takes an object and creates an embedding as 2D nump... | stack_v2_sparse_classes_36k_train_011394 | 16,335 | no_license | [
{
"docstring": ":param object2MatrixFunction: function that takes an object and creates an embedding as 2D numpy array of the object :param distanceBetween2Objects: function that takes two objects and returns the distance :param embedding_dim: the dimension of raw object embedding :param feature_dim: the number... | 4 | stack_v2_sparse_classes_30k_train_002092 | Implement the Python class `ProcessInMemoryBatch` described below.
Class description:
For processing one batch of objects that are stored in memory
Method signatures and docstrings:
- def __init__(self, object2MatrixFunction, distanceBetween2Objects, embedding_dim, feature_dim): :param object2MatrixFunction: function... | Implement the Python class `ProcessInMemoryBatch` described below.
Class description:
For processing one batch of objects that are stored in memory
Method signatures and docstrings:
- def __init__(self, object2MatrixFunction, distanceBetween2Objects, embedding_dim, feature_dim): :param object2MatrixFunction: function... | b3d5afd9b0a33da01ff83516e934d4620f73bcce | <|skeleton|>
class ProcessInMemoryBatch:
"""For processing one batch of objects that are stored in memory"""
def __init__(self, object2MatrixFunction, distanceBetween2Objects, embedding_dim, feature_dim):
""":param object2MatrixFunction: function that takes an object and creates an embedding as 2D nump... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ProcessInMemoryBatch:
"""For processing one batch of objects that are stored in memory"""
def __init__(self, object2MatrixFunction, distanceBetween2Objects, embedding_dim, feature_dim):
""":param object2MatrixFunction: function that takes an object and creates an embedding as 2D numpy array of th... | the_stack_v2_python_sparse | src/train.py | wxhC3SC6OPm8M1HXboMy/embedding4DTW | train | 2 |
b3309162925e9c6938c0c8d94c1e71c1b0c0c0d0 | [
"context.set_code(grpc.StatusCode.UNIMPLEMENTED)\ncontext.set_details('Method not implemented!')\nraise NotImplementedError('Method not implemented!')",
"context.set_code(grpc.StatusCode.UNIMPLEMENTED)\ncontext.set_details('Method not implemented!')\nraise NotImplementedError('Method not implemented!')",
"conte... | <|body_start_0|>
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
<|end_body_0|>
<|body_start_1|>
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not im... | Missing associated documentation comment in .proto file. | SimulatorServicer | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SimulatorServicer:
"""Missing associated documentation comment in .proto file."""
def startDeviceSimulation(self, request, context):
"""Missing associated documentation comment in .proto file."""
<|body_0|>
def stopDeviceSimulation(self, request, context):
"""Mis... | stack_v2_sparse_classes_36k_train_011395 | 6,706 | no_license | [
{
"docstring": "Missing associated documentation comment in .proto file.",
"name": "startDeviceSimulation",
"signature": "def startDeviceSimulation(self, request, context)"
},
{
"docstring": "Missing associated documentation comment in .proto file.",
"name": "stopDeviceSimulation",
"sign... | 3 | stack_v2_sparse_classes_30k_train_006461 | Implement the Python class `SimulatorServicer` described below.
Class description:
Missing associated documentation comment in .proto file.
Method signatures and docstrings:
- def startDeviceSimulation(self, request, context): Missing associated documentation comment in .proto file.
- def stopDeviceSimulation(self, r... | Implement the Python class `SimulatorServicer` described below.
Class description:
Missing associated documentation comment in .proto file.
Method signatures and docstrings:
- def startDeviceSimulation(self, request, context): Missing associated documentation comment in .proto file.
- def stopDeviceSimulation(self, r... | eb1cd0d0ee5eb02f27f76967679c29068b277a2e | <|skeleton|>
class SimulatorServicer:
"""Missing associated documentation comment in .proto file."""
def startDeviceSimulation(self, request, context):
"""Missing associated documentation comment in .proto file."""
