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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
15a6e750bf7c510c90aaca33e9acb57fef87c0b2 | [
"with pytest.raises(ValueError, match='Cannot request events with photon number below zero'):\n graph = nx.complete_graph(4)\n similarity.feature_vector_mc(graph, [-1, 4], 1)",
"with pytest.raises(ValueError, match='Maximum number of photons per mode must be at least'):\n graph = nx.complete_graph(4)\n ... | <|body_start_0|>
with pytest.raises(ValueError, match='Cannot request events with photon number below zero'):
graph = nx.complete_graph(4)
similarity.feature_vector_mc(graph, [-1, 4], 1)
<|end_body_0|>
<|body_start_1|>
with pytest.raises(ValueError, match='Maximum number of phot... | Tests for the function ``strawberryfields.apps.graph.similarity.feature_vector_mc`` | TestFeatureVectorMC | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestFeatureVectorMC:
"""Tests for the function ``strawberryfields.apps.graph.similarity.feature_vector_mc``"""
def test_bad_event_photon_numbers_mc(self):
"""Test if function raises a ``ValueError`` when input a minimum photon number that is below zero."""
<|body_0|>
def... | stack_v2_sparse_classes_36k_train_027200 | 14,580 | permissive | [
{
"docstring": "Test if function raises a ``ValueError`` when input a minimum photon number that is below zero.",
"name": "test_bad_event_photon_numbers_mc",
"signature": "def test_bad_event_photon_numbers_mc(self)"
},
{
"docstring": "Test if function raises a ``ValueError`` when input a non-pos... | 3 | null | Implement the Python class `TestFeatureVectorMC` described below.
Class description:
Tests for the function ``strawberryfields.apps.graph.similarity.feature_vector_mc``
Method signatures and docstrings:
- def test_bad_event_photon_numbers_mc(self): Test if function raises a ``ValueError`` when input a minimum photon ... | Implement the Python class `TestFeatureVectorMC` described below.
Class description:
Tests for the function ``strawberryfields.apps.graph.similarity.feature_vector_mc``
Method signatures and docstrings:
- def test_bad_event_photon_numbers_mc(self): Test if function raises a ``ValueError`` when input a minimum photon ... | 0c1c805fd5dfce465a8955ee3faf81037023a23e | <|skeleton|>
class TestFeatureVectorMC:
"""Tests for the function ``strawberryfields.apps.graph.similarity.feature_vector_mc``"""
def test_bad_event_photon_numbers_mc(self):
"""Test if function raises a ``ValueError`` when input a minimum photon number that is below zero."""
<|body_0|>
def... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TestFeatureVectorMC:
"""Tests for the function ``strawberryfields.apps.graph.similarity.feature_vector_mc``"""
def test_bad_event_photon_numbers_mc(self):
"""Test if function raises a ``ValueError`` when input a minimum photon number that is below zero."""
with pytest.raises(ValueError, m... | the_stack_v2_python_sparse | artifacts/old_dataset_versions/original_commits_v02/strawberryfields/strawberryfields#194/after/test_similarity.py | MattePalte/Bugs-Quantum-Computing-Platforms | train | 4 |
d490c244870ebe20a63289c8fdbc93370a2f2710 | [
"self.ak = accesskey\nself.sk = secretkey\nself.region = region\nself.headtosign = ['Host', 'X-Sdk-Date']",
"canonical_method = method.upper() if method else EMPTYSTRING\nuri = urlparse.urlparse(url).path\nuri = uri.replace(':', '%3A')\ncanonical_uri = uri if uri.endswith('/') else uri + '/'\nif params:\n resu... | <|body_start_0|>
self.ak = accesskey
self.sk = secretkey
self.region = region
self.headtosign = ['Host', 'X-Sdk-Date']
<|end_body_0|>
<|body_start_1|>
canonical_method = method.upper() if method else EMPTYSTRING
uri = urlparse.urlparse(url).path
uri = uri.replace... | AkSksignature | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AkSksignature:
def __init__(self, accesskey=None, secretkey=None, region=None):
"""This class is used to sign the request header. The use method is to construct the class object and then call the signature method to generate the signature string. Create a signature for a ak sk session to... | stack_v2_sparse_classes_36k_train_027201 | 18,281 | permissive | [
{
"docstring": "This class is used to sign the request header. The use method is to construct the class object and then call the signature method to generate the signature string. Create a signature for a ak sk session to a cloud provider. The AkSksignature creates a :class:`~openstack.aksksession.AkSksignature... | 5 | stack_v2_sparse_classes_30k_train_003539 | Implement the Python class `AkSksignature` described below.
Class description:
Implement the AkSksignature class.
Method signatures and docstrings:
- def __init__(self, accesskey=None, secretkey=None, region=None): This class is used to sign the request header. The use method is to construct the class object and then... | Implement the Python class `AkSksignature` described below.
Class description:
Implement the AkSksignature class.
Method signatures and docstrings:
- def __init__(self, accesskey=None, secretkey=None, region=None): This class is used to sign the request header. The use method is to construct the class object and then... | 60d75438d71ffb7998f5dc407ffa890cc98d3171 | <|skeleton|>
class AkSksignature:
def __init__(self, accesskey=None, secretkey=None, region=None):
"""This class is used to sign the request header. The use method is to construct the class object and then call the signature method to generate the signature string. Create a signature for a ak sk session to... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AkSksignature:
def __init__(self, accesskey=None, secretkey=None, region=None):
"""This class is used to sign the request header. The use method is to construct the class object and then call the signature method to generate the signature string. Create a signature for a ak sk session to a cloud provi... | the_stack_v2_python_sparse | openstack/aksksession.py | huaweicloudsdk/sdk-python | train | 20 | |
5513c0bd04c6232f6d7c5520c5dbc7348c9ed3ec | [
"best_buy = [float('-inf')] * (k + 1)\nbest_sell = [0] * (k + 1)\nfor i, price in enumerate(prices):\n for j in range(1, k + 1):\n best_buy[j] = max(best_buy[j], best_sell[j - 1] - price)\n best_sell[j] = max(best_sell[j], best_buy[j] + price)\nreturn best_sell[k]",
"def max_profit_unlimited(pric... | <|body_start_0|>
best_buy = [float('-inf')] * (k + 1)
best_sell = [0] * (k + 1)
for i, price in enumerate(prices):
for j in range(1, k + 1):
best_buy[j] = max(best_buy[j], best_sell[j - 1] - price)
best_sell[j] = max(best_sell[j], best_buy[j] + price)
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def maxProfit(self, k, prices):
""":type k: int :type prices: List[int] :rtype: int"""
<|body_0|>
def maxProfit_optimized(self, k, prices):
""":type k: int :type prices: List[int] :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
... | stack_v2_sparse_classes_36k_train_027202 | 2,800 | no_license | [
{
"docstring": ":type k: int :type prices: List[int] :rtype: int",
"name": "maxProfit",
"signature": "def maxProfit(self, k, prices)"
},
{
"docstring": ":type k: int :type prices: List[int] :rtype: int",
"name": "maxProfit_optimized",
"signature": "def maxProfit_optimized(self, k, prices... | 2 | stack_v2_sparse_classes_30k_train_006307 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def maxProfit(self, k, prices): :type k: int :type prices: List[int] :rtype: int
- def maxProfit_optimized(self, k, prices): :type k: int :type prices: List[int] :rtype: int | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def maxProfit(self, k, prices): :type k: int :type prices: List[int] :rtype: int
- def maxProfit_optimized(self, k, prices): :type k: int :type prices: List[int] :rtype: int
<|s... | e60ba45fe2f2e5e3b3abfecec3db76f5ce1fde59 | <|skeleton|>
class Solution:
def maxProfit(self, k, prices):
""":type k: int :type prices: List[int] :rtype: int"""
<|body_0|>
def maxProfit_optimized(self, k, prices):
""":type k: int :type prices: List[int] :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def maxProfit(self, k, prices):
""":type k: int :type prices: List[int] :rtype: int"""
best_buy = [float('-inf')] * (k + 1)
best_sell = [0] * (k + 1)
for i, price in enumerate(prices):
for j in range(1, k + 1):
best_buy[j] = max(best_buy[j]... | the_stack_v2_python_sparse | src/lt_188.py | oxhead/CodingYourWay | train | 0 | |
f0c9bd0e9b89962c6f417af23172ea536acfe5c9 | [
"if current_user in self.event.guests:\n return jsonify({'status': 200, 'message': 'You are registered as a guest'})\nelif current_user in self.event.participants:\n return jsonify({'status': 200, 'message': 'You are registered as a participant'})\nelse:\n return jsonify({'status': 200, 'message': 'You are... | <|body_start_0|>
if current_user in self.event.guests:
return jsonify({'status': 200, 'message': 'You are registered as a guest'})
elif current_user in self.event.participants:
return jsonify({'status': 200, 'message': 'You are registered as a participant'})
else:
... | Resource for registering and unregistering as a guest fo event | UserAsGuest | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class UserAsGuest:
"""Resource for registering and unregistering as a guest fo event"""
def get(self, event_id: int) -> Response:
"""Method for checking registration as a guest Parameters ---------- event_id : int Event id Returns ------- Response Response message with status code"""
... | stack_v2_sparse_classes_36k_train_027203 | 6,917 | no_license | [
{
"docstring": "Method for checking registration as a guest Parameters ---------- event_id : int Event id Returns ------- Response Response message with status code",
"name": "get",
"signature": "def get(self, event_id: int) -> Response"
},
{
"docstring": "Method for registering as a guest Param... | 3 | stack_v2_sparse_classes_30k_train_019261 | Implement the Python class `UserAsGuest` described below.
Class description:
Resource for registering and unregistering as a guest fo event
Method signatures and docstrings:
- def get(self, event_id: int) -> Response: Method for checking registration as a guest Parameters ---------- event_id : int Event id Returns --... | Implement the Python class `UserAsGuest` described below.
Class description:
Resource for registering and unregistering as a guest fo event
Method signatures and docstrings:
- def get(self, event_id: int) -> Response: Method for checking registration as a guest Parameters ---------- event_id : int Event id Returns --... | 51e4d69f88c120cfc587fd007f21528a7bd661a0 | <|skeleton|>
class UserAsGuest:
"""Resource for registering and unregistering as a guest fo event"""
def get(self, event_id: int) -> Response:
"""Method for checking registration as a guest Parameters ---------- event_id : int Event id Returns ------- Response Response message with status code"""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class UserAsGuest:
"""Resource for registering and unregistering as a guest fo event"""
def get(self, event_id: int) -> Response:
"""Method for checking registration as a guest Parameters ---------- event_id : int Event id Returns ------- Response Response message with status code"""
if current... | the_stack_v2_python_sparse | flask_app/resources/guest.py | Kyrylo-Kotelevets/Flask_Events | train | 0 |
6c921cedc11e8e67ca6e11221b1b5e1203b4c5f6 | [
"super(Model, self).__init__()\nself.input_size_dyn = input_size_dyn\nself.input_size_stat = input_size_stat\nself.hidden_size = hidden_size\nself.initial_forget_bias = initial_forget_bias\nself.dropout_rate = dropout\nself.concat_static = concat_static\nself.no_static = no_static\nif self.concat_static or self.no_... | <|body_start_0|>
super(Model, self).__init__()
self.input_size_dyn = input_size_dyn
self.input_size_stat = input_size_stat
self.hidden_size = hidden_size
self.initial_forget_bias = initial_forget_bias
self.dropout_rate = dropout
self.concat_static = concat_static
... | Wrapper class that connects LSTM/EA-LSTM with fully connceted layer | Model | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Model:
"""Wrapper class that connects LSTM/EA-LSTM with fully connceted layer"""
def __init__(self, input_size_dyn: int, input_size_stat: int, hidden_size: int, initial_forget_bias: int=5, dropout: float=0.0, concat_static: bool=False, no_static: bool=False):
"""Initialize model. Par... | stack_v2_sparse_classes_36k_train_027204 | 26,253 | permissive | [
{
"docstring": "Initialize model. Parameters ---------- input_size_dyn: int Number of dynamic input features. input_size_stat: int Number of static input features (used in the EA-LSTM input gate). hidden_size: int Number of LSTM cells/hidden units. initial_forget_bias: int Value of the initial forget gate bias.... | 2 | stack_v2_sparse_classes_30k_train_010754 | Implement the Python class `Model` described below.
Class description:
Wrapper class that connects LSTM/EA-LSTM with fully connceted layer
Method signatures and docstrings:
- def __init__(self, input_size_dyn: int, input_size_stat: int, hidden_size: int, initial_forget_bias: int=5, dropout: float=0.0, concat_static: ... | Implement the Python class `Model` described below.
Class description:
Wrapper class that connects LSTM/EA-LSTM with fully connceted layer
Method signatures and docstrings:
- def __init__(self, input_size_dyn: int, input_size_stat: int, hidden_size: int, initial_forget_bias: int=5, dropout: float=0.0, concat_static: ... | 88334091dec71ae43fe4256603d65045141936b5 | <|skeleton|>
class Model:
"""Wrapper class that connects LSTM/EA-LSTM with fully connceted layer"""
def __init__(self, input_size_dyn: int, input_size_stat: int, hidden_size: int, initial_forget_bias: int=5, dropout: float=0.0, concat_static: bool=False, no_static: bool=False):
"""Initialize model. Par... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Model:
"""Wrapper class that connects LSTM/EA-LSTM with fully connceted layer"""
def __init__(self, input_size_dyn: int, input_size_stat: int, hidden_size: int, initial_forget_bias: int=5, dropout: float=0.0, concat_static: bool=False, no_static: bool=False):
"""Initialize model. Parameters -----... | the_stack_v2_python_sparse | src/evaluate/EALSTM_err_est_oversamp_norm2.py | jdwillard19/lake_conus_surface_temp_2021 | train | 0 |
544488cdea87a6adc270543132613ac171fec0ad | [
"super(DataQualityOperator, self).__init__(*args, **kwargs)\nself.redshift_conn_id = redshift_conn_id\nself.table = table\nself.sql_query = sql_query\nself.at_least = at_least\nself.equals = equals",
"if self.at_least and type(self.at_least) is str:\n self.at_least = int(self.at_least)\nif self.equals and type... | <|body_start_0|>
super(DataQualityOperator, self).__init__(*args, **kwargs)
self.redshift_conn_id = redshift_conn_id
self.table = table
self.sql_query = sql_query
self.at_least = at_least
self.equals = equals
<|end_body_0|>
<|body_start_1|>
if self.at_least and t... | The DataQualityOperator performs data validation on tables after data has been inserted. Data validation can be don either against the `at_least` or `equals` arguments. | DataQualityOperator | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DataQualityOperator:
"""The DataQualityOperator performs data validation on tables after data has been inserted. Data validation can be don either against the `at_least` or `equals` arguments."""
def __init__(self, redshift_conn_id='', table='', sql_query='', at_least=None, equals=None, *arg... | stack_v2_sparse_classes_36k_train_027205 | 2,983 | no_license | [
{
"docstring": "Init method for the operator Args: redshift_conn_id Connection information for the datbase table Name of table to check sql_query Query to execute for getting row count at_least At least this many rows need to exist in the table if value set. equals Row count in table must match specified value.... | 2 | stack_v2_sparse_classes_30k_train_016948 | Implement the Python class `DataQualityOperator` described below.
Class description:
The DataQualityOperator performs data validation on tables after data has been inserted. Data validation can be don either against the `at_least` or `equals` arguments.
Method signatures and docstrings:
- def __init__(self, redshift_... | Implement the Python class `DataQualityOperator` described below.
Class description:
The DataQualityOperator performs data validation on tables after data has been inserted. Data validation can be don either against the `at_least` or `equals` arguments.
Method signatures and docstrings:
- def __init__(self, redshift_... | 27930a41a6de6049a05375f488c9ac94608ed2fe | <|skeleton|>
class DataQualityOperator:
"""The DataQualityOperator performs data validation on tables after data has been inserted. Data validation can be don either against the `at_least` or `equals` arguments."""
def __init__(self, redshift_conn_id='', table='', sql_query='', at_least=None, equals=None, *arg... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DataQualityOperator:
"""The DataQualityOperator performs data validation on tables after data has been inserted. Data validation can be don either against the `at_least` or `equals` arguments."""
def __init__(self, redshift_conn_id='', table='', sql_query='', at_least=None, equals=None, *args, **kwargs):... | the_stack_v2_python_sparse | 06-capstone-project/airflow/plugins/operators/data_quality.py | mvillafuertem/udacity-data-engineer-nanodegree | train | 0 |
a769aabb21e4f95eb3bd46093b24f4cd0406b2a2 | [
"if x < 0:\n return False\ncount = 0\ny = x\nwhile y:\n count += 1\n y = y // 10\ncount -= 1\nwhile x and count:\n decimal = False\n if x >= 10:\n decimal = True\n up = x // 10 ** count\n x = x % 10 ** count\n if x == 0:\n if decimal:\n return False\n return T... | <|body_start_0|>
if x < 0:
return False
count = 0
y = x
while y:
count += 1
y = y // 10
count -= 1
while x and count:
decimal = False
if x >= 10:
decimal = True
up = x // 10 ** count
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def isPalindrome(self, x):
""":type x: int :rtype: bool"""
<|body_0|>
def isPalindrome_failed(self, x):
""":type x: int :rtype: bool"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
if x < 0:
return False
count = 0
... | stack_v2_sparse_classes_36k_train_027206 | 3,011 | no_license | [
{
"docstring": ":type x: int :rtype: bool",
"name": "isPalindrome",
"signature": "def isPalindrome(self, x)"
},
{
"docstring": ":type x: int :rtype: bool",
"name": "isPalindrome_failed",
"signature": "def isPalindrome_failed(self, x)"
}
] | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def isPalindrome(self, x): :type x: int :rtype: bool
- def isPalindrome_failed(self, x): :type x: int :rtype: bool | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def isPalindrome(self, x): :type x: int :rtype: bool
- def isPalindrome_failed(self, x): :type x: int :rtype: bool
<|skeleton|>
class Solution:
def isPalindrome(self, x):
... | 93266095329e2e8e949a72371b88b07382a60e0d | <|skeleton|>
class Solution:
def isPalindrome(self, x):
""":type x: int :rtype: bool"""
<|body_0|>
def isPalindrome_failed(self, x):
""":type x: int :rtype: bool"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def isPalindrome(self, x):
""":type x: int :rtype: bool"""
if x < 0:
return False
count = 0
y = x
while y:
count += 1
y = y // 10
count -= 1
while x and count:
decimal = False
if x >= ... | the_stack_v2_python_sparse | isPalindrome_9.py | shivangi-prog/leetcode | train | 0 | |
02146d82c21ec828cfd31d3abd5dcadaa22d4923 | [
"self._future = future\nself._callback = callback\nself._running_callback = False",
"response = self._future.wait()\nself._running_callback = True\nreturn self._callback(response)",
"if self._running_callback:\n return False\nreturn self._future.cancel()"
] | <|body_start_0|>
self._future = future
self._callback = callback
self._running_callback = False
<|end_body_0|>
<|body_start_1|>
response = self._future.wait()
self._running_callback = True
return self._callback(response)
<|end_body_1|>
<|body_start_2|>
if self._... | Internal base class with common attributes for Result implementations. Subclasses will not inherit this docstring and should not use it. Instead, they should use _doc. | _Result | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class _Result:
"""Internal base class with common attributes for Result implementations. Subclasses will not inherit this docstring and should not use it. Instead, they should use _doc."""
def __init__(self, future, callback):
""":arg original: The future this depends on. For example, a Tr... | stack_v2_sparse_classes_36k_train_027207 | 2,610 | no_license | [
{
"docstring": ":arg original: The future this depends on. For example, a Transfer which returns a Response from wait(). :arg callback: A callback which takes one argument. Whatever this returns will be returned by wait(). For example, a parse function which takes a Response and returns a Route.",
"name": "... | 3 | stack_v2_sparse_classes_30k_train_000851 | Implement the Python class `_Result` described below.
Class description:
Internal base class with common attributes for Result implementations. Subclasses will not inherit this docstring and should not use it. Instead, they should use _doc.
Method signatures and docstrings:
- def __init__(self, future, callback): :ar... | Implement the Python class `_Result` described below.
Class description:
Internal base class with common attributes for Result implementations. Subclasses will not inherit this docstring and should not use it. Instead, they should use _doc.
Method signatures and docstrings:
- def __init__(self, future, callback): :ar... | 71db923df2862ff246755f1d819b0eecc76960ef | <|skeleton|>
class _Result:
"""Internal base class with common attributes for Result implementations. Subclasses will not inherit this docstring and should not use it. Instead, they should use _doc."""
def __init__(self, future, callback):
""":arg original: The future this depends on. For example, a Tr... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class _Result:
"""Internal base class with common attributes for Result implementations. Subclasses will not inherit this docstring and should not use it. Instead, they should use _doc."""
def __init__(self, future, callback):
""":arg original: The future this depends on. For example, a Transfer which ... | the_stack_v2_python_sparse | because/_future_base.py | jrbeckwith/because | train | 3 |
e1ada5ffa37343f5d6ff2475231eeaf0e32d1efa | [
"citations.sort()\ncitations.reverse()\ncount = 0\nfor i, num in enumerate(citations, 1):\n if num >= i:\n count += 1\nreturn count",
"table = [0] * (len(citations) + 1)\nfor num in citations:\n if num > len(citations):\n table[-1] += 1\n continue\n table[num] += 1\nresult = 0\nfor i... | <|body_start_0|>
citations.sort()
citations.reverse()
count = 0
for i, num in enumerate(citations, 1):
if num >= i:
count += 1
return count
<|end_body_0|>
<|body_start_1|>
table = [0] * (len(citations) + 1)
for num in citations:
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def hIndex_slow(self, citations):
""":type citations: List[int] :rtype: int"""
<|body_0|>
def hIndex_linear(self, citations):
""":type citations: List[int] :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
citations.sort()
... | stack_v2_sparse_classes_36k_train_027208 | 882 | no_license | [
{
"docstring": ":type citations: List[int] :rtype: int",
"name": "hIndex_slow",
"signature": "def hIndex_slow(self, citations)"
},
{
"docstring": ":type citations: List[int] :rtype: int",
"name": "hIndex_linear",
"signature": "def hIndex_linear(self, citations)"
}
] | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def hIndex_slow(self, citations): :type citations: List[int] :rtype: int
- def hIndex_linear(self, citations): :type citations: List[int] :rtype: int | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def hIndex_slow(self, citations): :type citations: List[int] :rtype: int
- def hIndex_linear(self, citations): :type citations: List[int] :rtype: int
<|skeleton|>
class Solution... | e61776bcfd5d93c663b247d71e00f1b298683714 | <|skeleton|>
class Solution:
def hIndex_slow(self, citations):
""":type citations: List[int] :rtype: int"""
<|body_0|>
def hIndex_linear(self, citations):
""":type citations: List[int] :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def hIndex_slow(self, citations):
""":type citations: List[int] :rtype: int"""
citations.sort()
citations.reverse()
count = 0
for i, num in enumerate(citations, 1):
if num >= i:
count += 1
return count
def hIndex_linear... | the_stack_v2_python_sparse | bloomberg/h_index.py | Omega094/lc_practice | train | 0 | |
b38989d148a7bdbe085c2511acd1689c1b1fa96c | [
"if minfo is None:\n minfo = {}\nsuper(ResetStatsMessage, self).__init__(minfo)\nself.IsSystemMessage = False\nself.IsForward = True\nself.IsReliable = True\nself.DomainList = minfo.get('DomainList', [])\nself.MetricList = minfo.get('MetricList', [])",
"result = super(ResetStatsMessage, self).dump()\nresult['D... | <|body_start_0|>
if minfo is None:
minfo = {}
super(ResetStatsMessage, self).__init__(minfo)
self.IsSystemMessage = False
self.IsForward = True
self.IsReliable = True
self.DomainList = minfo.get('DomainList', [])
self.MetricList = minfo.get('MetricList... | Reset stats messages are sent to a peer node to request it to reset statistics. Attributes: ResetStatsMessage.MessageType (str): The class name of the message. IsSystemMessage (bool): Whether or not this is a system message. System messages have special delivery priority rules. IsForward (bool): Whether the message sho... | ResetStatsMessage | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ResetStatsMessage:
"""Reset stats messages are sent to a peer node to request it to reset statistics. Attributes: ResetStatsMessage.MessageType (str): The class name of the message. IsSystemMessage (bool): Whether or not this is a system message. System messages have special delivery priority rul... | stack_v2_sparse_classes_36k_train_027209 | 13,482 | permissive | [
{
"docstring": "Constructor for the ResetStatsMessage class. Args: minfo (dict): Dictionary of values for message fields.",
"name": "__init__",
"signature": "def __init__(self, minfo=None)"
},
{
"docstring": "Dumps a dict containing object attributes. Returns: dict: A mapping of object attribute... | 2 | stack_v2_sparse_classes_30k_val_000454 | Implement the Python class `ResetStatsMessage` described below.