<|body_0|>
def stopDeviceSimulation(self, request, context):
"""Mis... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SimulatorServicer:
"""Missing associated documentation comment in .proto file."""
def startDeviceSimulation(self, request, context):
"""Missing associated documentation comment in .proto file."""
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not imple... | the_stack_v2_python_sparse | src/gateway/protocol_buffers/generated_files/simulator_services_pb2_grpc.py | andru1236/mock-backend | train | 3 |
81876ccd754274a281e78fd6ab59805c8ec4960b | [
"items = []\nparent_urls = response.xpath('//div[@id=\"tab01\"]/div/h3/a/@href').extract()\nparent_titles = response.xpath('//div[@id=\"tab01\"]/div/h3/a/text()').extract()\nsub_urls = response.xpath('//div[@class=\"clearfix\"]/ul/li/a/@href').extract()\nsub_titles = response.xpath('//div[@class=\"clearfix\"]/ul/li... | <|body_start_0|>
items = []
parent_urls = response.xpath('//div[@id="tab01"]/div/h3/a/@href').extract()
parent_titles = response.xpath('//div[@id="tab01"]/div/h3/a/text()').extract()
sub_urls = response.xpath('//div[@class="clearfix"]/ul/li/a/@href').extract()
sub_titles = respon... | SinaSpider | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SinaSpider:
def parse(self, response):
"""解析所有大类title,url; 小类title,url。获取小类中文章的url,交给content_parse处理 :param response: :return:"""
<|body_0|>
def sub_pares(self, response):
"""对于返回的小类sub_url,进行递归请求 :param response: :return:"""
<|body_1|>
def content_parse... | stack_v2_sparse_classes_36k_train_011396 | 3,641 | no_license | [
{
"docstring": "解析所有大类title,url; 小类title,url。获取小类中文章的url,交给content_parse处理 :param response: :return:",
"name": "parse",
"signature": "def parse(self, response)"
},
{
"docstring": "对于返回的小类sub_url,进行递归请求 :param response: :return:",
"name": "sub_pares",
"signature": "def sub_pares(self, res... | 3 | stack_v2_sparse_classes_30k_train_012204 | Implement the Python class `SinaSpider` described below.
Class description:
Implement the SinaSpider class.
Method signatures and docstrings:
- def parse(self, response): 解析所有大类title,url; 小类title,url。获取小类中文章的url,交给content_parse处理 :param response: :return:
- def sub_pares(self, response): 对于返回的小类sub_url,进行递归请求 :param ... | Implement the Python class `SinaSpider` described below.
Class description:
Implement the SinaSpider class.
Method signatures and docstrings:
- def parse(self, response): 解析所有大类title,url; 小类title,url。获取小类中文章的url,交给content_parse处理 :param response: :return:
- def sub_pares(self, response): 对于返回的小类sub_url,进行递归请求 :param ... | bac744667f60618b5ed96d301293955ccfce7f91 | <|skeleton|>
class SinaSpider:
def parse(self, response):
"""解析所有大类title,url; 小类title,url。获取小类中文章的url,交给content_parse处理 :param response: :return:"""
<|body_0|>
def sub_pares(self, response):
"""对于返回的小类sub_url,进行递归请求 :param response: :return:"""
<|body_1|>
def content_parse... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SinaSpider:
def parse(self, response):
"""解析所有大类title,url; 小类title,url。获取小类中文章的url,交给content_parse处理 :param response: :return:"""
items = []
parent_urls = response.xpath('//div[@id="tab01"]/div/h3/a/@href').extract()
parent_titles = response.xpath('//div[@id="tab01"]/div/h3/a/t... | the_stack_v2_python_sparse | sinanews/sinanews/spiders/sina.py | Cprocc/spiderStudy | train | 0 | |