Class description:
Reset stats messages are sent to a peer node to request it to reset statistics. Attributes: ResetStatsMessage.MessageType (str): The class name of the message. IsSystemMessage (bool): Whether or not this is a system message. System mess... | Implement the Python class `ResetStatsMessage` described below.
Class description:
Reset stats messages are sent to a peer node to request it to reset statistics. Attributes: ResetStatsMessage.MessageType (str): The class name of the message. IsSystemMessage (bool): Whether or not this is a system message. System mess... | 8f4ca1aab54ef420a0db10c8ca822ec8686cd423 | <|skeleton|>
class ResetStatsMessage:
"""Reset stats messages are sent to a peer node to request it to reset statistics. Attributes: ResetStatsMessage.MessageType (str): The class name of the message. IsSystemMessage (bool): Whether or not this is a system message. System messages have special delivery priority rul... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ResetStatsMessage:
"""Reset stats messages are sent to a peer node to request it to reset statistics. Attributes: ResetStatsMessage.MessageType (str): The class name of the message. IsSystemMessage (bool): Whether or not this is a system message. System messages have special delivery priority rules. IsForward... | the_stack_v2_python_sparse | validator/gossip/messages/gossip_debug.py | aludvik/sawtooth-core | train | 0 |
b7256234838ebc0ff2cb38d2452c03ad780a3213 | [
"account = Account.objects.create(firstname='Test', lastname='Test', email='test@email.com')\npost = Post.objects.create(title='Test Title', content='Test Content', account=account)\nPostComment.objects.create(post=post, account=account, comment='Comment 1')\nPostComment.objects.create(post=post, account=account, c... | <|body_start_0|>
account = Account.objects.create(firstname='Test', lastname='Test', email='test@email.com')
post = Post.objects.create(title='Test Title', content='Test Content', account=account)
PostComment.objects.create(post=post, account=account, comment='Comment 1')
PostComment.obj... | Test class for the PostCommentListView | PostCommentViewTests | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PostCommentViewTests:
"""Test class for the PostCommentListView"""
def setUp(self):
"""Set up variables for tests"""
<|body_0|>
def test_status_code(self):
"""Test status code response from endpoint"""
<|body_1|>
def test_list(self):
"""Test ... | stack_v2_sparse_classes_36k_train_027210 | 15,306 | permissive | [
{
"docstring": "Set up variables for tests",
"name": "setUp",
"signature": "def setUp(self)"
},
{
"docstring": "Test status code response from endpoint",
"name": "test_status_code",
"signature": "def test_status_code(self)"
},
{
"docstring": "Test list function in PostCommentView... | 6 | stack_v2_sparse_classes_30k_train_021329 | Implement the Python class `PostCommentViewTests` described below.
Class description:
Test class for the PostCommentListView
Method signatures and docstrings:
- def setUp(self): Set up variables for tests
- def test_status_code(self): Test status code response from endpoint
- def test_list(self): Test list function i... | Implement the Python class `PostCommentViewTests` described below.
Class description:
Test class for the PostCommentListView
Method signatures and docstrings:
- def setUp(self): Set up variables for tests
- def test_status_code(self): Test status code response from endpoint
- def test_list(self): Test list function i... | a364e9997c1c91b09f5db8a004deb4df305fa8cf | <|skeleton|>
class PostCommentViewTests:
"""Test class for the PostCommentListView"""
def setUp(self):
"""Set up variables for tests"""
<|body_0|>
def test_status_code(self):
"""Test status code response from endpoint"""
<|body_1|>
def test_list(self):
"""Test ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PostCommentViewTests:
"""Test class for the PostCommentListView"""
def setUp(self):
"""Set up variables for tests"""
account = Account.objects.create(firstname='Test', lastname='Test', email='test@email.com')
post = Post.objects.create(title='Test Title', content='Test Content', a... | the_stack_v2_python_sparse | libStash/blogs/tests.py | Dev-Rem/libStash | train | 0 |
821ced1924541beac87d68d2066ca75365cf22ed | [
"a1, a2 = ([], [])\nwhile l1:\n a1.append(l1.val)\n l1 = l1.next\nwhile l2:\n a2.append(l2.val)\n l2 = l2.next\ns1 = ''\nfor i in range(len(a1) - 1, -1, -1):\n s1 = s1 + str(a1[i])\ns2 = ''\nfor j in range(len(a2) - 1, -1, -1):\n s2 = s2 + str(a2[j])\nm1, m2 = (int(s1), int(s2))\nm3 = str(m1 + m2)... | <|body_start_0|>
a1, a2 = ([], [])
while l1:
a1.append(l1.val)
l1 = l1.next
while l2:
a2.append(l2.val)
l2 = l2.next
s1 = ''
for i in range(len(a1) - 1, -1, -1):
s1 = s1 + str(a1[i])
s2 = ''
for j in rang... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def addTwoNumbers1(self, l1: ListNode, l2: ListNode) -> ListNode:
"""正向思维，根据题意直推法，先将两个链表转换成列表形式 为了计算两个链表之和，将列表转成字符串形式，再将字符串转成 int型 此时计算出和，因为最后返回的是链表形式，所以计算出和之后，将和值转为字符串 然后再从后往前遍历字符串，并插入空链表，最后可返回结果链表 地址：https://leetcode-cn.com/problems/add-two-numbers/solution/liang-shu-xiang-ji... | stack_v2_sparse_classes_36k_train_027211 | 7,511 | no_license | [
{
"docstring": "正向思维，根据题意直推法，先将两个链表转换成列表形式 为了计算两个链表之和，将列表转成字符串形式，再将字符串转成 int型 此时计算出和，因为最后返回的是链表形式，所以计算出和之后，将和值转为字符串 然后再从后往前遍历字符串，并插入空链表，最后可返回结果链表 地址：https://leetcode-cn.com/problems/add-two-numbers/solution/liang-shu-xiang-jia-by-xing-yun-de-bei-ji-lang/ # 注意此思路没问题，结果有问题。。。。 :param l1: :param l2: :return:",
... | 3 | stack_v2_sparse_classes_30k_train_011128 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def addTwoNumbers1(self, l1: ListNode, l2: ListNode) -> ListNode: 正向思维，根据题意直推法，先将两个链表转换成列表形式 为了计算两个链表之和，将列表转成字符串形式，再将字符串转成 int型 此时计算出和，因为最后返回的是链表形式，所以计算出和之后，将和值转为字符串 然后再从后往前遍历字符串... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def addTwoNumbers1(self, l1: ListNode, l2: ListNode) -> ListNode: 正向思维，根据题意直推法，先将两个链表转换成列表形式 为了计算两个链表之和，将列表转成字符串形式，再将字符串转成 int型 此时计算出和，因为最后返回的是链表形式，所以计算出和之后，将和值转为字符串 然后再从后往前遍历字符串... | 51943e2c2c4ec70c7c1d5b53c9fdf0a719428d7a | <|skeleton|>
class Solution:
def addTwoNumbers1(self, l1: ListNode, l2: ListNode) -> ListNode:
"""正向思维，根据题意直推法，先将两个链表转换成列表形式 为了计算两个链表之和，将列表转成字符串形式，再将字符串转成 int型 此时计算出和，因为最后返回的是链表形式，所以计算出和之后，将和值转为字符串 然后再从后往前遍历字符串，并插入空链表，最后可返回结果链表 地址：https://leetcode-cn.com/problems/add-two-numbers/solution/liang-shu-xiang-ji... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def addTwoNumbers1(self, l1: ListNode, l2: ListNode) -> ListNode:
"""正向思维，根据题意直推法，先将两个链表转换成列表形式 为了计算两个链表之和，将列表转成字符串形式，再将字符串转成 int型 此时计算出和，因为最后返回的是链表形式，所以计算出和之后，将和值转为字符串 然后再从后往前遍历字符串，并插入空链表，最后可返回结果链表 地址：https://leetcode-cn.com/problems/add-two-numbers/solution/liang-shu-xiang-jia-by-xing-yun-... | the_stack_v2_python_sparse | LeetCode_practice/LinkedList/0002_AddTwoNumbers.py | LeBron-Jian/BasicAlgorithmPractice | train | 13 | |
31960e3bc7f4660d6f8f35a3f50da1109ca5e881 | [
"tmp = 0\nfor ele in nums:\n if ele == target:\n tmp += 1\nreturn tmp",
"def find_target(target):\n i, j = (0, len(nums) - 1)\n while i <= j:\n mid = (i + j) // 2\n if nums[mid] <= target:\n i = mid + 1\n else:\n j = mid - 1\n return i\nreturn find_tar... | <|body_start_0|>
tmp = 0
for ele in nums:
if ele == target:
tmp += 1
return tmp
<|end_body_0|>
<|body_start_1|>
def find_target(target):
i, j = (0, len(nums) - 1)
while i <= j:
mid = (i + j) // 2
if nums... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def search(self, nums: List[int], target: int) -> int:
"""遍历 :param nums: :param target: :return:"""
<|body_0|>
def search(self, nums: List[int], target: int) -> int:
"""排序树组, 所以可以用两次二分查找, 寻找左边界target-1和右边界target 如果最后一个数是target,找右边界时 i 必须等于 j, 所以while i<=j ... | stack_v2_sparse_classes_36k_train_027212 | 1,575 | no_license | [
{
"docstring": "遍历 :param nums: :param target: :return:",
"name": "search",
"signature": "def search(self, nums: List[int], target: int) -> int"
},
{
"docstring": "排序树组, 所以可以用两次二分查找, 寻找左边界target-1和右边界target 如果最后一个数是target,找右边界时 i 必须等于 j, 所以while i<=j :param nums: :param target: :return:",
"n... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def search(self, nums: List[int], target: int) -> int: 遍历 :param nums: :param target: :return:
- def search(self, nums: List[int], target: int) -> int: 排序树组, 所以可以用两次二分查找, 寻找左边界ta... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def search(self, nums: List[int], target: int) -> int: 遍历 :param nums: :param target: :return:
- def search(self, nums: List[int], target: int) -> int: 排序树组, 所以可以用两次二分查找, 寻找左边界ta... | b1680014ce3f55ba952a1e64241c0cbb783cc436 | <|skeleton|>
class Solution:
def search(self, nums: List[int], target: int) -> int:
"""遍历 :param nums: :param target: :return:"""
<|body_0|>
def search(self, nums: List[int], target: int) -> int:
"""排序树组, 所以可以用两次二分查找, 寻找左边界target-1和右边界target 如果最后一个数是target,找右边界时 i 必须等于 j, 所以while i<=j ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def search(self, nums: List[int], target: int) -> int:
"""遍历 :param nums: :param target: :return:"""
tmp = 0
for ele in nums:
if ele == target:
tmp += 1
return tmp
def search(self, nums: List[int], target: int) -> int:
"""排序树组,... | the_stack_v2_python_sparse | 53_1.py | sun510001/leetcode_jianzhi_offer_2 | train | 0 | |
f6c01432cbbf8dde67242911ff17c0f4114d67e7 | [
"self.sess = requests.session()\nself.username = username\nself.password = password\nself.token = ''\nself.expiretime = -1\nself.special_topic_id = special_topic_id\nself.start_floor = start_floor",
"login_data = {'client_id': '9a1fd200-8687-44b1-4c20-08d50a96e5cd', 'client_secret': '8b53f727-08e2-4509-8857-e34bf... | <|body_start_0|>
self.sess = requests.session()
self.username = username
self.password = password
self.token = ''
self.expiretime = -1
self.special_topic_id = special_topic_id
self.start_floor = start_floor
<|end_body_0|>
<|body_start_1|>
login_data = {'c... | CC98 | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CC98:
def __init__(self, username, password, special_topic_id, start_floor):
"""初始化。 :param username: 用户名 :param password: 密码"""
<|body_0|>
def login(self):
"""发起登录请求 以获取access_token 存入self.token 并将过期时间写入self.expiretime"""
<|body_1|>
def get_topic_post(s... | stack_v2_sparse_classes_36k_train_027213 | 5,050 | permissive | [
{
"docstring": "初始化。 :param username: 用户名 :param password: 密码",
"name": "__init__",
"signature": "def __init__(self, username, password, special_topic_id, start_floor)"
},
{
"docstring": "发起登录请求 以获取access_token 存入self.token 并将过期时间写入self.expiretime",
"name": "login",
"signature": "def log... | 5 | stack_v2_sparse_classes_30k_train_020290 | Implement the Python class `CC98` described below.
Class description:
Implement the CC98 class.
Method signatures and docstrings:
- def __init__(self, username, password, special_topic_id, start_floor): 初始化。 :param username: 用户名 :param password: 密码
- def login(self): 发起登录请求 以获取access_token 存入self.token 并将过期时间写入self.e... | Implement the Python class `CC98` described below.
Class description:
Implement the CC98 class.
Method signatures and docstrings:
- def __init__(self, username, password, special_topic_id, start_floor): 初始化。 :param username: 用户名 :param password: 密码
- def login(self): 发起登录请求 以获取access_token 存入self.token 并将过期时间写入self.e... | a98d2a2df168b93531c32c5bd57917c194421a00 | <|skeleton|>
class CC98:
def __init__(self, username, password, special_topic_id, start_floor):
"""初始化。 :param username: 用户名 :param password: 密码"""
<|body_0|>
def login(self):
"""发起登录请求 以获取access_token 存入self.token 并将过期时间写入self.expiretime"""
<|body_1|>
def get_topic_post(s... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CC98:
def __init__(self, username, password, special_topic_id, start_floor):
"""初始化。 :param username: 用户名 :param password: 密码"""
self.sess = requests.session()
self.username = username
self.password = password
self.token = ''
self.expiretime = -1
self.sp... | the_stack_v2_python_sparse | 8. CC98拼车信息批量汇总/pinche.py | 2222RyanLee/ZJU-toolkit | train | 0 | |
2ca1ec20745f4cf0e43e5c0b22c205a1108d9f79 | [
"self._checkpoint_every_n = checkpoint_every_n\nrun_experiment.Runner.__init__(self, base_dir, create_agent_fn, **kwargs)\nself._training_steps = int(self._training_steps * self._agent._gin_param_multiplier)",
"self._checkpointer = checkpointer.Checkpointer(self._checkpoint_dir, checkpoint_file_prefix, checkpoint... | <|body_start_0|>
self._checkpoint_every_n = checkpoint_every_n
run_experiment.Runner.__init__(self, base_dir, create_agent_fn, **kwargs)
self._training_steps = int(self._training_steps * self._agent._gin_param_multiplier)
<|end_body_0|>
<|body_start_1|>
self._checkpointer = checkpointer... | Extends the base Runner for every-n-step checkpoint writing. | ElephantRunner | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ElephantRunner:
"""Extends the base Runner for every-n-step checkpoint writing."""
def __init__(self, base_dir, create_agent_fn, checkpoint_every_n=1, **kwargs):
"""Initialize the Runner object in charge of running a full experiment. Args: base_dir: str, the base directory to host al... | stack_v2_sparse_classes_36k_train_027214 | 6,020 | permissive | [
{
"docstring": "Initialize the Runner object in charge of running a full experiment. Args: base_dir: str, the base directory to host all required sub-directories. create_agent_fn: A function that takes as args a Tensorflow session and an environment, and returns an agent. checkpoint_every_n: int, the frequency ... | 3 | stack_v2_sparse_classes_30k_train_020323 | Implement the Python class `ElephantRunner` described below.
Class description:
Extends the base Runner for every-n-step checkpoint writing.
Method signatures and docstrings:
- def __init__(self, base_dir, create_agent_fn, checkpoint_every_n=1, **kwargs): Initialize the Runner object in charge of running a full exper... | Implement the Python class `ElephantRunner` described below.
Class description:
Extends the base Runner for every-n-step checkpoint writing.
Method signatures and docstrings:
- def __init__(self, base_dir, create_agent_fn, checkpoint_every_n=1, **kwargs): Initialize the Runner object in charge of running a full exper... | 727ec399ad17b4dd1f71ce69a26fc3b0371d9fa7 | <|skeleton|>
class ElephantRunner:
"""Extends the base Runner for every-n-step checkpoint writing."""
def __init__(self, base_dir, create_agent_fn, checkpoint_every_n=1, **kwargs):
"""Initialize the Runner object in charge of running a full experiment. Args: base_dir: str, the base directory to host al... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ElephantRunner:
"""Extends the base Runner for every-n-step checkpoint writing."""
def __init__(self, base_dir, create_agent_fn, checkpoint_every_n=1, **kwargs):
"""Initialize the Runner object in charge of running a full experiment. Args: base_dir: str, the base directory to host all required su... | the_stack_v2_python_sparse | experience_replay/run_experience_replay_experiment.py | Ayoob7/google-research | train | 2 |
d9a3588699d934f25cedb6b3cd5b3bffc02a1cdf | [
"Parametre.__init__(self, 'dupliquer', 'duplicate')\nself.nom_groupe = 'administrateur'\nself.schema = '<nombre> <depuis:nombre>'\nself.aide_courte = 'marque un rapport comme dupliqué'\nself.aide_longue = \"Cette commande est un raccourci pour indiquer qu'un rapport est une duplication d'un autre rapport. Il attend... | <|body_start_0|>
Parametre.__init__(self, 'dupliquer', 'duplicate')
self.nom_groupe = 'administrateur'
self.schema = '<nombre> <depuis:nombre>'
self.aide_courte = 'marque un rapport comme dupliqué'
self.aide_longue = "Cette commande est un raccourci pour indiquer qu'un rapport es... | Commande 'rapport dupliquer' | PrmDupliquer | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PrmDupliquer:
"""Commande 'rapport dupliquer'"""
def __init__(self):
"""Constructeur du paramètre."""
<|body_0|>
def interpreter(self, personnage, dic_masques):
"""Méthode d'interprétation de commande"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
... | stack_v2_sparse_classes_36k_train_027215 | 3,853 | permissive | [
{
"docstring": "Constructeur du paramètre.",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Méthode d'interprétation de commande",
"name": "interpreter",
"signature": "def interpreter(self, personnage, dic_masques)"
}
] | 2 | null | Implement the Python class `PrmDupliquer` described below.
Class description:
Commande 'rapport dupliquer'
Method signatures and docstrings:
- def __init__(self): Constructeur du paramètre.
- def interpreter(self, personnage, dic_masques): Méthode d'interprétation de commande | Implement the Python class `PrmDupliquer` described below.
Class description:
Commande 'rapport dupliquer'
Method signatures and docstrings:
- def __init__(self): Constructeur du paramètre.
- def interpreter(self, personnage, dic_masques): Méthode d'interprétation de commande
<|skeleton|>
class PrmDupliquer:
"""... | 7e93bff08cdf891352efba587e89c40f3b4a2301 | <|skeleton|>
class PrmDupliquer:
"""Commande 'rapport dupliquer'"""
def __init__(self):
"""Constructeur du paramètre."""
<|body_0|>
def interpreter(self, personnage, dic_masques):
"""Méthode d'interprétation de commande"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PrmDupliquer:
"""Commande 'rapport dupliquer'"""
def __init__(self):
"""Constructeur du paramètre."""
Parametre.__init__(self, 'dupliquer', 'duplicate')
self.nom_groupe = 'administrateur'
self.schema = '<nombre> <depuis:nombre>'
self.aide_courte = 'marque un rappor... | the_stack_v2_python_sparse | src/secondaires/rapport/commandes/rapport/dupliquer.py | vincent-lg/tsunami | train | 5 |
904ad16643d5390cce6f3eca7e91341367fa2cb0 | [
"num_classes = 10\ninputs = np.random.rand(2, input_size, input_size, 3)\ntf.keras.backend.set_image_data_format('channels_last')\nbackbone = backbones.ResNet(model_id=50)\ndecoder = fpn.FPN(input_specs=backbone.output_specs, min_level=2, max_level=7)\nhead = segmentation_heads.SegmentationHead(num_classes, level=l... | <|body_start_0|>
num_classes = 10
inputs = np.random.rand(2, input_size, input_size, 3)
tf.keras.backend.set_image_data_format('channels_last')
backbone = backbones.ResNet(model_id=50)
decoder = fpn.FPN(input_specs=backbone.output_specs, min_level=2, max_level=7)
head = s... | SegmentationNetworkTest | [
"Apache-2.0",
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SegmentationNetworkTest:
def test_segmentation_network_creation(self, input_size, level):
"""Test for creation of a segmentation network."""
<|body_0|>
def test_serialize_deserialize(self):
"""Validate the network can be serialized and deserialized."""
<|body... | stack_v2_sparse_classes_36k_train_027216 | 2,881 | permissive | [
{
"docstring": "Test for creation of a segmentation network.",
"name": "test_segmentation_network_creation",
"signature": "def test_segmentation_network_creation(self, input_size, level)"
},
{
"docstring": "Validate the network can be serialized and deserialized.",
"name": "test_serialize_de... | 2 | null | Implement the Python class `SegmentationNetworkTest` described below.
Class description:
Implement the SegmentationNetworkTest class.
Method signatures and docstrings:
- def test_segmentation_network_creation(self, input_size, level): Test for creation of a segmentation network.
- def test_serialize_deserialize(self)... | Implement the Python class `SegmentationNetworkTest` described below.
Class description:
Implement the SegmentationNetworkTest class.
Method signatures and docstrings:
- def test_segmentation_network_creation(self, input_size, level): Test for creation of a segmentation network.
- def test_serialize_deserialize(self)... | 6fc53292b1d3ce3c0340ce724c2c11c77e663d27 | <|skeleton|>
class SegmentationNetworkTest:
def test_segmentation_network_creation(self, input_size, level):
"""Test for creation of a segmentation network."""
<|body_0|>
def test_serialize_deserialize(self):
"""Validate the network can be serialized and deserialized."""
<|body... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SegmentationNetworkTest:
def test_segmentation_network_creation(self, input_size, level):
"""Test for creation of a segmentation network."""
num_classes = 10
inputs = np.random.rand(2, input_size, input_size, 3)
tf.keras.backend.set_image_data_format('channels_last')
ba... | the_stack_v2_python_sparse | models/official/vision/beta/modeling/segmentation_model_test.py | aboerzel/German_License_Plate_Recognition | train | 34 | |
71ea0b59ded2cafb3682c273ef8162b42e75a88c | [
"dummy = ListNode(-1)\ndummy.next = head\nprev_node = dummy\nwhile head and head.next:\n first_node = head\n second_node = head.next\n prev_node.next = second_node\n first_node.next = second_node.next\n second_node.next = first_node\n prev_node = first_node\n head = first_node.next\nreturn dumm... | <|body_start_0|>
dummy = ListNode(-1)
dummy.next = head
prev_node = dummy
while head and head.next:
first_node = head
second_node = head.next
prev_node.next = second_node
first_node.next = second_node.next
second_node.next = fir... | LinkedList | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LinkedList:
def swap_all_pairs(self, head: 'ListNode') -> 'ListNode':
"""Approach: Iterative Time Complexity: O(N) Space Complexity: O(1) :param head: :return:"""
<|body_0|>
def swap_pairs(self, head: 'ListNode') -> 'ListNode':
"""Approach: Recursion Time Complexity:... | stack_v2_sparse_classes_36k_train_027217 | 1,439 | no_license | [
{
"docstring": "Approach: Iterative Time Complexity: O(N) Space Complexity: O(1) :param head: :return:",
"name": "swap_all_pairs",
"signature": "def swap_all_pairs(self, head: 'ListNode') -> 'ListNode'"
},
{
"docstring": "Approach: Recursion Time Complexity: O(N) Space Complexity: O(N) :param he... | 2 | null | Implement the Python class `LinkedList` described below.
Class description:
Implement the LinkedList class.
Method signatures and docstrings:
- def swap_all_pairs(self, head: 'ListNode') -> 'ListNode': Approach: Iterative Time Complexity: O(N) Space Complexity: O(1) :param head: :return:
- def swap_pairs(self, head: ... | Implement the Python class `LinkedList` described below.
Class description:
Implement the LinkedList class.
Method signatures and docstrings:
- def swap_all_pairs(self, head: 'ListNode') -> 'ListNode': Approach: Iterative Time Complexity: O(N) Space Complexity: O(1) :param head: :return:
- def swap_pairs(self, head: ... | 65cc78b5afa0db064f9fe8f06597e3e120f7363d | <|skeleton|>
class LinkedList:
def swap_all_pairs(self, head: 'ListNode') -> 'ListNode':
"""Approach: Iterative Time Complexity: O(N) Space Complexity: O(1) :param head: :return:"""
<|body_0|>
def swap_pairs(self, head: 'ListNode') -> 'ListNode':
"""Approach: Recursion Time Complexity:... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class LinkedList:
def swap_all_pairs(self, head: 'ListNode') -> 'ListNode':
"""Approach: Iterative Time Complexity: O(N) Space Complexity: O(1) :param head: :return:"""
dummy = ListNode(-1)
dummy.next = head
prev_node = dummy
while head and head.next:
first_node =... | the_stack_v2_python_sparse | revisited/linked_list/swap_pairs_from_nodes.py | Shiv2157k/leet_code | train | 1 | |
876c4c5ad2e05d9566d11a448648a417ac961d7d | [
"request = self.context['request']\nLocationRelationContentTypeSerializer = location_relation_serializers['LocationRelationContentTypeSerializer']\nrelated_content = get_related_content(obj, LocationRelationContentTypeSerializer, obj.location_relation_relation, request)\nreturn related_content",
"request = self.c... | <|body_start_0|>
request = self.context['request']
LocationRelationContentTypeSerializer = location_relation_serializers['LocationRelationContentTypeSerializer']
related_content = get_related_content(obj, LocationRelationContentTypeSerializer, obj.location_relation_relation, request)
ret... | Serialize an object based on a provided field | LocationFieldMethodSerializer | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LocationFieldMethodSerializer:
"""Serialize an object based on a provided field"""
def get_climate_conditions(self, obj):
"""Get related object type data :param obj: Current record object :return: Related content type :rtype: Object/record"""
<|body_0|>
def get_soils(sel... | stack_v2_sparse_classes_36k_train_027218 | 7,095 | permissive | [
{
"docstring": "Get related object type data :param obj: Current record object :return: Related content type :rtype: Object/record",
"name": "get_climate_conditions",
"signature": "def get_climate_conditions(self, obj)"
},
{
"docstring": "Get related soil data :param obj: Current record object :... | 3 | stack_v2_sparse_classes_30k_train_012580 | Implement the Python class `LocationFieldMethodSerializer` described below.
Class description:
Serialize an object based on a provided field
Method signatures and docstrings:
- def get_climate_conditions(self, obj): Get related object type data :param obj: Current record object :return: Related content type :rtype: O... | Implement the Python class `LocationFieldMethodSerializer` described below.
Class description:
Serialize an object based on a provided field
Method signatures and docstrings:
- def get_climate_conditions(self, obj): Get related object type data :param obj: Current record object :return: Related content type :rtype: O... | 65a9c2b0f57828ca7e355018e1a6366e893ee836 | <|skeleton|>
class LocationFieldMethodSerializer:
"""Serialize an object based on a provided field"""
def get_climate_conditions(self, obj):
"""Get related object type data :param obj: Current record object :return: Related content type :rtype: Object/record"""
<|body_0|>
def get_soils(sel... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class LocationFieldMethodSerializer:
"""Serialize an object based on a provided field"""
def get_climate_conditions(self, obj):
"""Get related object type data :param obj: Current record object :return: Related content type :rtype: Object/record"""
request = self.context['request']
Loca... | the_stack_v2_python_sparse | csacompendium/locations/api/location/locationserializers.py | nkoech/csacompendium | train | 1 |
3d3ad799f7567edc33596bba82f96f319225ce12 | [
"if head == None or head.next == None:\n return False\nslow = head\nfast = head.next\nwhile slow != fast:\n if fast == None or fast.next == None:\n return False\n else:\n slow = slow.next\n fast = fast.next.next\nreturn True",
"table = {}\nwhile head != None:\n if head in table.ke... | <|body_start_0|>
if head == None or head.next == None:
return False
slow = head
fast = head.next
while slow != fast:
if fast == None or fast.next == None:
return False
else:
slow = slow.next
fast = fast.n... | Solution | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def hasCycle(self, head):
""":type head: ListNode :rtype: bool"""
<|body_0|>
def hasCycleHash(self, head):
""":type head: ListNode :rtype: bool"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
if head == None or head.next == None:
... | stack_v2_sparse_classes_36k_train_027219 | 1,205 | permissive | [
{
"docstring": ":type head: ListNode :rtype: bool",
"name": "hasCycle",
"signature": "def hasCycle(self, head)"
},
{
"docstring": ":type head: ListNode :rtype: bool",
"name": "hasCycleHash",
"signature": "def hasCycleHash(self, head)"
}
] | 2 | stack_v2_sparse_classes_30k_train_014857 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def hasCycle(self, head): :type head: ListNode :rtype: bool
- def hasCycleHash(self, head): :type head: ListNode :rtype: bool | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def hasCycle(self, head): :type head: ListNode :rtype: bool
- def hasCycleHash(self, head): :type head: ListNode :rtype: bool
<|skeleton|>
class Solution:
def hasCycle(self... | 0e4af391274e33a9bb9f999a9032b74d06fc878e | <|skeleton|>
class Solution:
def hasCycle(self, head):
""":type head: ListNode :rtype: bool"""
<|body_0|>
def hasCycleHash(self, head):
""":type head: ListNode :rtype: bool"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def hasCycle(self, head):
""":type head: ListNode :rtype: bool"""
if head == None or head.next == None:
return False
slow = head
fast = head.next
while slow != fast:
if fast == None or fast.next == None:
return False
... | the_stack_v2_python_sparse | leetcode/link-list/cycle.py | Gaurav-Pande/DataStructures | train | 6 | |
956f9e60f465d4f1a98167191b3b818c37ad776d | [
"super().__init__(*args, **kwargs)\nif ext_args == {}:\n self._freq = 1\nelse:\n self._freq = ext_args.freq",
"if engine.rank == 0 and engine.last_iter.val % self._freq == 0:\n dirname = './ckpt_{}'.format(engine.name)\n if not os.path.exists(dirname):\n try:\n os.mkdir(dirname)\n ... | <|body_start_0|>
super().__init__(*args, **kwargs)
if ext_args == {}:
self._freq = 1
else:
self._freq = ext_args.freq
<|end_body_0|>
<|body_start_1|>
if engine.rank == 0 and engine.last_iter.val % self._freq == 0:
dirname = './ckpt_{}'.format(engine.n... | Overview: Hook to save checkpoint Interfaces: __init__, __call__ Property: name, priority, position | SaveCkptHook | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SaveCkptHook:
"""Overview: Hook to save checkpoint Interfaces: __init__, __call__ Property: name, priority, position"""
def __init__(self, *args, ext_args: EasyDict=EasyDict(), **kwargs) -> None:
"""Overview: init SaveCkptHook Arguments: - ext_args (:obj:`EasyDict`): extended_args, u... | stack_v2_sparse_classes_36k_train_027220 | 15,244 | permissive | [
{
"docstring": "Overview: init SaveCkptHook Arguments: - ext_args (:obj:`EasyDict`): extended_args, use ext_args.freq to set save_ckpt_freq",
"name": "__init__",
"signature": "def __init__(self, *args, ext_args: EasyDict=EasyDict(), **kwargs) -> None"
},
{
"docstring": "Overview: Save checkpoint... | 2 | stack_v2_sparse_classes_30k_train_000347 | Implement the Python class `SaveCkptHook` described below.
Class description:
Overview: Hook to save checkpoint Interfaces: __init__, __call__ Property: name, priority, position
Method signatures and docstrings:
- def __init__(self, *args, ext_args: EasyDict=EasyDict(), **kwargs) -> None: Overview: init SaveCkptHook ... | Implement the Python class `SaveCkptHook` described below.
Class description:
Overview: Hook to save checkpoint Interfaces: __init__, __call__ Property: name, priority, position
Method signatures and docstrings:
- def __init__(self, *args, ext_args: EasyDict=EasyDict(), **kwargs) -> None: Overview: init SaveCkptHook ... | eb483fa6e46602d58c8e7d2ca1e566adca28e703 | <|skeleton|>
class SaveCkptHook:
"""Overview: Hook to save checkpoint Interfaces: __init__, __call__ Property: name, priority, position"""
def __init__(self, *args, ext_args: EasyDict=EasyDict(), **kwargs) -> None:
"""Overview: init SaveCkptHook Arguments: - ext_args (:obj:`EasyDict`): extended_args, u... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SaveCkptHook:
"""Overview: Hook to save checkpoint Interfaces: __init__, __call__ Property: name, priority, position"""
def __init__(self, *args, ext_args: EasyDict=EasyDict(), **kwargs) -> None:
"""Overview: init SaveCkptHook Arguments: - ext_args (:obj:`EasyDict`): extended_args, use ext_args.f... | the_stack_v2_python_sparse | ding/worker/learner/learner_hook.py | shengxuesun/DI-engine | train | 1 |
241bb6da6f869f43c7ba6e691e28c397a370ff40 | [
"def serializeHelper(node, vals):\n if node:\n vals.append(node.val)\n serializeHelper(node.left, vals)\n serializeHelper(node.right, vals)\nvals = []\nserializeHelper(root, vals)\nreturn ' '.join(map(str, vals))",
"def deserializeHelper(minVal, maxVal, vals):\n if not vals:\n re... | <|body_start_0|>
def serializeHelper(node, vals):
if node:
vals.append(node.val)
serializeHelper(node.left, vals)
serializeHelper(node.right, vals)
vals = []
serializeHelper(root, vals)
return ' '.join(map(str, vals))
<|end_body... | Codec | [
"MIT"
] | 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_027221 | 1,344 | permissive | [
{
"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_020447 | 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:... | 4dc4e6642dc92f1983c13564cc0fd99917cab358 | <|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 serializeHelper(node, vals):
if node:
vals.append(node.val)
serializeHelper(node.left, vals)
serializeHelper(node.right, v... | the_stack_v2_python_sparse | Python/serialize-and-deserialize-bst.py | kamyu104/LeetCode-Solutions | train | 4,549 | |
a394f74e40675c97b862ff3546411770a3c39ffd | [
"self.ri = ROACHInterface(pid)\nself.ri.set_coeffs(coeffs)\nself.host = host\nself.port = port",
"sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\nsock.connect((self.host, self.port))\ntry:\n packetSize = 4096\n while True:\n self.ri.trig_data_bram()\n time.sleep(0.01)\n output ... | <|body_start_0|>
self.ri = ROACHInterface(pid)
self.ri.set_coeffs(coeffs)
self.host = host
self.port = port
<|end_body_0|>
<|body_start_1|>
sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
sock.connect((self.host, self.port))
try:
packetSize = ... | @brief a class to read data from the ROACH and transmit to a client via User Datagram Protocol (UDP) | transmitUDP | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class transmitUDP:
"""@brief a class to read data from the ROACH and transmit to a client via User Datagram Protocol (UDP)"""
def __init__(self, pid, host='192.168.1.101', port=12345, coeffs=[0, 0, 0, 0]):
"""@brief initialize the class, given a process ID, host and port @param pid: the pr... | stack_v2_sparse_classes_36k_train_027222 | 7,870 | no_license | [
{
"docstring": "@brief initialize the class, given a process ID, host and port @param pid: the process ID of the running BOF file (int) @param host: the IP address of the client to transmit to (str) @param port: the port over which to communicate (int)",
"name": "__init__",
"signature": "def __init__(se... | 3 | stack_v2_sparse_classes_30k_train_021617 | Implement the Python class `transmitUDP` described below.
Class description:
@brief a class to read data from the ROACH and transmit to a client via User Datagram Protocol (UDP)
Method signatures and docstrings:
- def __init__(self, pid, host='192.168.1.101', port=12345, coeffs=[0, 0, 0, 0]): @brief initialize the cl... | Implement the Python class `transmitUDP` described below.
Class description:
@brief a class to read data from the ROACH and transmit to a client via User Datagram Protocol (UDP)
Method signatures and docstrings:
- def __init__(self, pid, host='192.168.1.101', port=12345, coeffs=[0, 0, 0, 0]): @brief initialize the cl... | b96245b703618d309eb19cf965f8a9e1add4264e | <|skeleton|>
class transmitUDP:
"""@brief a class to read data from the ROACH and transmit to a client via User Datagram Protocol (UDP)"""
def __init__(self, pid, host='192.168.1.101', port=12345, coeffs=[0, 0, 0, 0]):
"""@brief initialize the class, given a process ID, host and port @param pid: the pr... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class transmitUDP:
"""@brief a class to read data from the ROACH and transmit to a client via User Datagram Protocol (UDP)"""
def __init__(self, pid, host='192.168.1.101', port=12345, coeffs=[0, 0, 0, 0]):
"""@brief initialize the class, given a process ID, host and port @param pid: the process ID of t... | the_stack_v2_python_sparse | nickhand/python/fir_filter_udp_tx.py | AaronParsons/astro250 | train | 4 |
aa3e244372d5360abcc594cdcc213108b839aaf2 | [
"self.engine = engine\nself.identity_name = identity_name\nself.job_id = job_id\nself._update_lock = threading.Lock()\nself.public_stickers = {}\nself.private_stickers = {}\nif public_stickers and isinstance(public_stickers, dict):\n self.public_stickers.update(public_stickers)\nif private_stickers and isinstanc... | <|body_start_0|>
self.engine = engine
self.identity_name = identity_name
self.job_id = job_id
self._update_lock = threading.Lock()
self.public_stickers = {}
self.private_stickers = {}
if public_stickers and isinstance(public_stickers, dict):
self.publi... | FLContextManager manages the creation and updates of FLContext objects for a run. NOTE: The engine may create a new FLContextManager object for each RUN! | FLContextManager | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FLContextManager:
"""FLContextManager manages the creation and updates of FLContext objects for a run. NOTE: The engine may create a new FLContextManager object for each RUN!"""
def __init__(self, engine=None, identity_name: str='', job_id: str='', public_stickers=None, private_stickers=None... | stack_v2_sparse_classes_36k_train_027223 | 12,075 | permissive | [
{
"docstring": "Init the FLContextManager. Args: engine: the engine that created this FLContextManager object identity_name (str): identity name job_id: the job id public_stickers: public sticky properties that are copied into or copied from private_stickers: private sticky properties that are copied into or co... | 5 | null | Implement the Python class `FLContextManager` described below.
Class description:
FLContextManager manages the creation and updates of FLContext objects for a run. NOTE: The engine may create a new FLContextManager object for each RUN!
Method signatures and docstrings:
- def __init__(self, engine=None, identity_name:... | Implement the Python class `FLContextManager` described below.
Class description:
FLContextManager manages the creation and updates of FLContext objects for a run. NOTE: The engine may create a new FLContextManager object for each RUN!
Method signatures and docstrings:
- def __init__(self, engine=None, identity_name:... | 1433290c203bd23f34c29e11795ce592bc067888 | <|skeleton|>
class FLContextManager:
"""FLContextManager manages the creation and updates of FLContext objects for a run. NOTE: The engine may create a new FLContextManager object for each RUN!"""
def __init__(self, engine=None, identity_name: str='', job_id: str='', public_stickers=None, private_stickers=None... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class FLContextManager:
"""FLContextManager manages the creation and updates of FLContext objects for a run. NOTE: The engine may create a new FLContextManager object for each RUN!"""
def __init__(self, engine=None, identity_name: str='', job_id: str='', public_stickers=None, private_stickers=None):
""... | the_stack_v2_python_sparse | nvflare/apis/fl_context.py | NVIDIA/NVFlare | train | 442 |
c6f948787986b71d67e4e784efde83a8339742c4 | [
"self.n = 5\nself.m = 2\nself.L = L",
"a = u[0]\nphidot = u[1]\ntheta = x[2]\nv = x[3]\nphi = x[4]\nL = self.L\nreturn np.array([v * np.cos(theta), v * np.sin(theta), v * np.tan(phi) / L, a, phidot]) + w",
"theta = x[2]\nv = x[3]\nphi = x[4]\nL = self.L\nfx = np.array([[0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [-v * np... | <|body_start_0|>
self.n = 5
self.m = 2
self.L = L
<|end_body_0|>
<|body_start_1|>
a = u[0]
phidot = u[1]
theta = x[2]
v = x[3]
phi = x[4]
L = self.L
return np.array([v * np.cos(theta), v * np.sin(theta), v * np.tan(phi) / L, a, phidot]) + ... | Simple car dynamics with controls as acceleration, steering rate | SimpleCarDynamics | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SimpleCarDynamics:
"""Simple car dynamics with controls as acceleration, steering rate"""
def __init__(self, L=1.0):
"""Constructor that initializes the order"""
<|body_0|>
def xdot(self, t, x, u, w):
"""x = [x, y, theta, v, phi] Basic dynamics of xdot = [v c_the... | stack_v2_sparse_classes_36k_train_027224 | 1,434 | permissive | [
{
"docstring": "Constructor that initializes the order",
"name": "__init__",
"signature": "def __init__(self, L=1.0)"
},
{
"docstring": "x = [x, y, theta, v, phi] Basic dynamics of xdot = [v c_theta, v sin_theta, v tan_phi/L, a, phidot] u = [a, phidot]",
"name": "xdot",
"signature": "def... | 3 | stack_v2_sparse_classes_30k_train_019646 | Implement the Python class `SimpleCarDynamics` described below.
Class description:
Simple car dynamics with controls as acceleration, steering rate
Method signatures and docstrings:
- def __init__(self, L=1.0): Constructor that initializes the order
- def xdot(self, t, x, u, w): x = [x, y, theta, v, phi] Basic dynami... | Implement the Python class `SimpleCarDynamics` described below.
Class description:
Simple car dynamics with controls as acceleration, steering rate
Method signatures and docstrings:
- def __init__(self, L=1.0): Constructor that initializes the order
- def xdot(self, t, x, u, w): x = [x, y, theta, v, phi] Basic dynami... | 4faa05317fb895575bc1b943d919e54355d56280 | <|skeleton|>
class SimpleCarDynamics:
"""Simple car dynamics with controls as acceleration, steering rate"""
def __init__(self, L=1.0):
"""Constructor that initializes the order"""
<|body_0|>
def xdot(self, t, x, u, w):
"""x = [x, y, theta, v, phi] Basic dynamics of xdot = [v c_the... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SimpleCarDynamics:
"""Simple car dynamics with controls as acceleration, steering rate"""
def __init__(self, L=1.0):
"""Constructor that initializes the order"""
self.n = 5
self.m = 2
self.L = L
def xdot(self, t, x, u, w):
"""x = [x, y, theta, v, phi] Basic dy... | the_stack_v2_python_sparse | src/ddp_sim_car_model/scripts/simple_car_dynamics.py | lipengfeizju/aoc_project | train | 0 |
24eda61b4eb9bf82940f548487f7ff09425ca867 | [
"if minfo is None:\n minfo = {}\nsuper(DumpJournalValueMessage, self).__init__(minfo)\nself.IsSystemMessage = False\nself.IsForward = True\nself.IsReliable = True\nself.TransactionType = minfo.get('TransactionType')\nself.Name = minfo.get('Name')",
"result = super(DumpJournalValueMessage, self).dump()\nresult[... | <|body_start_0|>
if minfo is None:
minfo = {}
super(DumpJournalValueMessage, self).__init__(minfo)
self.IsSystemMessage = False
self.IsForward = True
self.IsReliable = True
self.TransactionType = minfo.get('TransactionType')
self.Name = minfo.get('Name... | Represents the message format for exchanging dump journal value messages. Attributes: DumpJournalValueMessage.MessageType (str): The class name of the message. IsSystemMessage (bool): Whether or not this message is a system message. IsForward (bool): Whether or not this message is forwarded. IsReliable (bool): Whether ... | DumpJournalValueMessage | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DumpJournalValueMessage:
"""Represents the message format for exchanging dump journal value messages. Attributes: DumpJournalValueMessage.MessageType (str): The class name of the message. IsSystemMessage (bool): Whether or not this message is a system message. IsForward (bool): Whether or not thi... | stack_v2_sparse_classes_36k_train_027225 | 5,835 | permissive | [
{
"docstring": "Constructor for the DumpJournalValueMessage class. Args: minfo (dict): A dict containing initial values for the new DumpJournalValueMessage.",
"name": "__init__",
"signature": "def __init__(self, minfo=None)"
},
{
"docstring": "Returns a dict containing information about the Dump... | 2 | stack_v2_sparse_classes_30k_train_007638 | Implement the Python class `DumpJournalValueMessage` described below.
Class description:
Represents the message format for exchanging dump journal value messages. Attributes: DumpJournalValueMessage.MessageType (str): The class name of the message. IsSystemMessage (bool): Whether or not this message is a system messag... | Implement the Python class `DumpJournalValueMessage` described below.
Class description:
Represents the message format for exchanging dump journal value messages. Attributes: DumpJournalValueMessage.MessageType (str): The class name of the message. IsSystemMessage (bool): Whether or not this message is a system messag... | 8f4ca1aab54ef420a0db10c8ca822ec8686cd423 | <|skeleton|>
class DumpJournalValueMessage:
"""Represents the message format for exchanging dump journal value messages. Attributes: DumpJournalValueMessage.MessageType (str): The class name of the message. IsSystemMessage (bool): Whether or not this message is a system message. IsForward (bool): Whether or not thi... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DumpJournalValueMessage:
"""Represents the message format for exchanging dump journal value messages. Attributes: DumpJournalValueMessage.MessageType (str): The class name of the message. IsSystemMessage (bool): Whether or not this message is a system message. IsForward (bool): Whether or not this message is ... | the_stack_v2_python_sparse | validator/journal/messages/journal_debug.py | aludvik/sawtooth-core | train | 0 |
192e405449a4080e6b982be91eee43b39b9f9515 | [
"self.code_value = None\nself.display_text = None\nself.abbreviation_text = None\nself.info_xml = None\nif xml is not None:\n self.parse_xml(xml)",
"xmlutils = XmlUtils(xml)\nself.code_value = xmlutils.get_string_by_xpath('code-value')\nself.display_text = xmlutils.get_string_by_xpath('display-text')\nself.abb... | <|body_start_0|>
self.code_value = None
self.display_text = None
self.abbreviation_text = None
self.info_xml = None
if xml is not None:
self.parse_xml(xml)
<|end_body_0|>
<|body_start_1|>
xmlutils = XmlUtils(xml)
self.code_value = xmlutils.get_string_... | Describes an indivdual code item. Attributes: code_value This is the value associated with the item which uniquelyidentifies it within a vocabulary. display_text This is the display text of the item. abbreviation_text This is the abbreviation text of an item. info_xml Contains important auxillary information that can b... | VocabularyCodeItem | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class VocabularyCodeItem:
"""Describes an indivdual code item. Attributes: code_value This is the value associated with the item which uniquelyidentifies it within a vocabulary. display_text This is the display text of the item. abbreviation_text This is the abbreviation text of an item. info_xml Conta... | stack_v2_sparse_classes_36k_train_027226 | 1,487 | permissive | [
{
"docstring": ":param xml: lxml.etree.Element representing a single VocabularyItem",
"name": "__init__",
"signature": "def __init__(self, xml=None)"
},
{
"docstring": ":param xml: lxml.etree.Element representing a single VocabularyItem",
"name": "parse_xml",
"signature": "def parse_xml(... | 2 | stack_v2_sparse_classes_30k_train_013471 | Implement the Python class `VocabularyCodeItem` described below.