d2295f6d27c31e69bd8355a0bab9e16f7b9cadb3 | [
"preorder = []\n\ndef preOrder(node):\n if node:\n preorder.append(node.val)\n preOrder(node.left)\n preOrder(node.right)\npreOrder(root)\nreturn ' '.join(map(str, preorder))",
"deque = collections.deque((int(val) for val in data.split()))\n\ndef build(floor, ceiling):\n if deque and fl... | <|body_start_0|>
preorder = []
def preOrder(node):
if node:
preorder.append(node.val)
preOrder(node.left)
preOrder(node.right)
preOrder(root)
return ' '.join(map(str, preorder))
<|end_body_0|>
<|body_start_1|>
deque = ... | Codec | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
<|body_0|>
def deserialize(self, data):
"""Decodes your encoded data to tree. :type data: str :rtype: TreeNode"""
<|body_1|>
<|end_skeleton|>
<|body_... | stack_v2_sparse_classes_36k_train_011397 | 1,954 | 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_000054 | 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:... | d1666d44226274f13af25cf878cd63a24e1c5528 | <|skeleton|>
class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
<|body_0|>
def deserialize(self, data):
"""Decodes your encoded data to tree. :type data: str :rtype: TreeNode"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
preorder = []
def preOrder(node):
if node:
preorder.append(node.val)
preOrder(node.left)
preOrder(node.right)
... | the_stack_v2_python_sparse | BinaryTree/LeetCode449_SerializeAndDeserializeBST.py | rexhzhang/LeetCodeProbelms | train | 0 | |
80e40081905c1e571740b3c3bcadc49418c112a2 | [
"processor = cls.processors.get(dnn_cfg.processor)\ndownload_model(dnn_cfg, opt_verbose=False)\nreturn processor(dnn_cfg)",
"name = enum_obj.name.lower()\ndnn_cfg = modelzoo_cfg.modelzoo.get(name)\nreturn cls.from_dnn_cfg(dnn_cfg)"
] | <|body_start_0|>
processor = cls.processors.get(dnn_cfg.processor)
download_model(dnn_cfg, opt_verbose=False)
return processor(dnn_cfg)
<|end_body_0|>
<|body_start_1|>
name = enum_obj.name.lower()
dnn_cfg = modelzoo_cfg.modelzoo.get(name)
return cls.from_dnn_cfg(dnn_cfg)... | DNNFactory | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DNNFactory:
def from_dnn_cfg(cls, dnn_cfg):
"""Creates DNN model based on configuration from ModelZoo :param dnn_cfg: DNN object for the model :returns (NetProc):"""
<|body_0|>
def from_enum(cls, enum_obj):
"""Loads DNN model based on enum name. Use from_dnn_cfg for ... | stack_v2_sparse_classes_36k_train_011398 | 1,836 | permissive | [
{
"docstring": "Creates DNN model based on configuration from ModelZoo :param dnn_cfg: DNN object for the model :returns (NetProc):",
"name": "from_dnn_cfg",
"signature": "def from_dnn_cfg(cls, dnn_cfg)"
},
{
"docstring": "Loads DNN model based on enum name. Use from_dnn_cfg for custom props. :p... | 2 | null | Implement the Python class `DNNFactory` described below.
Class description:
Implement the DNNFactory class.
Method signatures and docstrings:
- def from_dnn_cfg(cls, dnn_cfg): Creates DNN model based on configuration from ModelZoo :param dnn_cfg: DNN object for the model :returns (NetProc):
- def from_enum(cls, enum_... | Implement the Python class `DNNFactory` described below.
Class description:
Implement the DNNFactory class.