Class description:
Describes an indivdual code item. Attributes: code_value This is the value associated with the item which uniquelyidentifies it within a vocabulary. display_text This is the display text of the item. abbreviation_text This is the abbre... | Implement the Python class `VocabularyCodeItem` described below.
Class description:
Describes an indivdual code item. Attributes: code_value This is the value associated with the item which uniquelyidentifies it within a vocabulary. display_text This is the display text of the item. abbreviation_text This is the abbre... | 2b6fa7c1687300bcc2e501368883fbb13dc80495 | <|skeleton|>
class VocabularyCodeItem:
"""Describes an indivdual code item. Attributes: code_value This is the value associated with the item which uniquelyidentifies it within a vocabulary. display_text This is the display text of the item. abbreviation_text This is the abbreviation text of an item. info_xml Conta... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class VocabularyCodeItem:
"""Describes an indivdual code item. Attributes: code_value This is the value associated with the item which uniquelyidentifies it within a vocabulary. display_text This is the display text of the item. abbreviation_text This is the abbreviation text of an item. info_xml Contains important... | the_stack_v2_python_sparse | src/healthvaultlib/objects/vocabularycodeitem.py | rajeevs1992/pyhealthvault | train | 1 |
7f4a5857020dcfd981b4c7051abc58e4bf672be0 | [
"requesition_obj = self.pool.get('purchase.requisition')\nrequesition = requesition_obj.browse(cr, uid, context['request_id'])\nsubject = 'RFQ For ' + requesition.name\nir_mail = self.pool.get('ir.mail_server')\npartner_obj = self.pool.get('res.partner')\npartner_ids = requesition_obj.get_partner_ids(cr, uid, ids, ... | <|body_start_0|>
requesition_obj = self.pool.get('purchase.requisition')
requesition = requesition_obj.browse(cr, uid, context['request_id'])
subject = 'RFQ For ' + requesition.name
ir_mail = self.pool.get('ir.mail_server')
partner_obj = self.pool.get('res.partner')
partn... | purchase_send_email_quotation_wizard | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class purchase_send_email_quotation_wizard:
def send_message(self, cr, uid, ids, context=None):
"""This Method for send Email For Suppliers"""
<|body_0|>
def default_get(self, cr, uid, fields, context=None):
"""To get default values for the object. @return: A dictionary wh... | stack_v2_sparse_classes_36k_train_027227 | 3,800 | no_license | [
{
"docstring": "This Method for send Email For Suppliers",
"name": "send_message",
"signature": "def send_message(self, cr, uid, ids, context=None)"
},
{
"docstring": "To get default values for the object. @return: A dictionary which of fields with values.",
"name": "default_get",
"signa... | 2 | null | Implement the Python class `purchase_send_email_quotation_wizard` described below.
Class description:
Implement the purchase_send_email_quotation_wizard class.
Method signatures and docstrings:
- def send_message(self, cr, uid, ids, context=None): This Method for send Email For Suppliers
- def default_get(self, cr, u... | Implement the Python class `purchase_send_email_quotation_wizard` described below.
Class description:
Implement the purchase_send_email_quotation_wizard class.
Method signatures and docstrings:
- def send_message(self, cr, uid, ids, context=None): This Method for send Email For Suppliers
- def default_get(self, cr, u... | 0b997095c260d58b026440967fea3a202bef7efb | <|skeleton|>
class purchase_send_email_quotation_wizard:
def send_message(self, cr, uid, ids, context=None):
"""This Method for send Email For Suppliers"""
<|body_0|>
def default_get(self, cr, uid, fields, context=None):
"""To get default values for the object. @return: A dictionary wh... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class purchase_send_email_quotation_wizard:
def send_message(self, cr, uid, ids, context=None):
"""This Method for send Email For Suppliers"""
requesition_obj = self.pool.get('purchase.requisition')
requesition = requesition_obj.browse(cr, uid, context['request_id'])
subject = 'RFQ F... | the_stack_v2_python_sparse | v_7/Dongola/ntc/purchase_send_email_quotation/wizard/purchase_send_email_quotation_wizard.py | musabahmed/baba | train | 0 | |
72bf3ca78c47c383c695c4644f4b366813895f9d | [
"if bool(vocab_path) == bool(spm_model_path):\n raise ValueError('Exactly 1 of `vocab_path` or `spm_model_path` must be specified, not both.')\nself.vocab_path = vocab_path\nself.do_lower_case = do_lower_case\nself.spm_model_path = spm_model_path\nself.generate_document_ids = generate_document_ids",
"def file_... | <|body_start_0|>
if bool(vocab_path) == bool(spm_model_path):
raise ValueError('Exactly 1 of `vocab_path` or `spm_model_path` must be specified, not both.')
self.vocab_path = vocab_path
self.do_lower_case = do_lower_case
self.spm_model_path = spm_model_path
self.gener... | PTransform for reading text files into tokenized documents. | ReadFilesToTokenizedDocuments | [
"Apache-2.0",
"CC-BY-4.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ReadFilesToTokenizedDocuments:
"""PTransform for reading text files into tokenized documents."""
def __init__(self, vocab_path=None, do_lower_case=True, spm_model_path=None, generate_document_ids=False):
"""Initialization. Args: vocab_path: Path to the BERT vocabulary file to use wit... | stack_v2_sparse_classes_36k_train_027228 | 24,441 | permissive | [
{
"docstring": "Initialization. Args: vocab_path: Path to the BERT vocabulary file to use with the BERT tokenizer. Leave as None or set to empty string if using `spm_model_path` instead. do_lower_case: Whether to lowercase all text for BERT tokenization (default True). Must match assumption in `vocab_path`. Ign... | 2 | stack_v2_sparse_classes_30k_train_020227 | Implement the Python class `ReadFilesToTokenizedDocuments` described below.
Class description:
PTransform for reading text files into tokenized documents.
Method signatures and docstrings:
- def __init__(self, vocab_path=None, do_lower_case=True, spm_model_path=None, generate_document_ids=False): Initialization. Args... | Implement the Python class `ReadFilesToTokenizedDocuments` described below.
Class description:
PTransform for reading text files into tokenized documents.
Method signatures and docstrings:
- def __init__(self, vocab_path=None, do_lower_case=True, spm_model_path=None, generate_document_ids=False): Initialization. Args... | 5573d9c5822f4e866b6692769963ae819cb3f10d | <|skeleton|>
class ReadFilesToTokenizedDocuments:
"""PTransform for reading text files into tokenized documents."""
def __init__(self, vocab_path=None, do_lower_case=True, spm_model_path=None, generate_document_ids=False):
"""Initialization. Args: vocab_path: Path to the BERT vocabulary file to use wit... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ReadFilesToTokenizedDocuments:
"""PTransform for reading text files into tokenized documents."""
def __init__(self, vocab_path=None, do_lower_case=True, spm_model_path=None, generate_document_ids=False):
"""Initialization. Args: vocab_path: Path to the BERT vocabulary file to use with the BERT to... | the_stack_v2_python_sparse | readtwice/data_utils/beam_utils.py | Jimmy-INL/google-research | train | 1 |
453b540ab2ea15c18dd1b44086fd04c9aa595456 | [
"self.auto_lock_duration_usecs = auto_lock_duration_usecs\nself.coexisting_lock_mode = coexisting_lock_mode\nself.default_retention_duration_usecs = default_retention_duration_usecs\nself.default_retention_duration_years = default_retention_duration_years\nself.hold_timestamp_usecs = hold_timestamp_usecs\nself.igno... | <|body_start_0|>
self.auto_lock_duration_usecs = auto_lock_duration_usecs
self.coexisting_lock_mode = coexisting_lock_mode
self.default_retention_duration_usecs = default_retention_duration_usecs
self.default_retention_duration_years = default_retention_duration_years
self.hold_t... | Implementation of the 'ViewIdMappingProto_FileLevelDataLockConfig' model. TODO: type description here. Attributes: auto_lock_duration_usecs (long|int): Auto-lock automatically commit files to WORM state in the filesystem if they have not been modified for an administrator-specified period of time. When the auto-lock is... | ViewIdMappingProto_FileLevelDataLockConfig | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ViewIdMappingProto_FileLevelDataLockConfig:
"""Implementation of the 'ViewIdMappingProto_FileLevelDataLockConfig' model. TODO: type description here. Attributes: auto_lock_duration_usecs (long|int): Auto-lock automatically commit files to WORM state in the filesystem if they have not been modifie... | stack_v2_sparse_classes_36k_train_027229 | 7,275 | permissive | [
{
"docstring": "Constructor for the ViewIdMappingProto_FileLevelDataLockConfig class",
"name": "__init__",
"signature": "def __init__(self, auto_lock_duration_usecs=None, coexisting_lock_mode=None, default_retention_duration_usecs=None, default_retention_duration_years=None, hold_timestamp_usecs=None, i... | 2 | null | Implement the Python class `ViewIdMappingProto_FileLevelDataLockConfig` described below.
Class description:
Implementation of the 'ViewIdMappingProto_FileLevelDataLockConfig' model. TODO: type description here. Attributes: auto_lock_duration_usecs (long|int): Auto-lock automatically commit files to WORM state in the f... | Implement the Python class `ViewIdMappingProto_FileLevelDataLockConfig` described below.
Class description:
Implementation of the 'ViewIdMappingProto_FileLevelDataLockConfig' model. TODO: type description here. Attributes: auto_lock_duration_usecs (long|int): Auto-lock automatically commit files to WORM state in the f... | e4973dfeb836266904d0369ea845513c7acf261e | <|skeleton|>
class ViewIdMappingProto_FileLevelDataLockConfig:
"""Implementation of the 'ViewIdMappingProto_FileLevelDataLockConfig' model. TODO: type description here. Attributes: auto_lock_duration_usecs (long|int): Auto-lock automatically commit files to WORM state in the filesystem if they have not been modifie... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ViewIdMappingProto_FileLevelDataLockConfig:
"""Implementation of the 'ViewIdMappingProto_FileLevelDataLockConfig' model. TODO: type description here. Attributes: auto_lock_duration_usecs (long|int): Auto-lock automatically commit files to WORM state in the filesystem if they have not been modified for an admi... | the_stack_v2_python_sparse | cohesity_management_sdk/models/view_id_mapping_proto_file_level_data_lock_config.py | cohesity/management-sdk-python | train | 24 |
882f05fee0ca61119a447aa523b611feae24f2c6 | [
"if len(index) == 0:\n return ''\nelif len(index) == 1:\n return str(index[0])\nelse:\n return str(index[1])",
"if len(index) == 0:\n return 10\nelif len(index) == 1:\n return 3000\nelse:\n return 0"
] | <|body_start_0|>
if len(index) == 0:
return ''
elif len(index) == 1:
return str(index[0])
else:
return str(index[1])
<|end_body_0|>
<|body_start_1|>
if len(index) == 0:
return 10
elif len(index) == 1:
return 3000
... | Virtual tree control | MyTree | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MyTree:
"""Virtual tree control"""
def OnGetItemText(self, index):
"""index is tuple of 0-based (root, child, child, ...) indices. An empty tuple () represents the hidden root item"""
<|body_0|>
def OnGetChildrenCount(self, index):
"""index is tuple of 0-based (r... | stack_v2_sparse_classes_36k_train_027230 | 1,354 | permissive | [
{
"docstring": "index is tuple of 0-based (root, child, child, ...) indices. An empty tuple () represents the hidden root item",
"name": "OnGetItemText",
"signature": "def OnGetItemText(self, index)"
},
{
"docstring": "index is tuple of 0-based (root, child, child, ...) indices. An empty tuple (... | 2 | stack_v2_sparse_classes_30k_train_013162 | Implement the Python class `MyTree` described below.
Class description:
Virtual tree control
Method signatures and docstrings:
- def OnGetItemText(self, index): index is tuple of 0-based (root, child, child, ...) indices. An empty tuple () represents the hidden root item
- def OnGetChildrenCount(self, index): index i... | Implement the Python class `MyTree` described below.
Class description:
Virtual tree control
Method signatures and docstrings:
- def OnGetItemText(self, index): index is tuple of 0-based (root, child, child, ...) indices. An empty tuple () represents the hidden root item
- def OnGetChildrenCount(self, index): index i... | 7d49a4c7841fe6611591cff2dc49586e079831e5 | <|skeleton|>
class MyTree:
"""Virtual tree control"""
def OnGetItemText(self, index):
"""index is tuple of 0-based (root, child, child, ...) indices. An empty tuple () represents the hidden root item"""
<|body_0|>
def OnGetChildrenCount(self, index):
"""index is tuple of 0-based (r... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MyTree:
"""Virtual tree control"""
def OnGetItemText(self, index):
"""index is tuple of 0-based (root, child, child, ...) indices. An empty tuple () represents the hidden root item"""
if len(index) == 0:
return ''
elif len(index) == 1:
return str(index[0])
... | the_stack_v2_python_sparse | demo/virtualtree.py | spyke/spyke | train | 24 |
6de0436abd47ba94fac9bb05fdbe77550bf7c91f | [
"self.column_names: List[str] = kargs.pop('column_names')\nsuper().__init__(*args, **kargs)\nself.set_fields_from_dict(['item_column', 'confirm_items', 'send_confirmation', 'track_read'])\nitem_column_name = self.fields['item_column'].initial\nif item_column_name is None:\n item_column_name = next((cname for cna... | <|body_start_0|>
self.column_names: List[str] = kargs.pop('column_names')
super().__init__(*args, **kargs)
self.set_fields_from_dict(['item_column', 'confirm_items', 'send_confirmation', 'track_read'])
item_column_name = self.fields['item_column'].initial
if item_column_name is N... | Form to edit the Send Email action. | EmailActionForm | [
"MIT",
"LGPL-2.0-or-later",
"Python-2.0",
"BSD-3-Clause",
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class EmailActionForm:
"""Form to edit the Send Email action."""
def __init__(self, *args, **kargs):
"""Store column names and adjust initial values."""
<|body_0|>
def clean(self):
"""Verify email values."""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
... | stack_v2_sparse_classes_36k_train_027231 | 20,237 | permissive | [
{
"docstring": "Store column names and adjust initial values.",
"name": "__init__",
"signature": "def __init__(self, *args, **kargs)"
},
{
"docstring": "Verify email values.",
"name": "clean",
"signature": "def clean(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_012499 | Implement the Python class `EmailActionForm` described below.
Class description:
Form to edit the Send Email action.
Method signatures and docstrings:
- def __init__(self, *args, **kargs): Store column names and adjust initial values.
- def clean(self): Verify email values. | Implement the Python class `EmailActionForm` described below.
Class description:
Form to edit the Send Email action.
Method signatures and docstrings:
- def __init__(self, *args, **kargs): Store column names and adjust initial values.
- def clean(self): Verify email values.
<|skeleton|>
class EmailActionForm:
""... | 5473e9faa24c71a2a1102d47ebc2cbf27608e42a | <|skeleton|>
class EmailActionForm:
"""Form to edit the Send Email action."""
def __init__(self, *args, **kargs):
"""Store column names and adjust initial values."""
<|body_0|>
def clean(self):
"""Verify email values."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class EmailActionForm:
"""Form to edit the Send Email action."""
def __init__(self, *args, **kargs):
"""Store column names and adjust initial values."""
self.column_names: List[str] = kargs.pop('column_names')
super().__init__(*args, **kargs)
self.set_fields_from_dict(['item_col... | the_stack_v2_python_sparse | ontask/action/forms/run.py | LucasFranciscoCorreia/ontask_b | train | 0 |
8fc8de9e109627902332dfd948543c191d9b840c | [
"super().__init__()\nself.nikola = nikola\nself.quiet = quiet",
"if self.quiet:\n doit_config = {'verbosity': 0, 'reporter': 'zero'}\nelse:\n doit_config = {'reporter': ExecutedOnlyReporter, 'outfile': sys.stderr}\ndoit_config['default_tasks'] = ['render_site', 'post_render']\ndoit_config.update(self.nikola... | <|body_start_0|>
super().__init__()
self.nikola = nikola
self.quiet = quiet
<|end_body_0|>
<|body_start_1|>
if self.quiet:
doit_config = {'verbosity': 0, 'reporter': 'zero'}
else:
doit_config = {'reporter': ExecutedOnlyReporter, 'outfile': sys.stderr}
... | Nikola-specific task loader. | NikolaTaskLoader | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class NikolaTaskLoader:
"""Nikola-specific task loader."""
def __init__(self, nikola, quiet=False):
"""Initialize the loader."""
<|body_0|>
def load_doit_config(self):
"""Load doit configuration."""
<|body_1|>
def load_tasks(self, cmd, pos_args):
"... | stack_v2_sparse_classes_36k_train_027232 | 16,687 | permissive | [
{
"docstring": "Initialize the loader.",
"name": "__init__",
"signature": "def __init__(self, nikola, quiet=False)"
},
{
"docstring": "Load doit configuration.",
"name": "load_doit_config",
"signature": "def load_doit_config(self)"
},
{
"docstring": "Load Nikola tasks.",
"nam... | 3 | null | Implement the Python class `NikolaTaskLoader` described below.
Class description:
Nikola-specific task loader.
Method signatures and docstrings:
- def __init__(self, nikola, quiet=False): Initialize the loader.
- def load_doit_config(self): Load doit configuration.
- def load_tasks(self, cmd, pos_args): Load Nikola t... | Implement the Python class `NikolaTaskLoader` described below.
Class description:
Nikola-specific task loader.
Method signatures and docstrings:
- def __init__(self, nikola, quiet=False): Initialize the loader.
- def load_doit_config(self): Load doit configuration.
- def load_tasks(self, cmd, pos_args): Load Nikola t... | 2b10e9952bac5a1119e6845c7a2c28273aca9775 | <|skeleton|>
class NikolaTaskLoader:
"""Nikola-specific task loader."""
def __init__(self, nikola, quiet=False):
"""Initialize the loader."""
<|body_0|>
def load_doit_config(self):
"""Load doit configuration."""
<|body_1|>
def load_tasks(self, cmd, pos_args):
"... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class NikolaTaskLoader:
"""Nikola-specific task loader."""
def __init__(self, nikola, quiet=False):
"""Initialize the loader."""
super().__init__()
self.nikola = nikola
self.quiet = quiet
def load_doit_config(self):
"""Load doit configuration."""
if self.qui... | the_stack_v2_python_sparse | nikola/__main__.py | getnikola/nikola | train | 2,142 |
80a78379f029f5c9b321a37559c7cf8169ba0538 | [
"super().__init__()\nself.n_guesses = n_guesses\nself.queues = queues\nself.logger = logging.getLogger(self.__class__.__name__)",
"self.queues.send_inputs(uniform(0, 10))\nself.logger.info('Submitted initial random guess')\ntrain_X = []\ntrain_y = []\ngpr = GaussianProcessRegressor(normalize_y=True, kernel=kernel... | <|body_start_0|>
super().__init__()
self.n_guesses = n_guesses
self.queues = queues
self.logger = logging.getLogger(self.__class__.__name__)
<|end_body_0|>
<|body_start_1|>
self.queues.send_inputs(uniform(0, 10))
self.logger.info('Submitted initial random guess')
... | Tool that monitors results of simulations and calls for new ones, as appropriate | Thinker | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Thinker:
"""Tool that monitors results of simulations and calls for new ones, as appropriate"""
def __init__(self, queues: ClientQueues, n_guesses: int=10):
"""Args: n_guesses (int): Number of guesses the Thinker can make queues (ClientQueues): Queues for communicating with method se... | stack_v2_sparse_classes_36k_train_027233 | 5,039 | no_license | [
{
"docstring": "Args: n_guesses (int): Number of guesses the Thinker can make queues (ClientQueues): Queues for communicating with method server",
"name": "__init__",
"signature": "def __init__(self, queues: ClientQueues, n_guesses: int=10)"
},
{
"docstring": "Connects to the Redis queue with th... | 2 | stack_v2_sparse_classes_30k_train_001710 | Implement the Python class `Thinker` described below.
Class description:
Tool that monitors results of simulations and calls for new ones, as appropriate
Method signatures and docstrings:
- def __init__(self, queues: ClientQueues, n_guesses: int=10): Args: n_guesses (int): Number of guesses the Thinker can make queue... | Implement the Python class `Thinker` described below.
Class description:
Tool that monitors results of simulations and calls for new ones, as appropriate
Method signatures and docstrings:
- def __init__(self, queues: ClientQueues, n_guesses: int=10): Args: n_guesses (int): Number of guesses the Thinker can make queue... | 042ce37e5acc8a240845b8cce11effe832c1c913 | <|skeleton|>
class Thinker:
"""Tool that monitors results of simulations and calls for new ones, as appropriate"""
def __init__(self, queues: ClientQueues, n_guesses: int=10):
"""Args: n_guesses (int): Number of guesses the Thinker can make queues (ClientQueues): Queues for communicating with method se... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Thinker:
"""Tool that monitors results of simulations and calls for new ones, as appropriate"""
def __init__(self, queues: ClientQueues, n_guesses: int=10):
"""Args: n_guesses (int): Number of guesses the Thinker can make queues (ClientQueues): Queues for communicating with method server"""
... | the_stack_v2_python_sparse | demo_apps/gpr_local/gpr_local.py | tskluzac/colmena | train | 0 |
1b09ef1681c4b7a75b25f40c56e2fe8ce1d0bbd6 | [
"self.heap = []\nself.k = k\nfor i in range(len(nums)):\n self.add(nums[i])",
"if self.k == 0:\n if self.heap[0] <= val:\n heapq.heapreplace(self.heap, val)\n return self.heap[0]\nelse:\n self.k = self.k - 1\n heapq.heappush(self.heap, val)\n if self.k == 0:\n return self.heap[0]\n... | <|body_start_0|>
self.heap = []
self.k = k
for i in range(len(nums)):
self.add(nums[i])
<|end_body_0|>
<|body_start_1|>
if self.k == 0:
if self.heap[0] <= val:
heapq.heapreplace(self.heap, val)
return self.heap[0]
else:
... | KthLargest | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class KthLargest:
def __init__(self, k, nums):
""":type k: int :type nums: List[int]"""
<|body_0|>
def add(self, val):
""":type val: int :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
self.heap = []
self.k = k
for i in range(l... | stack_v2_sparse_classes_36k_train_027234 | 769 | permissive | [
{
"docstring": ":type k: int :type nums: List[int]",
"name": "__init__",
"signature": "def __init__(self, k, nums)"
},
{
"docstring": ":type val: int :rtype: int",
"name": "add",
"signature": "def add(self, val)"
}
] | 2 | null | Implement the Python class `KthLargest` described below.
Class description:
Implement the KthLargest class.
Method signatures and docstrings:
- def __init__(self, k, nums): :type k: int :type nums: List[int]
- def add(self, val): :type val: int :rtype: int | Implement the Python class `KthLargest` described below.
Class description:
Implement the KthLargest class.
Method signatures and docstrings:
- def __init__(self, k, nums): :type k: int :type nums: List[int]
- def add(self, val): :type val: int :rtype: int
<|skeleton|>
class KthLargest:
def __init__(self, k, nu... | e7a6906ecc5bce665dec5d0f057b302a64d50f40 | <|skeleton|>
class KthLargest:
def __init__(self, k, nums):
""":type k: int :type nums: List[int]"""
<|body_0|>
def add(self, val):
""":type val: int :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class KthLargest:
def __init__(self, k, nums):
""":type k: int :type nums: List[int]"""
self.heap = []
self.k = k
for i in range(len(nums)):
self.add(nums[i])
def add(self, val):
""":type val: int :rtype: int"""
if self.k == 0:
if self.hea... | the_stack_v2_python_sparse | practise/thekelement.py | mengyangbai/leetcode | train | 0 | |
52199d5344bb74983cb53ee0493b9ae79490b3d4 | [
"username = request.user.get_username()\nserializer = RowLevelSecuritySerializer(username=username)\npolicy_id = int(policy_id)\nrow = serializer.find_security_policies(policy_id=policy_id)[0]\npolicy = request.data.get('policy', row['policy'])\npolicy_type = request.data.get('policy_type', row['policy_type'])\ngra... | <|body_start_0|>
username = request.user.get_username()
serializer = RowLevelSecuritySerializer(username=username)
policy_id = int(policy_id)
row = serializer.find_security_policies(policy_id=policy_id)[0]
policy = request.data.get('policy', row['policy'])
policy_type = r... | manage the RLS table based on row IDs | RowLevelSecurityById | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RowLevelSecurityById:
"""manage the RLS table based on row IDs"""
def patch(self, request, policy_id, format=None):
"""Update a security policy of the specified id. Ignores additional URL params, and just uses body --- omit_serializer: true parameters: - name: policy in: body descrip... | stack_v2_sparse_classes_36k_train_027235 | 31,465 | permissive | [
{
"docstring": "Update a security policy of the specified id. Ignores additional URL params, and just uses body --- omit_serializer: true parameters: - name: policy in: body description: New SQL Condition that governs access to the table. e.g. \"accessible = 1\" type: string required: false - name: policy_type ... | 2 | stack_v2_sparse_classes_30k_train_001828 | Implement the Python class `RowLevelSecurityById` described below.
Class description:
manage the RLS table based on row IDs
Method signatures and docstrings:
- def patch(self, request, policy_id, format=None): Update a security policy of the specified id. Ignores additional URL params, and just uses body --- omit_ser... | Implement the Python class `RowLevelSecurityById` described below.
Class description:
manage the RLS table based on row IDs
Method signatures and docstrings:
- def patch(self, request, policy_id, format=None): Update a security policy of the specified id. Ignores additional URL params, and just uses body --- omit_ser... | f066b472c2b66cc3b868bbe433aed2d4557aea32 | <|skeleton|>
class RowLevelSecurityById:
"""manage the RLS table based on row IDs"""
def patch(self, request, policy_id, format=None):
"""Update a security policy of the specified id. Ignores additional URL params, and just uses body --- omit_serializer: true parameters: - name: policy in: body descrip... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RowLevelSecurityById:
"""manage the RLS table based on row IDs"""
def patch(self, request, policy_id, format=None):
"""Update a security policy of the specified id. Ignores additional URL params, and just uses body --- omit_serializer: true parameters: - name: policy in: body description: New SQL... | the_stack_v2_python_sparse | src/api/views.py | datahuborg/datahub | train | 199 |
c919586f4590e88eeb6f8257c07bd686f2da2238 | [
"self.child_number = child_number\nself.text = text\nself.note = note\nself.text_tag = text_tag\nself.note_tag = note_tag",
"outline_node = node_list_item.node\nmatch = True\nif self.child_number is not None and self.child_number != node_list_item.child_number:\n match = False\nelif self.text is not None and s... | <|body_start_0|>
self.child_number = child_number
self.text = text
self.note = note
self.text_tag = text_tag
self.note_tag = note_tag
<|end_body_0|>
<|body_start_1|>
outline_node = node_list_item.node
match = True
if self.child_number is not None and self... | Used to identify the nodes in the outline which match a given set of criteria, so that node can be used as an extracted field for further processing or output. Stores a set of criteria against which a given node can be matched, and the method to determine whether a supplied outline node matches it. Where one of the sup... | NodeAncestryMatchingCriteria | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class NodeAncestryMatchingCriteria:
"""Used to identify the nodes in the outline which match a given set of criteria, so that node can be used as an extracted field for further processing or output. Stores a set of criteria against which a given node can be matched, and the method to determine whether ... | stack_v2_sparse_classes_36k_train_027236 | 2,825 | no_license | [
{
"docstring": "Args: child_number: The sequence number of this child within all the children of the parent node. text: The value of the text attribute to match with (after separating out the tag_text value). note: The value of the note attribute to match with (after separating out the tag_note value). text_tag... | 2 | stack_v2_sparse_classes_30k_val_000513 | Implement the Python class `NodeAncestryMatchingCriteria` described below.
Class description:
Used to identify the nodes in the outline which match a given set of criteria, so that node can be used as an extracted field for further processing or output. Stores a set of criteria against which a given node can be matche... | Implement the Python class `NodeAncestryMatchingCriteria` described below.
Class description:
Used to identify the nodes in the outline which match a given set of criteria, so that node can be used as an extracted field for further processing or output. Stores a set of criteria against which a given node can be matche... | d7671cb41f0fe9b17f5cdff202b7616ea2a85bcf | <|skeleton|>
class NodeAncestryMatchingCriteria:
"""Used to identify the nodes in the outline which match a given set of criteria, so that node can be used as an extracted field for further processing or output. Stores a set of criteria against which a given node can be matched, and the method to determine whether ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class NodeAncestryMatchingCriteria:
"""Used to identify the nodes in the outline which match a given set of criteria, so that node can be used as an extracted field for further processing or output. Stores a set of criteria against which a given node can be matched, and the method to determine whether a supplied ou... | the_stack_v2_python_sparse | outlines_unleashed/node_ancestry_matching_criteria.py | sevire/outlines-unleashed | train | 2 |
3981485afd9c87ed6b42bdfb18a2a8e1f3dd0ead | [
"kinect_y_offset = -0.092\nfirst_attempt = 'right'\nsecond_attempt = 'left'\nif -object_position.x + kinect_y_offset > 0:\n first_attempt = 'left'\n second_attempt = 'right'\nreturn (first_attempt, second_attempt)",
"rospy.loginfo('Asking object recognition service for object location')\nobject_recog_client... | <|body_start_0|>
kinect_y_offset = -0.092
first_attempt = 'right'
second_attempt = 'left'
if -object_position.x + kinect_y_offset > 0:
first_attempt = 'left'
second_attempt = 'right'
return (first_attempt, second_attempt)
<|end_body_0|>
<|body_start_1|>
... | GraspingHandlerClient | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GraspingHandlerClient:
def limb_move_order(self, object_position):
"""Chooses the order of which limbs to use to grab object. This is based on the object's position relative to the center of the robot :param object_position: geometry_msgs/Point, coordinates of object :return: string firs... | stack_v2_sparse_classes_36k_train_027237 | 3,945 | permissive | [
{
"docstring": "Chooses the order of which limbs to use to grab object. This is based on the object's position relative to the center of the robot :param object_position: geometry_msgs/Point, coordinates of object :return: string first_attempt, string second_attempt",
"name": "limb_move_order",
"signatu... | 2 | stack_v2_sparse_classes_30k_train_003336 | Implement the Python class `GraspingHandlerClient` described below.
Class description:
Implement the GraspingHandlerClient class.
Method signatures and docstrings:
- def limb_move_order(self, object_position): Chooses the order of which limbs to use to grab object. This is based on the object's position relative to t... | Implement the Python class `GraspingHandlerClient` described below.
Class description:
Implement the GraspingHandlerClient class.
Method signatures and docstrings:
- def limb_move_order(self, object_position): Chooses the order of which limbs to use to grab object. This is based on the object's position relative to t... | 1f9d05b7232cb9e76eff975e5ef1c8bf3fb5cde6 | <|skeleton|>
class GraspingHandlerClient:
def limb_move_order(self, object_position):
"""Chooses the order of which limbs to use to grab object. This is based on the object's position relative to the center of the robot :param object_position: geometry_msgs/Point, coordinates of object :return: string firs... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class GraspingHandlerClient:
def limb_move_order(self, object_position):
"""Chooses the order of which limbs to use to grab object. This is based on the object's position relative to the center of the robot :param object_position: geometry_msgs/Point, coordinates of object :return: string first_attempt, str... | the_stack_v2_python_sparse | grasping/src/grasping_handler_client.py | Hankfirst/de_niro | train | 1 | |
c3086ff33796e71dcaf293e35a4b2d1e0e338cea | [
"self.entity_name = entity_name\nself.model_dir = None\nself.read_model_from_s3 = read_model_from_s3\nself.read_embeddings_from_remote_url = read_embeddings_from_remote_url",
"trainer = pycrfsuite.Trainer(verbose=False)\nfor x_seq, y_seq in zip(x, y):\n trainer.append(x_seq, y_seq)\ntrainer.set_params({'c1': c... | <|body_start_0|>
self.entity_name = entity_name
self.model_dir = None
self.read_model_from_s3 = read_model_from_s3
self.read_embeddings_from_remote_url = read_embeddings_from_remote_url
<|end_body_0|>
<|body_start_1|>
trainer = pycrfsuite.Trainer(verbose=False)
for x_seq... | This class is used to train a Linear Chain Crf Model using Word Embeddings to carry out Named Entity Recognition (NER). | CrfTrain | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CrfTrain:
"""This class is used to train a Linear Chain Crf Model using Word Embeddings to carry out Named Entity Recognition (NER)."""
def __init__(self, entity_name, read_model_from_s3=False, read_embeddings_from_remote_url=False):
"""Args: entity_name (str): The destination path f... | stack_v2_sparse_classes_36k_train_027238 | 7,737 | no_license | [
{
"docstring": "Args: entity_name (str): The destination path for saving the trained model. read_model_from_s3 (bool): To indicate if cloud storage settings is required. read_embeddings_from_remote_url (bool): To indicate if cloud embeddings is active",
"name": "__init__",
"signature": "def __init__(sel... | 6 | stack_v2_sparse_classes_30k_train_002155 | Implement the Python class `CrfTrain` described below.
Class description:
This class is used to train a Linear Chain Crf Model using Word Embeddings to carry out Named Entity Recognition (NER).
Method signatures and docstrings:
- def __init__(self, entity_name, read_model_from_s3=False, read_embeddings_from_remote_ur... | Implement the Python class `CrfTrain` described below.
Class description:
This class is used to train a Linear Chain Crf Model using Word Embeddings to carry out Named Entity Recognition (NER).
Method signatures and docstrings:
- def __init__(self, entity_name, read_model_from_s3=False, read_embeddings_from_remote_ur... | b2ffe0413fd3622530779bedd103cca97ccbb1d6 | <|skeleton|>
class CrfTrain:
"""This class is used to train a Linear Chain Crf Model using Word Embeddings to carry out Named Entity Recognition (NER)."""
def __init__(self, entity_name, read_model_from_s3=False, read_embeddings_from_remote_url=False):
"""Args: entity_name (str): The destination path f... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CrfTrain:
"""This class is used to train a Linear Chain Crf Model using Word Embeddings to carry out Named Entity Recognition (NER)."""
def __init__(self, entity_name, read_model_from_s3=False, read_embeddings_from_remote_url=False):
"""Args: entity_name (str): The destination path for saving the... | the_stack_v2_python_sparse | models/crf_v2/crf_train.py | saishashank85/chatbot_challengers | train | 0 |
8c97c77cf471a7ab19e73df0d6ade833039a011b | [
"input_text = input_text.strip().replace(' ', ' ').replace(' ', ' ')\ninput_text_list = input_text.split()\nword_count = 0\ncolumn_count = 1\ncolumns_since_space = 1\nfor column in input_text:\n if column.isspace():\n word_count += 1\n columns_since_space = 0\n if (column_count / self.LINE_WI... | <|body_start_0|>
input_text = input_text.strip().replace(' ', ' ').replace(' ', ' ')
input_text_list = input_text.split()
word_count = 0
column_count = 1
columns_since_space = 1
for column in input_text:
if column.isspace():
word_count += 1
... | This class contains methods for formatting text. | FormatLine | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FormatLine:
"""This class contains methods for formatting text."""
def breakLine(self, input_text):
"""Creates line breaks at or before the value of self.LINE_WIDTH."""
<|body_0|>
def blockText(self, input_text):
"""Formats text into a block-style."""
<|b... | stack_v2_sparse_classes_36k_train_027239 | 6,993 | no_license | [
{
"docstring": "Creates line breaks at or before the value of self.LINE_WIDTH.",
"name": "breakLine",
"signature": "def breakLine(self, input_text)"
},
{
"docstring": "Formats text into a block-style.",
"name": "blockText",
"signature": "def blockText(self, input_text)"
},
{
"doc... | 3 | stack_v2_sparse_classes_30k_train_014150 | Implement the Python class `FormatLine` described below.
Class description:
This class contains methods for formatting text.
Method signatures and docstrings:
- def breakLine(self, input_text): Creates line breaks at or before the value of self.LINE_WIDTH.
- def blockText(self, input_text): Formats text into a block-... | Implement the Python class `FormatLine` described below.
Class description:
This class contains methods for formatting text.
Method signatures and docstrings:
- def breakLine(self, input_text): Creates line breaks at or before the value of self.LINE_WIDTH.
- def blockText(self, input_text): Formats text into a block-... | c0ddd8c01e083912e2dd891c963912fabc296d25 | <|skeleton|>
class FormatLine:
"""This class contains methods for formatting text."""
def breakLine(self, input_text):
"""Creates line breaks at or before the value of self.LINE_WIDTH."""
<|body_0|>
def blockText(self, input_text):
"""Formats text into a block-style."""
<|b... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class FormatLine:
"""This class contains methods for formatting text."""
def breakLine(self, input_text):
"""Creates line breaks at or before the value of self.LINE_WIDTH."""
input_text = input_text.strip().replace(' ', ' ').replace(' ', ' ')
input_text_list = input_text.split()
... | the_stack_v2_python_sparse | old/gamesrc/tools/old_format_line_generic.py | pixel-parrot/MD | train | 0 |
17d6169090f2bcd7b73f08bb21894e3c940a14ee | [
"nodes_used_final = 0\ncores_used = gpus_used = nodes_used = 0\nfor nsets, tasks, cpus_per_task, gpus in resource_sets:\n if not parallel:\n cores_used = gpus_used = nodes_used = 0\n for _ in range(nsets):\n cores_used += tasks * cpus_per_task\n gpus_used += gpus\n while cores_used... | <|body_start_0|>
nodes_used_final = 0
cores_used = gpus_used = nodes_used = 0
for nsets, tasks, cpus_per_task, gpus in resource_sets:
if not parallel:
cores_used = gpus_used = nodes_used = 0
for _ in range(nsets):
cores_used += tasks * cpus... | Environment profile for the Summit supercomputer. Example:: @Project.operation(directives={ "nranks": 3, # 3 MPI ranks per operation "ngpu": 3, # 3 GPUs "np": 3, # 3 CPU cores "rs_tasks": 3, # 3 tasks per resource set "extra_jsrun_args": '--smpiargs="-gpu"', # extra jsrun arguments }) def my_operation(job): ... https:/... | SummitEnvironment | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SummitEnvironment:
"""Environment profile for the Summit supercomputer. Example:: @Project.operation(directives={ "nranks": 3, # 3 MPI ranks per operation "ngpu": 3, # 3 GPUs "np": 3, # 3 CPU cores "rs_tasks": 3, # 3 tasks per resource set "extra_jsrun_args": '--smpiargs="-gpu"', # extra jsrun ar... | stack_v2_sparse_classes_36k_train_027240 | 10,994 | permissive | [
{
"docstring": "Compute the number of nodes needed. Parameters ---------- resource_sets : iterable of tuples Resource sets for each operation, as a sequence of tuples of *(Number of resource sets, tasks (MPI Ranks) per resource set, physical cores (CPUs) per resource set, GPUs per resource set)*. parallel : boo... | 4 | stack_v2_sparse_classes_30k_train_020518 | Implement the Python class `SummitEnvironment` described below.
Class description:
Environment profile for the Summit supercomputer. Example:: @Project.operation(directives={ "nranks": 3, # 3 MPI ranks per operation "ngpu": 3, # 3 GPUs "np": 3, # 3 CPU cores "rs_tasks": 3, # 3 tasks per resource set "extra_jsrun_args"... | Implement the Python class `SummitEnvironment` described below.
Class description:
Environment profile for the Summit supercomputer. Example:: @Project.operation(directives={ "nranks": 3, # 3 MPI ranks per operation "ngpu": 3, # 3 GPUs "np": 3, # 3 CPU cores "rs_tasks": 3, # 3 tasks per resource set "extra_jsrun_args"... | 845865c5f34135243ac21800495c46c915662c64 | <|skeleton|>
class SummitEnvironment:
"""Environment profile for the Summit supercomputer. Example:: @Project.operation(directives={ "nranks": 3, # 3 MPI ranks per operation "ngpu": 3, # 3 GPUs "np": 3, # 3 CPU cores "rs_tasks": 3, # 3 tasks per resource set "extra_jsrun_args": '--smpiargs="-gpu"', # extra jsrun ar... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SummitEnvironment:
"""Environment profile for the Summit supercomputer. Example:: @Project.operation(directives={ "nranks": 3, # 3 MPI ranks per operation "ngpu": 3, # 3 GPUs "np": 3, # 3 CPU cores "rs_tasks": 3, # 3 tasks per resource set "extra_jsrun_args": '--smpiargs="-gpu"', # extra jsrun arguments }) de... | the_stack_v2_python_sparse | flow/environments/incite.py | glotzerlab/signac-flow | train | 54 |
40b2f7bc6cab45259948d6f2f70a07fae7082e5c | [
"out = field.name + ' '\ntype = field.type\ndefault = field.default\nif field.auto_increment:\n assert is_int.search(type)\n type = re.sub(is_int, 'serial', type)\nout += type\nif default is not None:\n out += ' DEFAULT ' + default\nif not field.null:\n out += ' NOT NULL'\nreturn out",
"func = index.f... | <|body_start_0|>
out = field.name + ' '
type = field.type
default = field.default
if field.auto_increment:
assert is_int.search(type)
type = re.sub(is_int, 'serial', type)
out += type
if default is not None:
out += ' DEFAULT ' + default... | exporter | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class exporter:
def field(self, field):
"""return the column-specific fraction of the CREATE TABLE"""
<|body_0|>
def index(self, index):
"""return the CREATE INDEX for the specified index"""
<|body_1|>
def alter_table(self, table):
"""return the ALTER ... | stack_v2_sparse_classes_36k_train_027241 | 2,687 | no_license | [
{
"docstring": "return the column-specific fraction of the CREATE TABLE",
"name": "field",
"signature": "def field(self, field)"
},
{
"docstring": "return the CREATE INDEX for the specified index",
"name": "index",
"signature": "def index(self, index)"
},
{
"docstring": "return t... | 3 | stack_v2_sparse_classes_30k_train_020012 | Implement the Python class `exporter` described below.
Class description:
Implement the exporter class.
Method signatures and docstrings:
- def field(self, field): return the column-specific fraction of the CREATE TABLE
- def index(self, index): return the CREATE INDEX for the specified index
- def alter_table(self, ... | Implement the Python class `exporter` described below.
Class description:
Implement the exporter class.
Method signatures and docstrings:
- def field(self, field): return the column-specific fraction of the CREATE TABLE
- def index(self, index): return the CREATE INDEX for the specified index
- def alter_table(self, ... | 642ff7f4ac31695e225445389325a26bdeab4ec7 | <|skeleton|>
class exporter:
def field(self, field):
"""return the column-specific fraction of the CREATE TABLE"""
<|body_0|>
def index(self, index):
"""return the CREATE INDEX for the specified index"""
<|body_1|>
def alter_table(self, table):
"""return the ALTER ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class exporter:
def field(self, field):
"""return the column-specific fraction of the CREATE TABLE"""
out = field.name + ' '
type = field.type
default = field.default
if field.auto_increment:
assert is_int.search(type)
type = re.sub(is_int, 'serial', t... | the_stack_v2_python_sparse | branches/cimarron/yoryo-branch/fvl/xot/Exporters/Pg.py | BackupTheBerlios/luca-erp-svn | train | 0 | |
df5d2e0541397e5c8c6863ced056aa9a5711873f | [
"query = self.session.query(VTradehistory.o_time, VTradehistory.o_deal, VTradehistory.login, VTradehistory.symbol, VTradehistory.o_action, VTradehistory.volume, VTradehistory.o_price, VTradehistory.o_commission, VTradehistory.positionid, VTradehistory.c_time, VTradehistory.c_deal, VTradehistory.volumeclosed, VTrade... | <|body_start_0|>
query = self.session.query(VTradehistory.o_time, VTradehistory.o_deal, VTradehistory.login, VTradehistory.symbol, VTradehistory.o_action, VTradehistory.volume, VTradehistory.o_price, VTradehistory.o_commission, VTradehistory.positionid, VTradehistory.c_time, VTradehistory.c_deal, VTradehistory.... | v_tradehistory视图操作 | VTradehistoryDao | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class VTradehistoryDao:
"""v_tradehistory视图操作"""
def search_by_uid(self, uid, start, end, mtlogin, page=None):
"""已知用户id，根据时间段，查询交易订单 :param uid:用户id :param start:起始时间 :param end:结束时间 :param page:请求页 :return:queryset"""
<|body_0|>
def searchsum_by_uid(self, uid, start, end, mt... | stack_v2_sparse_classes_36k_train_027242 | 26,694 | permissive | [
{
"docstring": "已知用户id，根据时间段，查询交易订单 :param uid:用户id :param start:起始时间 :param end:结束时间 :param page:请求页 :return:queryset",
"name": "search_by_uid",
"signature": "def search_by_uid(self, uid, start, end, mtlogin, page=None)"
},
{
"docstring": "已知用户id，根据时间段，查询总和 :param uid: 用户id :param start: 开始时间 :... | 2 | stack_v2_sparse_classes_30k_train_009878 | Implement the Python class `VTradehistoryDao` described below.
Class description:
v_tradehistory视图操作
Method signatures and docstrings:
- def search_by_uid(self, uid, start, end, mtlogin, page=None): 已知用户id，根据时间段，查询交易订单 :param uid:用户id :param start:起始时间 :param end:结束时间 :param page:请求页 :return:queryset
- def searchsum_... | Implement the Python class `VTradehistoryDao` described below.
Class description:
v_tradehistory视图操作
Method signatures and docstrings:
- def search_by_uid(self, uid, start, end, mtlogin, page=None): 已知用户id，根据时间段，查询交易订单 :param uid:用户id :param start:起始时间 :param end:结束时间 :param page:请求页 :return:queryset
- def searchsum_... | 1fadeecf31f1d25e258dc5d70c47a785f7b33961 | <|skeleton|>
class VTradehistoryDao:
"""v_tradehistory视图操作"""
def search_by_uid(self, uid, start, end, mtlogin, page=None):
"""已知用户id，根据时间段，查询交易订单 :param uid:用户id :param start:起始时间 :param end:结束时间 :param page:请求页 :return:queryset"""
<|body_0|>
def searchsum_by_uid(self, uid, start, end, mt... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class VTradehistoryDao:
"""v_tradehistory视图操作"""
def search_by_uid(self, uid, start, end, mtlogin, page=None):
"""已知用户id，根据时间段，查询交易订单 :param uid:用户id :param start:起始时间 :param end:结束时间 :param page:请求页 :return:queryset"""
query = self.session.query(VTradehistory.o_time, VTradehistory.o_deal, VTra... | the_stack_v2_python_sparse | xwcrm/model/views.py | MSUNorg/XWCRM | train | 0 |
d31cbc1f89c6b8c834a1e819fd9c0cbcdeb545c0 | [
"if not isinstance(network, NN.BackPropagationNetwork):\n raise ValueError('Bad parameter {network}')\nself.Network = network\nself.DataSet = dataSet",
"for i in range(self.MaxIterations):\n error = bpn.TrainEpoch(self.DataSet[0], self.DataSet[1], **kwags)\n if error <= min_error:\n break\n if ... | <|body_start_0|>
if not isinstance(network, NN.BackPropagationNetwork):
raise ValueError('Bad parameter {network}')
self.Network = network
self.DataSet = dataSet
<|end_body_0|>
<|body_start_1|>
for i in range(self.MaxIterations):
error = bpn.TrainEpoch(self.DataS... | This is a class which trains neural networks | NetworkTrainer | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class NetworkTrainer:
"""This is a class which trains neural networks"""
def __init__(self, network, dataSet):
"""Set up the trainer class"""
<|body_0|>
def TrainNetwork(self, min_error=0.1, display=True, **kwags):
"""Train the network until a specific error is reached... | stack_v2_sparse_classes_36k_train_027243 | 2,291 | no_license | [
{
"docstring": "Set up the trainer class",
"name": "__init__",
"signature": "def __init__(self, network, dataSet)"
},
{
"docstring": "Train the network until a specific error is reached",
"name": "TrainNetwork",
"signature": "def TrainNetwork(self, min_error=0.1, display=True, **kwags)"
... | 2 | stack_v2_sparse_classes_30k_train_001795 | Implement the Python class `NetworkTrainer` described below.