Method signatures and docstrings:
- def from_dnn_cfg(cls, dnn_cfg): Creates DNN model based on configuration from ModelZoo :param dnn_cfg: DNN object for the model :returns (NetProc):
- def from_enum(cls, enum_... | 5c490cb72607f60e33467a9a0f412d23024e5963 | <|skeleton|>
class DNNFactory:
def from_dnn_cfg(cls, dnn_cfg):
"""Creates DNN model based on configuration from ModelZoo :param dnn_cfg: DNN object for the model :returns (NetProc):"""
<|body_0|>
def from_enum(cls, enum_obj):
"""Loads DNN model based on enum name. Use from_dnn_cfg for ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DNNFactory:
def from_dnn_cfg(cls, dnn_cfg):
"""Creates DNN model based on configuration from ModelZoo :param dnn_cfg: DNN object for the model :returns (NetProc):"""
processor = cls.processors.get(dnn_cfg.processor)
download_model(dnn_cfg, opt_verbose=False)
return processor(dn... | the_stack_v2_python_sparse | src/vframe/image/dnn_factory.py | vframeio/vframe | train | 50 | |
6c534ab421dd312248fcb969e0c085ccd8f7d7b9 | [
"if freezer_type is not FreezerPropertyFreezer:\n assert issubclass(freezer_type, Freezer)\n if not on_freeze is on_thaw is do_nothing:\n raise Exception(\"You've passed a `freezer_type` argument, so you're not allowed to pass `on_freeze` or `on_thaw` arguments. The freeze/thaw handlers should be defin... | <|body_start_0|>
if freezer_type is not FreezerPropertyFreezer:
assert issubclass(freezer_type, Freezer)
if not on_freeze is on_thaw is do_nothing:
raise Exception("You've passed a `freezer_type` argument, so you're not allowed to pass `on_freeze` or `on_thaw` arguments. ... | A property which lazy-creates a freezer. A freezer is used as a context manager to "freeze" and "thaw" an object. See documentation of `Freezer` in this package for more info. The advantages of using a `FreezerProperty` instead of creating a freezer attribute for each instance: - The `.on_freeze` and `.on_thaw` decorat... | FreezerProperty | [
"BSD-3-Clause",
"MIT",
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FreezerProperty:
"""A property which lazy-creates a freezer. A freezer is used as a context manager to "freeze" and "thaw" an object. See documentation of `Freezer` in this package for more info. The advantages of using a `FreezerProperty` instead of creating a freezer attribute for each instance... | stack_v2_sparse_classes_36k_train_011399 | 3,931 | permissive | [
{
"docstring": "Create the `FreezerProperty`. All arguments are optional: You may pass in freeze/thaw handlers as `on_freeze` and `on_thaw`, but you don't have to. You may choose a specific freezer type to use as `freezer_type`, in which case you can't use either the `on_freeze`/`on_thaw` arguments nor the deco... | 4 | stack_v2_sparse_classes_30k_train_013269 | Implement the Python class `FreezerProperty` described below.
Class description:
A property which lazy-creates a freezer. A freezer is used as a context manager to "freeze" and "thaw" an object. See documentation of `Freezer` in this package for more info. The advantages of using a `FreezerProperty` instead of creatin... | Implement the Python class `FreezerProperty` described below.
Class description:
A property which lazy-creates a freezer. A freezer is used as a context manager to "freeze" and "thaw" an object. See documentation of `Freezer` in this package for more info. The advantages of using a `FreezerProperty` instead of creatin... | cb9ef64b48f1d03275484d707dc5079b6701ad0c | <|skeleton|>
class FreezerProperty:
"""A property which lazy-creates a freezer. A freezer is used as a context manager to "freeze" and "thaw" an object. See documentation of `Freezer` in this package for more info. The advantages of using a `FreezerProperty` instead of creating a freezer attribute for each instance... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class FreezerProperty:
"""A property which lazy-creates a freezer. A freezer is used as a context manager to "freeze" and "thaw" an object. See documentation of `Freezer` in this package for more info. The advantages of using a `FreezerProperty` instead of creating a freezer attribute for each instance: - The `.on_... | the_stack_v2_python_sparse | python_toolbox/freezing/freezer_property.py | cool-RR/python_toolbox | train | 130 |
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