Class description:
This is a class which trains neural networks
Method signatures and docstrings:
- def __init__(self, network, dataSet): Set up the trainer class
- def TrainNetwork(self, min_error=0.1, display=True, **kwags): Train the network until a spec... | Implement the Python class `NetworkTrainer` described below.
Class description:
This is a class which trains neural networks
Method signatures and docstrings:
- def __init__(self, network, dataSet): Set up the trainer class
- def TrainNetwork(self, min_error=0.1, display=True, **kwags): Train the network until a spec... | 8f7f107a93bb7578a00d531123ee7f5db61d807e | <|skeleton|>
class NetworkTrainer:
"""This is a class which trains neural networks"""
def __init__(self, network, dataSet):
"""Set up the trainer class"""
<|body_0|>
def TrainNetwork(self, min_error=0.1, display=True, **kwags):
"""Train the network until a specific error is reached... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class NetworkTrainer:
"""This is a class which trains neural networks"""
def __init__(self, network, dataSet):
"""Set up the trainer class"""
if not isinstance(network, NN.BackPropagationNetwork):
raise ValueError('Bad parameter {network}')
self.Network = network
sel... | the_stack_v2_python_sparse | Advanced/NeuralNetwork/TestScript.py | jsa4000/Getting-Started-Python | train | 0 |
59fb990cd7daafd55c010cf346c5c540891ce2ae | [
"pygame.init()\nwidth, height = Display_Config['BATTLE_SIZE']\nheight += Display_Config['LOG_SIZE'][1]\nheight += Display_Config['SELECT_SIZE'][1]\nif state['use_agent'] and Display_Config['VISUALIZE_AGENT_INFO']:\n width *= 2\nSCREEN_SIZE = (width, height)\nself.SCREEN = display.set_mode(scale(SCREEN_SIZE))\ndi... | <|body_start_0|>
pygame.init()
width, height = Display_Config['BATTLE_SIZE']
height += Display_Config['LOG_SIZE'][1]
height += Display_Config['SELECT_SIZE'][1]
if state['use_agent'] and Display_Config['VISUALIZE_AGENT_INFO']:
width *= 2
SCREEN_SIZE = (width, h... | Window | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Window:
def __init__(self, state):
"""Args: state ('dict of class:Pokemon'): A dictionary with all the information needed to display the a battle. Note: This dict have as key: "Ally_0", "Ally_1", "Foe_0" and "Foe_1" with the corresponding pokemon as a value. Action: Create and execute a ... | stack_v2_sparse_classes_36k_train_027244 | 6,184 | no_license | [
{
"docstring": "Args: state ('dict of class:Pokemon'): A dictionary with all the information needed to display the a battle. Note: This dict have as key: \"Ally_0\", \"Ally_1\", \"Foe_0\" and \"Foe_1\" with the corresponding pokemon as a value. Action: Create and execute a window where the 'state' of a battle i... | 6 | stack_v2_sparse_classes_30k_train_014480 | Implement the Python class `Window` described below.
Class description:
Implement the Window class.
Method signatures and docstrings:
- def __init__(self, state): Args: state ('dict of class:Pokemon'): A dictionary with all the information needed to display the a battle. Note: This dict have as key: "Ally_0", "Ally_1... | Implement the Python class `Window` described below.
Class description:
Implement the Window class.
Method signatures and docstrings:
- def __init__(self, state): Args: state ('dict of class:Pokemon'): A dictionary with all the information needed to display the a battle. Note: This dict have as key: "Ally_0", "Ally_1... | aa9defc6387788fc57d50dfdff7e4c43e8a1c358 | <|skeleton|>
class Window:
def __init__(self, state):
"""Args: state ('dict of class:Pokemon'): A dictionary with all the information needed to display the a battle. Note: This dict have as key: "Ally_0", "Ally_1", "Foe_0" and "Foe_1" with the corresponding pokemon as a value. Action: Create and execute a ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Window:
def __init__(self, state):
"""Args: state ('dict of class:Pokemon'): A dictionary with all the information needed to display the a battle. Note: This dict have as key: "Ally_0", "Ally_1", "Foe_0" and "Foe_1" with the corresponding pokemon as a value. Action: Create and execute a window where t... | the_stack_v2_python_sparse | Game/display/window.py | dieguigram/Pokemon-Python | train | 0 | |
178b9feef02172cf624586a2297447f07cb94888 | [
"self.site_envs = site_envs\nself.unique_site_types: List[str] = [env.sitetypes[0] for env in self.site_envs]\nself.ns = ns\nself.na = na\nself.aos = aos\nself.eigen_tol = eigen_tol\nself.pbc = pbc\nself.cutoff = cutoff",
"oss = [species for species in struct.site_properties['oss'] if species != '-1']\nXSites = n... | <|body_start_0|>
self.site_envs = site_envs
self.unique_site_types: List[str] = [env.sitetypes[0] for env in self.site_envs]
self.ns = ns
self.na = na
self.aos = aos
self.eigen_tol = eigen_tol
self.pbc = pbc
self.cutoff = cutoff
<|end_body_0|>
<|body_star... | _SiteEnvironments | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class _SiteEnvironments:
def __init__(self, site_envs: List[_SiteEnvironment], ns: int, na: int, aos: List[str], eigen_tol: float, pbc: np.typing.ArrayLike, cutoff: float):
"""Initialize Use Load to initialize this class. Parameters ---------- site_envs : List[_SiteEnvironment] list of _SiteEn... | stack_v2_sparse_classes_36k_train_027245 | 28,058 | permissive | [
{
"docstring": "Initialize Use Load to initialize this class. Parameters ---------- site_envs : List[_SiteEnvironment] list of _SiteEnvironment object ns : int number of spectator sites types na : int number of active sites types aos : List[str] Available occupational states for active sites string should be th... | 3 | null | Implement the Python class `_SiteEnvironments` described below.
Class description:
Implement the _SiteEnvironments class.
Method signatures and docstrings:
- def __init__(self, site_envs: List[_SiteEnvironment], ns: int, na: int, aos: List[str], eigen_tol: float, pbc: np.typing.ArrayLike, cutoff: float): Initialize U... | Implement the Python class `_SiteEnvironments` described below.
Class description:
Implement the _SiteEnvironments class.
Method signatures and docstrings:
- def __init__(self, site_envs: List[_SiteEnvironment], ns: int, na: int, aos: List[str], eigen_tol: float, pbc: np.typing.ArrayLike, cutoff: float): Initialize U... | ee6e67ebcf7bf04259cf13aff6388e2b791fea3d | <|skeleton|>
class _SiteEnvironments:
def __init__(self, site_envs: List[_SiteEnvironment], ns: int, na: int, aos: List[str], eigen_tol: float, pbc: np.typing.ArrayLike, cutoff: float):
"""Initialize Use Load to initialize this class. Parameters ---------- site_envs : List[_SiteEnvironment] list of _SiteEn... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class _SiteEnvironments:
def __init__(self, site_envs: List[_SiteEnvironment], ns: int, na: int, aos: List[str], eigen_tol: float, pbc: np.typing.ArrayLike, cutoff: float):
"""Initialize Use Load to initialize this class. Parameters ---------- site_envs : List[_SiteEnvironment] list of _SiteEnvironment obje... | the_stack_v2_python_sparse | deepchem/feat/material_featurizers/lcnn_featurizer.py | deepchem/deepchem | train | 4,876 | |
94ee02b8bd646e085100725fb7389e78ad1fb518 | [
"headers = {}\nif status:\n headers['x-image-meta-status'] = status\nurl = '/v1/images/' + image_id\nresponse = self._put(url, headers)\nresponse.raise_for_status()",
"response = self._head('/v1/images/{}'.format(image_id))\nresponse.raise_for_status()\ndata = self._retrieve_data(response)\nreturn Resource(sel... | <|body_start_0|>
headers = {}
if status:
headers['x-image-meta-status'] = status
url = '/v1/images/' + image_id
response = self._put(url, headers)
response.raise_for_status()
<|end_body_0|>
<|body_start_1|>
response = self._head('/v1/images/{}'.format(image_i... | Glance API client v1. | ApiClientV1 | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ApiClientV1:
"""Glance API client v1."""
def images_update(self, image_id, status=None):
"""Update image via API call."""
<|body_0|>
def images_get(self, image_id):
"""Get image via API call. Args: image id (str): image ID Returns: Resource: image"""
<|bo... | stack_v2_sparse_classes_36k_train_027246 | 1,422 | no_license | [
{
"docstring": "Update image via API call.",
"name": "images_update",
"signature": "def images_update(self, image_id, status=None)"
},
{
"docstring": "Get image via API call. Args: image id (str): image ID Returns: Resource: image",
"name": "images_get",
"signature": "def images_get(self... | 2 | null | Implement the Python class `ApiClientV1` described below.
Class description:
Glance API client v1.
Method signatures and docstrings:
- def images_update(self, image_id, status=None): Update image via API call.
- def images_get(self, image_id): Get image via API call. Args: image id (str): image ID Returns: Resource: ... | Implement the Python class `ApiClientV1` described below.
Class description:
Glance API client v1.
Method signatures and docstrings:
- def images_update(self, image_id, status=None): Update image via API call.
- def images_get(self, image_id): Get image via API call. Args: image id (str): image ID Returns: Resource: ... | e7583444cd24893ec6ae237b47db7c605b99b0c5 | <|skeleton|>
class ApiClientV1:
"""Glance API client v1."""
def images_update(self, image_id, status=None):
"""Update image via API call."""
<|body_0|>
def images_get(self, image_id):
"""Get image via API call. Args: image id (str): image ID Returns: Resource: image"""
<|bo... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ApiClientV1:
"""Glance API client v1."""
def images_update(self, image_id, status=None):
"""Update image via API call."""
headers = {}
if status:
headers['x-image-meta-status'] = status
url = '/v1/images/' + image_id
response = self._put(url, headers)
... | the_stack_v2_python_sparse | stepler/glance/api_clients/v1.py | Mirantis/stepler | train | 16 |
b4b0bb45cb9dd83973be2dc4ac1f50f87524f7ad | [
"sale_order_obj = self.pool.get('sale.order')\nsale_order_brws = sale_order_obj.browse(cr, uid, values['order_id'], context)\nproduct_brws = self.pool.get('product.product').browse(cr, uid, values['product_id'], context)\nfields_to_default = ['product_uom', 'discount', 'product_uom_qty', 'product_uos_qty', 'state']... | <|body_start_0|>
sale_order_obj = self.pool.get('sale.order')
sale_order_brws = sale_order_obj.browse(cr, uid, values['order_id'], context)
product_brws = self.pool.get('product.product').browse(cr, uid, values['product_id'], context)
fields_to_default = ['product_uom', 'discount', 'prod... | sale_order_line | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class sale_order_line:
def create_and_update_prices(self, cr, uid, values, context={}):
"""Create and update prices :param cr: :param uid: :param values: Must contain order_id, product_id, name, product_uom_qty :param context: :return:"""
<|body_0|>
def write_and_update_prices(sel... | stack_v2_sparse_classes_36k_train_027247 | 11,160 | no_license | [
{
"docstring": "Create and update prices :param cr: :param uid: :param values: Must contain order_id, product_id, name, product_uom_qty :param context: :return:",
"name": "create_and_update_prices",
"signature": "def create_and_update_prices(self, cr, uid, values, context={})"
},
{
"docstring": ... | 2 | null | Implement the Python class `sale_order_line` described below.
Class description:
Implement the sale_order_line class.
Method signatures and docstrings:
- def create_and_update_prices(self, cr, uid, values, context={}): Create and update prices :param cr: :param uid: :param values: Must contain order_id, product_id, n... | Implement the Python class `sale_order_line` described below.
Class description:
Implement the sale_order_line class.
Method signatures and docstrings:
- def create_and_update_prices(self, cr, uid, values, context={}): Create and update prices :param cr: :param uid: :param values: Must contain order_id, product_id, n... | bb925edf7eaee44a96da12cbb86263ece0ae052c | <|skeleton|>
class sale_order_line:
def create_and_update_prices(self, cr, uid, values, context={}):
"""Create and update prices :param cr: :param uid: :param values: Must contain order_id, product_id, name, product_uom_qty :param context: :return:"""
<|body_0|>
def write_and_update_prices(sel... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class sale_order_line:
def create_and_update_prices(self, cr, uid, values, context={}):
"""Create and update prices :param cr: :param uid: :param values: Must contain order_id, product_id, name, product_uom_qty :param context: :return:"""
sale_order_obj = self.pool.get('sale.order')
sale_ord... | the_stack_v2_python_sparse | ax_ws_sale_order/models/sale_order_line.py | davidts-leathergoods/odoo7 | train | 0 | |
28410eb18f8f161b2be122b87dabeef18baa25cd | [
"left = 0\nright = len(nums) - 1\nwhile left <= right:\n mid = (left + right) // 2\n if nums[mid] == target:\n return mid\n elif nums[mid] < target:\n left = mid + 1\n else:\n right = mid - 1\nreturn -1",
"left = 0\nright = len(nums) - 1\nwhile left <= right:\n mid = (left + ri... | <|body_start_0|>
left = 0
right = len(nums) - 1
while left <= right:
mid = (left + right) // 2
if nums[mid] == target:
return mid
elif nums[mid] < target:
left = mid + 1
else:
right = mid - 1
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def binarySearch(self, nums, target):
"""二查搜索，找到则返回索引，否则返回-1"""
<|body_0|>
def left_bound(self, nums, target):
"""寻找左侧边界的二分搜索"""
<|body_1|>
def right_bound(self, nums, target):
"""寻找左侧边界的二分搜索"""
<|body_2|>
<|end_skeleton|>
<|b... | stack_v2_sparse_classes_36k_train_027248 | 1,555 | no_license | [
{
"docstring": "二查搜索，找到则返回索引，否则返回-1",
"name": "binarySearch",
"signature": "def binarySearch(self, nums, target)"
},
{
"docstring": "寻找左侧边界的二分搜索",
"name": "left_bound",
"signature": "def left_bound(self, nums, target)"
},
{
"docstring": "寻找左侧边界的二分搜索",
"name": "right_bound",
... | 3 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def binarySearch(self, nums, target): 二查搜索，找到则返回索引，否则返回-1
- def left_bound(self, nums, target): 寻找左侧边界的二分搜索
- def right_bound(self, nums, target): 寻找左侧边界的二分搜索 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def binarySearch(self, nums, target): 二查搜索，找到则返回索引，否则返回-1
- def left_bound(self, nums, target): 寻找左侧边界的二分搜索
- def right_bound(self, nums, target): 寻找左侧边界的二分搜索
<|skeleton|>
class... | 837957ea22aa07ce28a6c23ea0419bd2011e1f88 | <|skeleton|>
class Solution:
def binarySearch(self, nums, target):
"""二查搜索，找到则返回索引，否则返回-1"""
<|body_0|>
def left_bound(self, nums, target):
"""寻找左侧边界的二分搜索"""
<|body_1|>
def right_bound(self, nums, target):
"""寻找左侧边界的二分搜索"""
<|body_2|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def binarySearch(self, nums, target):
"""二查搜索，找到则返回索引，否则返回-1"""
left = 0
right = len(nums) - 1
while left <= right:
mid = (left + right) // 2
if nums[mid] == target:
return mid
elif nums[mid] < target:
... | the_stack_v2_python_sparse | fuck/算法思维/二分法.py | 2226171237/Algorithmpractice | train | 0 | |
4465bedab331f4fc1378cddb2d4933ed199d76f4 | [
"super(DNET_COLORED, self).__init__()\nself.nc = 3\nself.ef_dim = nef\nself.df_dim = ndf\nself.define_module()",
"nef = self.ef_dim\nndf = self.df_dim\nself.netD_1 = nn.Sequential(conv4x4(self.nc, ndf), nn.BatchNorm2d(num_features=ndf), nn.LeakyReLU(negative_slope=0.2, inplace=True), conv4x4(ndf, ndf * 2), nn.Bat... | <|body_start_0|>
super(DNET_COLORED, self).__init__()
self.nc = 3
self.ef_dim = nef
self.df_dim = ndf
self.define_module()
<|end_body_0|>
<|body_start_1|>
nef = self.ef_dim
ndf = self.df_dim
self.netD_1 = nn.Sequential(conv4x4(self.nc, ndf), nn.BatchNorm2... | Discriminator class for the ENCOLOR stage. Args: - ndf (int, optional): Number of discriminator filters in the first convolutional layer. (Default: 64) - nef (int, optional): Projected embeddings dimensions. (Default: 128) | DNET_COLORED | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DNET_COLORED:
"""Discriminator class for the ENCOLOR stage. Args: - ndf (int, optional): Number of discriminator filters in the first convolutional layer. (Default: 64) - nef (int, optional): Projected embeddings dimensions. (Default: 128)"""
def __init__(self, ndf=64, nef=128):
"""I... | stack_v2_sparse_classes_36k_train_027249 | 22,492 | no_license | [
{
"docstring": "Initialize the ENCOLOR stage discriminator. Other attributes: - nc (int): Number of channels.",
"name": "__init__",
"signature": "def __init__(self, ndf=64, nef=128)"
},
{
"docstring": "Define stage 2 discriminator's model.",
"name": "define_module",
"signature": "def def... | 3 | stack_v2_sparse_classes_30k_train_002130 | Implement the Python class `DNET_COLORED` described below.
Class description:
Discriminator class for the ENCOLOR stage. Args: - ndf (int, optional): Number of discriminator filters in the first convolutional layer. (Default: 64) - nef (int, optional): Projected embeddings dimensions. (Default: 128)
Method signatures... | Implement the Python class `DNET_COLORED` described below.
Class description:
Discriminator class for the ENCOLOR stage. Args: - ndf (int, optional): Number of discriminator filters in the first convolutional layer. (Default: 64) - nef (int, optional): Projected embeddings dimensions. (Default: 128)
Method signatures... | 70d344d80425e7bbcc7984737dbe50a6638293c9 | <|skeleton|>
class DNET_COLORED:
"""Discriminator class for the ENCOLOR stage. Args: - ndf (int, optional): Number of discriminator filters in the first convolutional layer. (Default: 64) - nef (int, optional): Projected embeddings dimensions. (Default: 128)"""
def __init__(self, ndf=64, nef=128):
"""I... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DNET_COLORED:
"""Discriminator class for the ENCOLOR stage. Args: - ndf (int, optional): Number of discriminator filters in the first convolutional layer. (Default: 64) - nef (int, optional): Projected embeddings dimensions. (Default: 128)"""
def __init__(self, ndf=64, nef=128):
"""Initialize the... | the_stack_v2_python_sparse | TeleGAN/model.py | ails-lab/teleGAN | train | 1 |
6387c095a5ec6b367f22e096f4b47a3f26ff66e7 | [
"self.nodes = []\nhead = point = ListNode(0)\nfor l in lists:\n while l:\n self.nodes.append(l.val)\n l = l.next\nfor x in sorted(self.nodes):\n point.next = ListNode(x)\n point = point.next\nreturn head.next",
"def merge(lis1, lis2):\n result = pt = ListNode(0)\n while lis1 and lis2:... | <|body_start_0|>
self.nodes = []
head = point = ListNode(0)
for l in lists:
while l:
self.nodes.append(l.val)
l = l.next
for x in sorted(self.nodes):
point.next = ListNode(x)
point = point.next
return head.next
<... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def mergeKLists(self, lists):
""":type lists: List[ListNode] :rtype: ListNode"""
<|body_0|>
def mergeKLists(self, lists):
""":type lists: List[ListNode] :rtype: ListNode"""
<|body_1|>
def mergeKLists(self, lists):
""":type lists: List[L... | stack_v2_sparse_classes_36k_train_027250 | 2,864 | no_license | [
{
"docstring": ":type lists: List[ListNode] :rtype: ListNode",
"name": "mergeKLists",
"signature": "def mergeKLists(self, lists)"
},
{
"docstring": ":type lists: List[ListNode] :rtype: ListNode",
"name": "mergeKLists",
"signature": "def mergeKLists(self, lists)"
},
{
"docstring":... | 3 | stack_v2_sparse_classes_30k_val_001031 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def mergeKLists(self, lists): :type lists: List[ListNode] :rtype: ListNode
- def mergeKLists(self, lists): :type lists: List[ListNode] :rtype: ListNode
- def mergeKLists(self, li... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def mergeKLists(self, lists): :type lists: List[ListNode] :rtype: ListNode
- def mergeKLists(self, lists): :type lists: List[ListNode] :rtype: ListNode
- def mergeKLists(self, li... | 62ad010a992c031e8c0fe4d1a9b6f9364f96ed4c | <|skeleton|>
class Solution:
def mergeKLists(self, lists):
""":type lists: List[ListNode] :rtype: ListNode"""
<|body_0|>
def mergeKLists(self, lists):
""":type lists: List[ListNode] :rtype: ListNode"""
<|body_1|>
def mergeKLists(self, lists):
""":type lists: List[L... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def mergeKLists(self, lists):
""":type lists: List[ListNode] :rtype: ListNode"""
self.nodes = []
head = point = ListNode(0)
for l in lists:
while l:
self.nodes.append(l.val)
l = l.next
for x in sorted(self.nodes):
... | the_stack_v2_python_sparse | leetcode/solved/023_.py | usnnu/python_foundation | train | 0 | |
f1b06aeb6215bbceb5127b1c6f7f8d733ac74891 | [
"if head is None:\n return None\ndummyHead = ListNode(0)\nh1 = dummyHead\nh1.next = head\nh1 = h1.next\nh2 = h1.next\nwhile h2:\n if h2.val != h1.val:\n h1.next = h2\n h1 = h1.next\n h2 = h2.next\nh1.next = None\nreturn dummyHead.next",
"if head is None:\n return None\nh1 = head\nh2 = h1... | <|body_start_0|>
if head is None:
return None
dummyHead = ListNode(0)
h1 = dummyHead
h1.next = head
h1 = h1.next
h2 = h1.next
while h2:
if h2.val != h1.val:
h1.next = h2
h1 = h1.next
h2 = h2.next
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def deleteDuplicates(self, head):
""":type head: ListNode :rtype: ListNode"""
<|body_0|>
def deleteDuplicates2(self, head):
""":type head: ListNode :rtype: ListNode"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
if head is None:
... | stack_v2_sparse_classes_36k_train_027251 | 1,561 | no_license | [
{
"docstring": ":type head: ListNode :rtype: ListNode",
"name": "deleteDuplicates",
"signature": "def deleteDuplicates(self, head)"
},
{
"docstring": ":type head: ListNode :rtype: ListNode",
"name": "deleteDuplicates2",
"signature": "def deleteDuplicates2(self, head)"
}
] | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def deleteDuplicates(self, head): :type head: ListNode :rtype: ListNode
- def deleteDuplicates2(self, head): :type head: ListNode :rtype: ListNode | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def deleteDuplicates(self, head): :type head: ListNode :rtype: ListNode
- def deleteDuplicates2(self, head): :type head: ListNode :rtype: ListNode
<|skeleton|>
class Solution:
... | c34b55bb42dc44a9026a902f6afcc018b4154662 | <|skeleton|>
class Solution:
def deleteDuplicates(self, head):
""":type head: ListNode :rtype: ListNode"""
<|body_0|>
def deleteDuplicates2(self, head):
""":type head: ListNode :rtype: ListNode"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def deleteDuplicates(self, head):
""":type head: ListNode :rtype: ListNode"""
if head is None:
return None
dummyHead = ListNode(0)
h1 = dummyHead
h1.next = head
h1 = h1.next
h2 = h1.next
while h2:
if h2.val != h1... | the_stack_v2_python_sparse | Algorithm/Remove Duplicates from Sorted List.py | superpigBB/Happy-Coding | train | 0 | |
527d05accf5ec186ab6d4c7bbb87c205f8a14da0 | [
"self.what = what\nself.directory = directory\nif not os.path.exists(f'{settings.CONFIG_FOLDER}/.build_state'):\n os.makedirs(f'{settings.CONFIG_FOLDER}/.build_state')\nself.state_file_name = f'{settings.CONFIG_FOLDER}/.build_state/last_change_{what}.txt'",
"try:\n os.remove(self.state_file_name)\nexcept:\n... | <|body_start_0|>
self.what = what
self.directory = directory
if not os.path.exists(f'{settings.CONFIG_FOLDER}/.build_state'):
os.makedirs(f'{settings.CONFIG_FOLDER}/.build_state')
self.state_file_name = f'{settings.CONFIG_FOLDER}/.build_state/last_change_{what}.txt'
<|end_bod... | Try not to re-do what doesn't need to be redone | BuildState | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BuildState:
"""Try not to re-do what doesn't need to be redone"""
def __init__(self, what: str, directory: str) -> None:
"""Set initial state"""
<|body_0|>
def oh_never_mind(self) -> None:
"""If a task fails, we don't care if it didn't change since last, re-run,"... | stack_v2_sparse_classes_36k_train_027252 | 8,201 | permissive | [
{
"docstring": "Set initial state",
"name": "__init__",
"signature": "def __init__(self, what: str, directory: str) -> None"
},
{
"docstring": "If a task fails, we don't care if it didn't change since last, re-run,",
"name": "oh_never_mind",
"signature": "def oh_never_mind(self) -> None"... | 3 | stack_v2_sparse_classes_30k_train_012366 | Implement the Python class `BuildState` described below.
Class description:
Try not to re-do what doesn't need to be redone
Method signatures and docstrings:
- def __init__(self, what: str, directory: str) -> None: Set initial state
- def oh_never_mind(self) -> None: If a task fails, we don't care if it didn't change... | Implement the Python class `BuildState` described below.
Class description:
Try not to re-do what doesn't need to be redone
Method signatures and docstrings:
- def __init__(self, what: str, directory: str) -> None: Set initial state
- def oh_never_mind(self) -> None: If a task fails, we don't care if it didn't change... | d2771ae80994032bffa398baa246a9520e1a2e48 | <|skeleton|>
class BuildState:
"""Try not to re-do what doesn't need to be redone"""
def __init__(self, what: str, directory: str) -> None:
"""Set initial state"""
<|body_0|>
def oh_never_mind(self) -> None:
"""If a task fails, we don't care if it didn't change since last, re-run,"... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class BuildState:
"""Try not to re-do what doesn't need to be redone"""
def __init__(self, what: str, directory: str) -> None:
"""Set initial state"""
self.what = what
self.directory = directory
if not os.path.exists(f'{settings.CONFIG_FOLDER}/.build_state'):
os.make... | the_stack_v2_python_sparse | navio_tasks/build_state.py | matthewdeanmartin/cheese_grader | train | 9 |
fe53b5ab6aaacdafae2d2d97b37f545b51b62eac | [
"self.jsonService = JsonService()\nself.dataFrame = pandas.read_csv(csvFile)\nself.dataFrame.dropna(inplace=True)",
"properties = dict()\nfor index, row in self.dataFrame.iterrows():\n func(properties, row)\nprint(properties)\nself.jsonService.save(jsonFile, properties)"
] | <|body_start_0|>
self.jsonService = JsonService()
self.dataFrame = pandas.read_csv(csvFile)
self.dataFrame.dropna(inplace=True)
<|end_body_0|>
<|body_start_1|>
properties = dict()
for index, row in self.dataFrame.iterrows():
func(properties, row)
print(proper... | 处理 CSV 文件为 JSON 文件 | CsvHandler | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CsvHandler:
"""处理 CSV 文件为 JSON 文件"""
def __init__(self, csvFile):
"""初始化 新建对象时默认执行 参数值： csvFile (str): 要读取的 CSV 文件"""
<|body_0|>
def operate(self, func, jsonFile):
"""处理 CSV 文件为 JSON 文件 参数值： func (func(dict, row)): 处理规则函数 函数参数要求： dict(dict): JSON 文件格式 row(pandas.... | stack_v2_sparse_classes_36k_train_027253 | 2,353 | no_license | [
{
"docstring": "初始化 新建对象时默认执行 参数值： csvFile (str): 要读取的 CSV 文件",
"name": "__init__",
"signature": "def __init__(self, csvFile)"
},
{
"docstring": "处理 CSV 文件为 JSON 文件 参数值： func (func(dict, row)): 处理规则函数 函数参数要求： dict(dict): JSON 文件格式 row(pandas.series): CSV 文件的每一列 jsonFile (str): 要存储为的 JSON 文件",
... | 2 | stack_v2_sparse_classes_30k_train_001092 | Implement the Python class `CsvHandler` described below.
Class description:
处理 CSV 文件为 JSON 文件
Method signatures and docstrings:
- def __init__(self, csvFile): 初始化 新建对象时默认执行 参数值： csvFile (str): 要读取的 CSV 文件
- def operate(self, func, jsonFile): 处理 CSV 文件为 JSON 文件 参数值： func (func(dict, row)): 处理规则函数 函数参数要求： dict(dict): ... | Implement the Python class `CsvHandler` described below.
Class description:
处理 CSV 文件为 JSON 文件
Method signatures and docstrings:
- def __init__(self, csvFile): 初始化 新建对象时默认执行 参数值： csvFile (str): 要读取的 CSV 文件
- def operate(self, func, jsonFile): 处理 CSV 文件为 JSON 文件 参数值： func (func(dict, row)): 处理规则函数 函数参数要求： dict(dict): ... | 105caf2288435c50ae693ff12a0e4e72822587d6 | <|skeleton|>
class CsvHandler:
"""处理 CSV 文件为 JSON 文件"""
def __init__(self, csvFile):
"""初始化 新建对象时默认执行 参数值： csvFile (str): 要读取的 CSV 文件"""
<|body_0|>
def operate(self, func, jsonFile):
"""处理 CSV 文件为 JSON 文件 参数值： func (func(dict, row)): 处理规则函数 函数参数要求： dict(dict): JSON 文件格式 row(pandas.... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CsvHandler:
"""处理 CSV 文件为 JSON 文件"""
def __init__(self, csvFile):
"""初始化 新建对象时默认执行 参数值： csvFile (str): 要读取的 CSV 文件"""
self.jsonService = JsonService()
self.dataFrame = pandas.read_csv(csvFile)
self.dataFrame.dropna(inplace=True)
def operate(self, func, jsonFile):
... | the_stack_v2_python_sparse | CsvHandler.py | YuanLinStudio/Inspection-Record-Consolidating-System | train | 0 |
ffe8e6e878bca9a8f4771447b78f105f74b32c7b | [
"self.tolerance2 = tolerance ** 2.0\nself.pointList = pointList\nself.coords = [[point[0] for point in pointList], [point[1] for point in pointList], [point[2] for point in pointList]]\nself.mins = [10000, 10000, 10000]\nself.maxs = [-10000, -10000, -10000]\nfor dimension in xrange(3):\n self.mins[dimension] = i... | <|body_start_0|>
self.tolerance2 = tolerance ** 2.0
self.pointList = pointList
self.coords = [[point[0] for point in pointList], [point[1] for point in pointList], [point[2] for point in pointList]]
self.mins = [10000, 10000, 10000]
self.maxs = [-10000, -10000, -10000]
fo... | buckets class is used for fast 3d point overlapping puts all points into buckets in each dimension only compare to reasonably nearby points assumes tolerance <<<< 1 angstrom | Bucket3d | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Bucket3d:
"""buckets class is used for fast 3d point overlapping puts all points into buckets in each dimension only compare to reasonably nearby points assumes tolerance <<<< 1 angstrom"""
def __init__(self, pointList, tolerance):
"""takes the point list and makes buckets for search... | stack_v2_sparse_classes_36k_train_027254 | 4,829 | permissive | [
{
"docstring": "takes the point list and makes buckets for searching later. O(n)",
"name": "__init__",
"signature": "def __init__(self, pointList, tolerance)"
},
{
"docstring": "souped up for speed version of code. puts nearby points into the unionfind data structure 'clusters'. does every possi... | 3 | stack_v2_sparse_classes_30k_train_001988 | Implement the Python class `Bucket3d` described below.
Class description:
buckets class is used for fast 3d point overlapping puts all points into buckets in each dimension only compare to reasonably nearby points assumes tolerance <<<< 1 angstrom
Method signatures and docstrings:
- def __init__(self, pointList, tole... | Implement the Python class `Bucket3d` described below.
Class description:
buckets class is used for fast 3d point overlapping puts all points into buckets in each dimension only compare to reasonably nearby points assumes tolerance <<<< 1 angstrom
Method signatures and docstrings:
- def __init__(self, pointList, tole... | 5b930ce2fdf5def49444f1953457745af964efe9 | <|skeleton|>
class Bucket3d:
"""buckets class is used for fast 3d point overlapping puts all points into buckets in each dimension only compare to reasonably nearby points assumes tolerance <<<< 1 angstrom"""
def __init__(self, pointList, tolerance):
"""takes the point list and makes buckets for search... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Bucket3d:
"""buckets class is used for fast 3d point overlapping puts all points into buckets in each dimension only compare to reasonably nearby points assumes tolerance <<<< 1 angstrom"""
def __init__(self, pointList, tolerance):
"""takes the point list and makes buckets for searching later. O(... | the_stack_v2_python_sparse | KCNQ/docking/scripts/common/buckets.py | jilimcaoco/MPProjects | train | 0 |
de31272b0541e67565800a46e2d4dc62e1ad32d8 | [
"if id:\n activity = session.restclient.lookup_activity(str(id))\n if '_attributes' not in activity.keys():\n raise NotFoundError(f'Activity not found: {id}')\nself.request_href = activity['_links']['request']['href']\nself.href = activity['_links']['self']['href']\nself.workitem_href = activity['_link... | <|body_start_0|>
if id:
activity = session.restclient.lookup_activity(str(id))
if '_attributes' not in activity.keys():
raise NotFoundError(f'Activity not found: {id}')
self.request_href = activity['_links']['request']['href']
self.href = activity['_links'... | Activity | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Activity:
def __init__(self, session: 'Session', activity=None, id: str=None):
"""Represents an ISIM Activity. Can do lookup using the id attribute. Args: session (Session): Active ISIM Session activity (zeep.WSActivity, optional): Activity object returned from ISIM REST API. Defaults to... | stack_v2_sparse_classes_36k_train_027255 | 2,619 | permissive | [
{
"docstring": "Represents an ISIM Activity. Can do lookup using the id attribute. Args: session (Session): Active ISIM Session activity (zeep.WSActivity, optional): Activity object returned from ISIM REST API. Defaults to None. id (str, optional): Activity ID for lookup. Defaults to None.",
"name": "__init... | 2 | stack_v2_sparse_classes_30k_train_002802 | Implement the Python class `Activity` described below.
Class description:
Implement the Activity class.
Method signatures and docstrings:
- def __init__(self, session: 'Session', activity=None, id: str=None): Represents an ISIM Activity. Can do lookup using the id attribute. Args: session (Session): Active ISIM Sessi... | Implement the Python class `Activity` described below.
Class description:
Implement the Activity class.
Method signatures and docstrings:
- def __init__(self, session: 'Session', activity=None, id: str=None): Represents an ISIM Activity. Can do lookup using the id attribute. Args: session (Session): Active ISIM Sessi... | c86e87ab1ca7e39169a1a8e565de2afc7ff49d52 | <|skeleton|>
class Activity:
def __init__(self, session: 'Session', activity=None, id: str=None):
"""Represents an ISIM Activity. Can do lookup using the id attribute. Args: session (Session): Active ISIM Session activity (zeep.WSActivity, optional): Activity object returned from ISIM REST API. Defaults to... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Activity:
def __init__(self, session: 'Session', activity=None, id: str=None):
"""Represents an ISIM Activity. Can do lookup using the id attribute. Args: session (Session): Active ISIM Session activity (zeep.WSActivity, optional): Activity object returned from ISIM REST API. Defaults to None. id (str... | the_stack_v2_python_sparse | pyisim/entities/activity.py | cazdlt/pyisim | train | 1 | |
bf244c8d59f7592201f06cb1c6b38b4b540dd931 | [
"if not request.user.has_perm('Users.prop_get'):\n return HttpResponseForbidden()\nuser = user_backend.get(username=name)\nvalue = property_backend.get(user=user, key=subname)\nreturn HttpRestAuthResponse(request, value)",
"if not request.user.has_perm('Users.prop_set'):\n return HttpResponseForbidden()\nva... | <|body_start_0|>
if not request.user.has_perm('Users.prop_get'):
return HttpResponseForbidden()
user = user_backend.get(username=name)
value = property_backend.get(user=user, key=subname)
return HttpRestAuthResponse(request, value)
<|end_body_0|>
<|body_start_1|>
if ... | Handle requests to ``/users/<user>/props/<prop>/``. | UserPropHandler | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class UserPropHandler:
"""Handle requests to ``/users/<user>/props/<prop>/``."""
def get(self, request, largs, name, subname):
"""Get value of a single property."""
<|body_0|>
def put(self, request, largs, name, subname):
"""Set value of a single property."""
<... | stack_v2_sparse_classes_36k_train_027256 | 9,743 | no_license | [
{
"docstring": "Get value of a single property.",
"name": "get",
"signature": "def get(self, request, largs, name, subname)"
},
{
"docstring": "Set value of a single property.",
"name": "put",
"signature": "def put(self, request, largs, name, subname)"
},
{
"docstring": "Delete a... | 3 | stack_v2_sparse_classes_30k_train_004090 | Implement the Python class `UserPropHandler` described below.
Class description:
Handle requests to ``/users/<user>/props/<prop>/``.
Method signatures and docstrings:
- def get(self, request, largs, name, subname): Get value of a single property.
- def put(self, request, largs, name, subname): Set value of a single p... | Implement the Python class `UserPropHandler` described below.
Class description:
Handle requests to ``/users/<user>/props/<prop>/``.
Method signatures and docstrings:
- def get(self, request, largs, name, subname): Get value of a single property.
- def put(self, request, largs, name, subname): Set value of a single p... | 60769f6b4965836b2220878cfa2e1bc403d8f8a3 | <|skeleton|>
class UserPropHandler:
"""Handle requests to ``/users/<user>/props/<prop>/``."""
def get(self, request, largs, name, subname):
"""Get value of a single property."""
<|body_0|>
def put(self, request, largs, name, subname):
"""Set value of a single property."""
<... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class UserPropHandler:
"""Handle requests to ``/users/<user>/props/<prop>/``."""
def get(self, request, largs, name, subname):
"""Get value of a single property."""
if not request.user.has_perm('Users.prop_get'):
return HttpResponseForbidden()
user = user_backend.get(usernam... | the_stack_v2_python_sparse | env/lib/python3.6/site-packages/RestAuth/Users/views.py | sachinlokesh05/login-registration-forgotpassword-and-resetpassword-using-django-rest-framework- | train | 3 |
49894df219e40238550e88364f9285d77d9ed199 | [
"def searchrow(start, end):\n if start > end:\n return -1\n mid = start + (end - start) // 2\n if matrix[mid][0] <= target <= matrix[mid][-1]:\n return mid\n elif target < matrix[mid][0]:\n return searchrow(start, mid - 1)\n else:\n return searchrow(mid + 1, end)\n\ndef se... | <|body_start_0|>
def searchrow(start, end):
if start > end:
return -1
mid = start + (end - start) // 2
if matrix[mid][0] <= target <= matrix[mid][-1]:
return mid
elif target < matrix[mid][0]:
return searchrow(start, ... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def searchMatrix(self, matrix, target):
""":type matrix: List[List[int]] :type target: int :rtype: bool 两次二分搜索 时间击败62.95%，内存击败15.94%"""
<|body_0|>
def searchMatrix1(self, matrix, target):
""":type matrix: List[List[int]] :type target: int :rtype: bool 一次二分搜... | stack_v2_sparse_classes_36k_train_027257 | 3,120 | no_license | [
{
"docstring": ":type matrix: List[List[int]] :type target: int :rtype: bool 两次二分搜索 时间击败62.95%，内存击败15.94%",
"name": "searchMatrix",
"signature": "def searchMatrix(self, matrix, target)"
},
{
"docstring": ":type matrix: List[List[int]] :type target: int :rtype: bool 一次二分搜索 时间击败62.95%，内存击败13.28%",... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def searchMatrix(self, matrix, target): :type matrix: List[List[int]] :type target: int :rtype: bool 两次二分搜索 时间击败62.95%，内存击败15.94%
- def searchMatrix1(self, matrix, target): :type... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def searchMatrix(self, matrix, target): :type matrix: List[List[int]] :type target: int :rtype: bool 两次二分搜索 时间击败62.95%，内存击败15.94%
- def searchMatrix1(self, matrix, target): :type... | 2dc982e690b153c33bc7e27a63604f754a0df90c | <|skeleton|>
class Solution:
def searchMatrix(self, matrix, target):
""":type matrix: List[List[int]] :type target: int :rtype: bool 两次二分搜索 时间击败62.95%，内存击败15.94%"""
<|body_0|>
def searchMatrix1(self, matrix, target):
""":type matrix: List[List[int]] :type target: int :rtype: bool 一次二分搜... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def searchMatrix(self, matrix, target):
""":type matrix: List[List[int]] :type target: int :rtype: bool 两次二分搜索 时间击败62.95%，内存击败15.94%"""
def searchrow(start, end):
if start > end:
return -1
mid = start + (end - start) // 2
if matrix[... | the_stack_v2_python_sparse | 74_search-a-2d-matrix.py | 95275059/Algorithm | train | 0 | |
627d36c735d93da8c56ff51b2646df9bf1a8d284 | [
"super().__init__(coordinator)\nself.entity_description = description\nself._attr_name = f'{name} {description.name}'\nself._attr_unique_id = f'{station_id}_{description.key}'\nself.station_id = f'{station_id}'\nself._attr_device_info = DeviceInfo(entry_type=DeviceEntryType.SERVICE, identifiers={(DOMAIN, station_id... | <|body_start_0|>
super().__init__(coordinator)
self.entity_description = description
self._attr_name = f'{name} {description.name}'
self._attr_unique_id = f'{station_id}_{description.key}'
self.station_id = f'{station_id}'
self._attr_device_info = DeviceInfo(entry_type=De... | Implementation of a ZAMG sensor. | ZamgSensor | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ZamgSensor:
"""Implementation of a ZAMG sensor."""
def __init__(self, coordinator: ZamgDataUpdateCoordinator, name: str, station_id: str, description: ZamgSensorEntityDescription) -> None:
"""Initialize the sensor."""
<|body_0|>
def native_value(self) -> StateType:
... | stack_v2_sparse_classes_36k_train_027258 | 7,261 | permissive | [
{
"docstring": "Initialize the sensor.",
"name": "__init__",
"signature": "def __init__(self, coordinator: ZamgDataUpdateCoordinator, name: str, station_id: str, description: ZamgSensorEntityDescription) -> None"
},
{
"docstring": "Return the state of the sensor.",
"name": "native_value",
... | 3 | stack_v2_sparse_classes_30k_train_014629 | Implement the Python class `ZamgSensor` described below.
Class description:
Implementation of a ZAMG sensor.
Method signatures and docstrings:
- def __init__(self, coordinator: ZamgDataUpdateCoordinator, name: str, station_id: str, description: ZamgSensorEntityDescription) -> None: Initialize the sensor.
- def native... | Implement the Python class `ZamgSensor` described below.
Class description:
Implementation of a ZAMG sensor.
Method signatures and docstrings:
- def __init__(self, coordinator: ZamgDataUpdateCoordinator, name: str, station_id: str, description: ZamgSensorEntityDescription) -> None: Initialize the sensor.
- def native... | 80caeafcb5b6e2f9da192d0ea6dd1a5b8244b743 | <|skeleton|>
class ZamgSensor:
"""Implementation of a ZAMG sensor."""
def __init__(self, coordinator: ZamgDataUpdateCoordinator, name: str, station_id: str, description: ZamgSensorEntityDescription) -> None:
"""Initialize the sensor."""
<|body_0|>
def native_value(self) -> StateType:
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ZamgSensor:
"""Implementation of a ZAMG sensor."""
def __init__(self, coordinator: ZamgDataUpdateCoordinator, name: str, station_id: str, description: ZamgSensorEntityDescription) -> None:
"""Initialize the sensor."""
super().__init__(coordinator)
self.entity_description = descrip... | the_stack_v2_python_sparse | homeassistant/components/zamg/sensor.py | home-assistant/core | train | 35,501 |
2aa37973f000896d6317b2964e0783f309c6e0c6 | [
"seen = set()\nnum = list(str(n))\nsum = 0\nwhile True:\n for i in num:\n sum += int(i) ** 2\n if sum == 1:\n return True\n if sum in seen:\n return False\n seen.add(sum)\n num = list(str(sum))\n sum = 0",
"def get_next(n):\n sum = 0\n num = list(str(n))\n for i in ... | <|body_start_0|>
seen = set()
num = list(str(n))
sum = 0
while True:
for i in num:
sum += int(i) ** 2
if sum == 1:
return True
if sum in seen:
return False
seen.add(sum)
num = list... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def isHappy_1(self, n):
"""*使用set 检查数字是否在哈希集中需要O(1)的时间，而对于其他数据结构，则需要O(n)的时间。选择正确的数据结构是解决这些问题的关键部分。 时间复杂度 o(logn) 空间复杂度 o(logn)"""
<|body_0|>
def isHappy_2(self, n):
"""*使用两个指针 时间复杂度 o(logn) 空间复杂度 o(1)"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|... | stack_v2_sparse_classes_36k_train_027259 | 1,243 | no_license | [
{
"docstring": "*使用set 检查数字是否在哈希集中需要O(1)的时间，而对于其他数据结构，则需要O(n)的时间。选择正确的数据结构是解决这些问题的关键部分。 时间复杂度 o(logn) 空间复杂度 o(logn)",
"name": "isHappy_1",
"signature": "def isHappy_1(self, n)"
},
{
"docstring": "*使用两个指针 时间复杂度 o(logn) 空间复杂度 o(1)",
"name": "isHappy_2",
"signature": "def isHappy_2(self, n)... | 2 | stack_v2_sparse_classes_30k_train_020491 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def isHappy_1(self, n): *使用set 检查数字是否在哈希集中需要O(1)的时间，而对于其他数据结构，则需要O(n)的时间。选择正确的数据结构是解决这些问题的关键部分。 时间复杂度 o(logn) 空间复杂度 o(logn)
- def isHappy_2(self, n): *使用两个指针 时间复杂度 o(logn) 空间复杂度 ... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def isHappy_1(self, n): *使用set 检查数字是否在哈希集中需要O(1)的时间，而对于其他数据结构，则需要O(n)的时间。选择正确的数据结构是解决这些问题的关键部分。 时间复杂度 o(logn) 空间复杂度 o(logn)
- def isHappy_2(self, n): *使用两个指针 时间复杂度 o(logn) 空间复杂度 ... | ebf9503d4bc6d4335c463aa2b4622dd7df55fb87 | <|skeleton|>
class Solution:
def isHappy_1(self, n):
"""*使用set 检查数字是否在哈希集中需要O(1)的时间，而对于其他数据结构，则需要O(n)的时间。选择正确的数据结构是解决这些问题的关键部分。 时间复杂度 o(logn) 空间复杂度 o(logn)"""
<|body_0|>
def isHappy_2(self, n):
"""*使用两个指针 时间复杂度 o(logn) 空间复杂度 o(1)"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def isHappy_1(self, n):
"""*使用set 检查数字是否在哈希集中需要O(1)的时间，而对于其他数据结构，则需要O(n)的时间。选择正确的数据结构是解决这些问题的关键部分。 时间复杂度 o(logn) 空间复杂度 o(logn)"""
seen = set()
num = list(str(n))
sum = 0
while True:
for i in num:
sum += int(i) ** 2
if su... | the_stack_v2_python_sparse | 202_happy_number.py | huuu97/LeetCode | train | 0 | |
662482f17739f20dbe58adc46aeb496bf9e4a96f | [
"if not os.path.isfile(imgDest):\n shutil.copy2(imgSrc, imgDest)\n im = Image.open(imgDest)\n startWidth, startHeight = im.size\n newWidth, newHeight = self.computeThumbnailSize(startWidth, startHeight, float(maxHeigth), float(maxWidth))\n im.thumbnail((newWidth, newHeight), Image.ANTIALIAS)\n im.... | <|body_start_0|>
if not os.path.isfile(imgDest):
shutil.copy2(imgSrc, imgDest)
im = Image.open(imgDest)
startWidth, startHeight = im.size
newWidth, newHeight = self.computeThumbnailSize(startWidth, startHeight, float(maxHeigth), float(maxWidth))
im.thu... | Creates thumbnail of images | Thubmnailer | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Thubmnailer:
"""Creates thumbnail of images"""
def cacheImg(self, imgSrc, imgDest, maxHeigth=800, maxWidth=600):
"""Given the src and dst of the image create the thumbnail"""
<|body_0|>
def computeThumbnailSize(self, startWidth, startHeight, maxHeight, maxWidth):
... | stack_v2_sparse_classes_36k_train_027260 | 1,124 | no_license | [
{
"docstring": "Given the src and dst of the image create the thumbnail",
"name": "cacheImg",
"signature": "def cacheImg(self, imgSrc, imgDest, maxHeigth=800, maxWidth=600)"
},
{
"docstring": "Compute the size of the thumbnail",
"name": "computeThumbnailSize",
"signature": "def computeTh... | 2 | stack_v2_sparse_classes_30k_train_003134 | Implement the Python class `Thubmnailer` described below.
Class description:
Creates thumbnail of images
Method signatures and docstrings:
- def cacheImg(self, imgSrc, imgDest, maxHeigth=800, maxWidth=600): Given the src and dst of the image create the thumbnail
- def computeThumbnailSize(self, startWidth, startHeigh... | Implement the Python class `Thubmnailer` described below.
Class description:
Creates thumbnail of images
Method signatures and docstrings:
- def cacheImg(self, imgSrc, imgDest, maxHeigth=800, maxWidth=600): Given the src and dst of the image create the thumbnail
- def computeThumbnailSize(self, startWidth, startHeigh... | 16c7a637b9a5dd4d854feb7ddc3d2aac308a46a0 | <|skeleton|>
class Thubmnailer:
"""Creates thumbnail of images"""
def cacheImg(self, imgSrc, imgDest, maxHeigth=800, maxWidth=600):
"""Given the src and dst of the image create the thumbnail"""
<|body_0|>
def computeThumbnailSize(self, startWidth, startHeight, maxHeight, maxWidth):
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Thubmnailer:
"""Creates thumbnail of images"""
def cacheImg(self, imgSrc, imgDest, maxHeigth=800, maxWidth=600):
"""Given the src and dst of the image create the thumbnail"""
if not os.path.isfile(imgDest):
shutil.copy2(imgSrc, imgDest)
im = Image.open(imgDest)
... | the_stack_v2_python_sparse | webapp/webapp/thumbnailer.py | slackeater/cca | train | 1 |
cd2f4524b8b0f1bf5869c7228957671d012236c2 | [
"super().__init__()\nchannels = size + size\nself.kernel_size = kernel_size\nif causal:\n self.lorder = kernel_size - 1\n padding = 0\nelse:\n self.lorder = 0\n padding = (kernel_size - 1) // 2\nself.conv = torch.nn.Conv1d(channels, channels, kernel_size, stride=1, padding=padding, groups=channels)",
... | <|body_start_0|>
super().__init__()
channels = size + size
self.kernel_size = kernel_size
if causal:
self.lorder = kernel_size - 1
padding = 0
else:
self.lorder = 0
padding = (kernel_size - 1) // 2
self.conv = torch.nn.Conv1... | Depth-wise Convolution module definition. Args: size: Initial size to determine the number of channels. kernel_size: Size of the convolving kernel. causal: Whether to use causal convolution (set to True if streaming). | DepthwiseConvolution | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DepthwiseConvolution:
"""Depth-wise Convolution module definition. Args: size: Initial size to determine the number of channels. kernel_size: Size of the convolving kernel. causal: Whether to use causal convolution (set to True if streaming)."""
def __init__(self, size: int, kernel_size: int... | stack_v2_sparse_classes_36k_train_027261 | 7,416 | permissive | [
{
"docstring": "Construct a DepthwiseConvolution object.",
"name": "__init__",
"signature": "def __init__(self, size: int, kernel_size: int, causal: bool=False) -> None"
},
{
"docstring": "Compute convolution module. Args: x: DepthwiseConvolution input sequences. (B, T, D_hidden) mask: Source ma... | 2 | null | Implement the Python class `DepthwiseConvolution` described below.
Class description:
Depth-wise Convolution module definition. Args: size: Initial size to determine the number of channels. kernel_size: Size of the convolving kernel. causal: Whether to use causal convolution (set to True if streaming).
Method signatu... | Implement the Python class `DepthwiseConvolution` described below.
Class description:
Depth-wise Convolution module definition. Args: size: Initial size to determine the number of channels. kernel_size: Size of the convolving kernel. causal: Whether to use causal convolution (set to True if streaming).
Method signatu... | bcd20948db7846ee523443ef9fd78c7a1248c95e | <|skeleton|>
class DepthwiseConvolution:
"""Depth-wise Convolution module definition. Args: size: Initial size to determine the number of channels. kernel_size: Size of the convolving kernel. causal: Whether to use causal convolution (set to True if streaming)."""
def __init__(self, size: int, kernel_size: int... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DepthwiseConvolution:
"""Depth-wise Convolution module definition. Args: size: Initial size to determine the number of channels. kernel_size: Size of the convolving kernel. causal: Whether to use causal convolution (set to True if streaming)."""
def __init__(self, size: int, kernel_size: int, causal: boo... | the_stack_v2_python_sparse | espnet2/asr_transducer/encoder/modules/convolution.py | espnet/espnet | train | 7,242 |
a626a9a56a02f87d97b7dc242bbcc957c99f69ad | [
"all_candidate_embs = [item.vectors for item in prf_candidates]\nnew_emb_qs = np.mean(np.vstack((emb_qs, all_candidate_embs)), axis=0)\nreturn new_emb_qs",
"qids = list()\nnew_emb_qs = list()\nfor index, topic_id in enumerate(topic_ids):\n qids.append(topic_id)\n new_emb_qs.append(self.get_prf_q_emb(emb_qs[... | <|body_start_0|>
all_candidate_embs = [item.vectors for item in prf_candidates]
new_emb_qs = np.mean(np.vstack((emb_qs, all_candidate_embs)), axis=0)
return new_emb_qs
<|end_body_0|>
<|body_start_1|>
qids = list()
new_emb_qs = list()
for index, topic_id in enumerate(topi... | DenseVectorAveragePrf | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DenseVectorAveragePrf:
def get_prf_q_emb(self, emb_qs: np.ndarray=None, prf_candidates: List[PRFDenseSearchResult]=None):
"""Perform Average PRF with Dense Vectors Parameters ---------- emb_qs : np.ndarray Query embedding prf_candidates : List[PRFDenseSearchResult] List of PRFDenseSearch... | stack_v2_sparse_classes_36k_train_027262 | 7,539 | permissive | [
{
"docstring": "Perform Average PRF with Dense Vectors Parameters ---------- emb_qs : np.ndarray Query embedding prf_candidates : List[PRFDenseSearchResult] List of PRFDenseSearchResult, contains document embeddings. Returns ------- np.ndarray return new query embeddings",
"name": "get_prf_q_emb",
"sign... | 2 | stack_v2_sparse_classes_30k_train_014369 | Implement the Python class `DenseVectorAveragePrf` described below.
Class description:
Implement the DenseVectorAveragePrf class.
Method signatures and docstrings:
- def get_prf_q_emb(self, emb_qs: np.ndarray=None, prf_candidates: List[PRFDenseSearchResult]=None): Perform Average PRF with Dense Vectors Parameters ---... | Implement the Python class `DenseVectorAveragePrf` described below.
Class description:
Implement the DenseVectorAveragePrf class.
Method signatures and docstrings:
- def get_prf_q_emb(self, emb_qs: np.ndarray=None, prf_candidates: List[PRFDenseSearchResult]=None): Perform Average PRF with Dense Vectors Parameters ---... | 42b354914b230880c91b2e4e70605b472441a9a1 | <|skeleton|>
class DenseVectorAveragePrf:
def get_prf_q_emb(self, emb_qs: np.ndarray=None, prf_candidates: List[PRFDenseSearchResult]=None):
"""Perform Average PRF with Dense Vectors Parameters ---------- emb_qs : np.ndarray Query embedding prf_candidates : List[PRFDenseSearchResult] List of PRFDenseSearch... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DenseVectorAveragePrf:
def get_prf_q_emb(self, emb_qs: np.ndarray=None, prf_candidates: List[PRFDenseSearchResult]=None):
"""Perform Average PRF with Dense Vectors Parameters ---------- emb_qs : np.ndarray Query embedding prf_candidates : List[PRFDenseSearchResult] List of PRFDenseSearchResult, contai... | the_stack_v2_python_sparse | pyserini/search/faiss/_prf.py | castorini/pyserini | train | 1,070 | |
d20e257168e6df054aacb4aa5603647dd664d013 | [
"super(GradientDescent, self).__init__(data, missing_values_mask, orthogonality_constraint, use_projection, regularizations, RSE_flag, dtype)\nnew_GS = []\nfor Gi, g_Gi in zip(self.G, self.g_G):\n new_GS.append(tf.assign(Gi, Gi - lr * g_Gi))\nfor Si, g_Si in zip(self.Sf, self.g_S):\n new_GS.append(tf.assign(S... | <|body_start_0|>
super(GradientDescent, self).__init__(data, missing_values_mask, orthogonality_constraint, use_projection, regularizations, RSE_flag, dtype)
new_GS = []
for Gi, g_Gi in zip(self.G, self.g_G):
new_GS.append(tf.assign(Gi, Gi - lr * g_Gi))
for Si, g_Si in zip(se... | Class that is able to perform ordinary gradient descent. | GradientDescent | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GradientDescent:
"""Class that is able to perform ordinary gradient descent."""
def __init__(self, data, missing_values_mask=None, orthogonality_constraint=False, use_projection=False, regularizations=[], lr=10, RSE_flag=True, dtype=tf.float64):
"""Constructs tensorflow computational... | stack_v2_sparse_classes_36k_train_027263 | 28,488 | no_license | [
{
"docstring": "Constructs tensorflow computational graph for ordinary gradient descent.",
"name": "__init__",
"signature": "def __init__(self, data, missing_values_mask=None, orthogonality_constraint=False, use_projection=False, regularizations=[], lr=10, RSE_flag=True, dtype=tf.float64)"
},
{
... | 2 | stack_v2_sparse_classes_30k_train_020301 | Implement the Python class `GradientDescent` described below.
Class description:
Class that is able to perform ordinary gradient descent.
Method signatures and docstrings:
- def __init__(self, data, missing_values_mask=None, orthogonality_constraint=False, use_projection=False, regularizations=[], lr=10, RSE_flag=Tru... | Implement the Python class `GradientDescent` described below.
Class description:
Class that is able to perform ordinary gradient descent.
Method signatures and docstrings:
- def __init__(self, data, missing_values_mask=None, orthogonality_constraint=False, use_projection=False, regularizations=[], lr=10, RSE_flag=Tru... | 6068f7ec1e01d4cfd0d293a7fa9f484e897dd06b | <|skeleton|>
class GradientDescent:
"""Class that is able to perform ordinary gradient descent."""
def __init__(self, data, missing_values_mask=None, orthogonality_constraint=False, use_projection=False, regularizations=[], lr=10, RSE_flag=True, dtype=tf.float64):
"""Constructs tensorflow computational... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class GradientDescent:
"""Class that is able to perform ordinary gradient descent."""
def __init__(self, data, missing_values_mask=None, orthogonality_constraint=False, use_projection=False, regularizations=[], lr=10, RSE_flag=True, dtype=tf.float64):
"""Constructs tensorflow computational graph for or... | the_stack_v2_python_sparse | local_search.py | GP-IJS/IJS-Data-Fusion | train | 0 |
5a38a44b295b0a50c2e572998ffad20fb7baf644 | [
"run = models.InteractivePipelineRun.query.filter_by(run_uuid=run_uuid).one_or_none()\nif run is None:\n abort(404, description='Run not found')\nreturn run.__dict__",
"post_data = request.get_json()\nres = models.InteractivePipelineRun.query.filter_by(run_uuid=run_uuid).update({'status': post_data['status']})... | <|body_start_0|>
run = models.InteractivePipelineRun.query.filter_by(run_uuid=run_uuid).one_or_none()
if run is None:
abort(404, description='Run not found')
return run.__dict__
<|end_body_0|>
<|body_start_1|>
post_data = request.get_json()
res = models.InteractivePi... | Run | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Run:
def get(self, run_uuid):
"""Fetches an interactive pipeline run given its UUID."""
<|body_0|>
def put(self, run_uuid):
"""Sets the status of a pipeline run."""
<|body_1|>
def delete(self, run_uuid):
"""Stops a pipeline run given its UUID."""... | stack_v2_sparse_classes_36k_train_027264 | 9,599 | permissive | [
{
"docstring": "Fetches an interactive pipeline run given its UUID.",
"name": "get",
"signature": "def get(self, run_uuid)"
},
{
"docstring": "Sets the status of a pipeline run.",
"name": "put",
"signature": "def put(self, run_uuid)"
},
{
"docstring": "Stops a pipeline run given ... | 3 | null | Implement the Python class `Run` described below.
Class description:
Implement the Run class.
Method signatures and docstrings:
- def get(self, run_uuid): Fetches an interactive pipeline run given its UUID.
- def put(self, run_uuid): Sets the status of a pipeline run.
- def delete(self, run_uuid): Stops a pipeline ru... | Implement the Python class `Run` described below.
Class description:
Implement the Run class.
Method signatures and docstrings:
- def get(self, run_uuid): Fetches an interactive pipeline run given its UUID.
- def put(self, run_uuid): Sets the status of a pipeline run.
- def delete(self, run_uuid): Stops a pipeline ru... | 0d78bf21e6da84754bd8ba8ebe4ff0d6631a92f9 | <|skeleton|>
class Run:
def get(self, run_uuid):
"""Fetches an interactive pipeline run given its UUID."""
<|body_0|>
def put(self, run_uuid):
"""Sets the status of a pipeline run."""
<|body_1|>
def delete(self, run_uuid):
"""Stops a pipeline run given its UUID."""... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Run:
def get(self, run_uuid):
"""Fetches an interactive pipeline run given its UUID."""
run = models.InteractivePipelineRun.query.filter_by(run_uuid=run_uuid).one_or_none()
if run is None:
abort(404, description='Run not found')
return run.__dict__
def put(self... | the_stack_v2_python_sparse | data/codefile/orchest@orchest__6b629d0__services$orchest-api$app$app$apis$namespace_runs.py.target.py | ualberta-smr/PyMigBench | train | 1 | |
6e6e2d47512cdbd5432c4001855a5b23687540ae | [
"self.net_desc = descript\nself.block = _prune_basic_block\nself.encoding = descript.get('encoding')\nself.chn = descript.get('chn')\nself.chn_node = descript.get('chn_node')\nself.chn_mask = descript.get('chn_mask', None)\nself.chn_node_mask = descript.get('chn_node_mask', None)\nself.num_blocks = descript.get('nu... | <|body_start_0|>
self.net_desc = descript
self.block = _prune_basic_block
self.encoding = descript.get('encoding')
self.chn = descript.get('chn')
self.chn_node = descript.get('chn_node')
self.chn_mask = descript.get('chn_mask', None)
self.chn_node_mask = descript.... | PruneResNet. :param descript: network desc :type descript: dict | PruneResNet | [
"Apache-2.0",
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PruneResNet:
"""PruneResNet. :param descript: network desc :type descript: dict"""
def __init__(self, descript):
"""Init PruneResNet."""
<|body_0|>
def _forward_prune_block(self, x, bottleneck, block, planes, inner_planes, num_blocks, stride, training, name):
"""... | stack_v2_sparse_classes_36k_train_027265 | 3,242 | permissive | [
{
"docstring": "Init PruneResNet.",
"name": "__init__",
"signature": "def __init__(self, descript)"
},
{
"docstring": "Create resolution block of ResNet.",
"name": "_forward_prune_block",
"signature": "def _forward_prune_block(self, x, bottleneck, block, planes, inner_planes, num_blocks,... | 3 | null | Implement the Python class `PruneResNet` described below.
Class description:
PruneResNet. :param descript: network desc :type descript: dict
Method signatures and docstrings:
- def __init__(self, descript): Init PruneResNet.
- def _forward_prune_block(self, x, bottleneck, block, planes, inner_planes, num_blocks, stri... | Implement the Python class `PruneResNet` described below.
Class description:
PruneResNet. :param descript: network desc :type descript: dict
Method signatures and docstrings:
- def __init__(self, descript): Init PruneResNet.
- def _forward_prune_block(self, x, bottleneck, block, planes, inner_planes, num_blocks, stri... | df51ed9c1d6dbde1deef63f2a037a369f8554406 | <|skeleton|>
class PruneResNet:
"""PruneResNet. :param descript: network desc :type descript: dict"""
def __init__(self, descript):
"""Init PruneResNet."""
<|body_0|>
def _forward_prune_block(self, x, bottleneck, block, planes, inner_planes, num_blocks, stride, training, name):
"""... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PruneResNet:
"""PruneResNet. :param descript: network desc :type descript: dict"""
def __init__(self, descript):
"""Init PruneResNet."""
self.net_desc = descript
self.block = _prune_basic_block
self.encoding = descript.get('encoding')
self.chn = descript.get('chn')... | the_stack_v2_python_sparse | built-in/TensorFlow/Research/cv/image_classification/Cars_for_TensorFlow/automl/vega/search_space/networks/tensorflow/backbones/prune_resnet.py | Huawei-Ascend/modelzoo | train | 1 |
b68c803b0d7409a155d5fdd3aa706f1d1f540f7c | [
"l = len(nums)\nif l <= 1:\n return l\ntemp = nums[0]\nself.mark = 1\ncount = 1\nfor i in nums[1:]:\n if i > temp:\n self.mark = 1\n break\n elif i < temp:\n self.mark = -1\n break\n else:\n self.mark = 0\n temp = i\n count += 1\nif self.mark == 0:\n return 1\... | <|body_start_0|>
l = len(nums)
if l <= 1:
return l
temp = nums[0]
self.mark = 1
count = 1
for i in nums[1:]:
if i > temp:
self.mark = 1
break
elif i < temp:
self.mark = -1
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def wiggleMaxLength(self, nums):
""":type nums: List[int] :rtype: int 32ms"""
<|body_0|>
def wiggleMaxLength_1(self, nums):
""":type nums: List[int] :rtype: int 29ms"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
l = len(nums)
if ... | stack_v2_sparse_classes_36k_train_027266 | 3,075 | no_license | [
{
"docstring": ":type nums: List[int] :rtype: int 32ms",
"name": "wiggleMaxLength",
"signature": "def wiggleMaxLength(self, nums)"
},
{
"docstring": ":type nums: List[int] :rtype: int 29ms",
"name": "wiggleMaxLength_1",
"signature": "def wiggleMaxLength_1(self, nums)"
}
] | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def wiggleMaxLength(self, nums): :type nums: List[int] :rtype: int 32ms
- def wiggleMaxLength_1(self, nums): :type nums: List[int] :rtype: int 29ms | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def wiggleMaxLength(self, nums): :type nums: List[int] :rtype: int 32ms
- def wiggleMaxLength_1(self, nums): :type nums: List[int] :rtype: int 29ms
<|skeleton|>
class Solution:
... | 679a2b246b8b6bb7fc55ed1c8096d3047d6d4461 | <|skeleton|>
class Solution:
def wiggleMaxLength(self, nums):
""":type nums: List[int] :rtype: int 32ms"""
<|body_0|>
def wiggleMaxLength_1(self, nums):
""":type nums: List[int] :rtype: int 29ms"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def wiggleMaxLength(self, nums):
""":type nums: List[int] :rtype: int 32ms"""
l = len(nums)
if l <= 1:
return l
temp = nums[0]
self.mark = 1
count = 1
for i in nums[1:]:
if i > temp:
self.mark = 1
... | the_stack_v2_python_sparse | WiggleSubsequence_MID_376.py | 953250587/leetcode-python | train | 2 | |
969cd429a2f1ed38233465f3efcfcd2348eb8cdb | [
"logging.debug('%s', request)\nbot_id = request.bot_id\nrealms.check_bot_get_acl(bot_id)\nbot = bot_management.get_info_key(bot_id).get()\ndeleted = False\nif not bot:\n events = bot_management.get_events_query(bot_id).fetch(1)\n if not events:\n raise endpoints.NotFoundException('%s not found.' % bot_... | <|body_start_0|>
logging.debug('%s', request)
bot_id = request.bot_id
realms.check_bot_get_acl(bot_id)
bot = bot_management.get_info_key(bot_id).get()
deleted = False
if not bot:
events = bot_management.get_events_query(bot_id).fetch(1)
if not even... | Bot-related API. Permits querying information about the bot's properties | SwarmingBotService | [
"Apache-2.0",
"LicenseRef-scancode-unknown-license-reference"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SwarmingBotService:
"""Bot-related API. Permits querying information about the bot's properties"""
def get(self, request):
"""Returns information about a known bot. This includes its state and dimensions, and if it is currently running a task."""
<|body_0|>
def delete(se... | stack_v2_sparse_classes_36k_train_027267 | 42,982 | permissive | [
{
"docstring": "Returns information about a known bot. This includes its state and dimensions, and if it is currently running a task.",
"name": "get",
"signature": "def get(self, request)"
},
{
"docstring": "Deletes the bot corresponding to a provided bot_id. At that point, the bot will not appe... | 5 | null | Implement the Python class `SwarmingBotService` described below.
Class description:
Bot-related API. Permits querying information about the bot's properties
Method signatures and docstrings:
- def get(self, request): Returns information about a known bot. This includes its state and dimensions, and if it is currently... | Implement the Python class `SwarmingBotService` described below.
Class description:
Bot-related API. Permits querying information about the bot's properties
Method signatures and docstrings:
- def get(self, request): Returns information about a known bot. This includes its state and dimensions, and if it is currently... | 10cc5fdcca53e2a1690867acbe6fce099273f092 | <|skeleton|>
class SwarmingBotService:
"""Bot-related API. Permits querying information about the bot's properties"""
def get(self, request):
"""Returns information about a known bot. This includes its state and dimensions, and if it is currently running a task."""
<|body_0|>
def delete(se... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SwarmingBotService:
"""Bot-related API. Permits querying information about the bot's properties"""
def get(self, request):
"""Returns information about a known bot. This includes its state and dimensions, and if it is currently running a task."""
logging.debug('%s', request)
bot_i... | the_stack_v2_python_sparse | appengine/swarming/handlers_endpoints.py | luci/luci-py | train | 84 |
1fe4cabbb4f4a39dbd98fc8783d192f90489670f | [
"array_len = len(array)\nif array_len == 0:\n return [-1, -1]\nmax_val = -9999\nmin_val = 9999\nfirst_pos = -1\nlast_pos = -1\nfor i in range(array_len):\n if max_val <= array[i]:\n max_val = max(array[i], max_val)\n else:\n last_pos = i\nfor i in range(array_len - 1, -1, -1):\n if min_val... | <|body_start_0|>
array_len = len(array)
if array_len == 0:
return [-1, -1]
max_val = -9999
min_val = 9999
first_pos = -1
last_pos = -1
for i in range(array_len):
if max_val <= array[i]:
max_val = max(array[i], max_val)
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def subSort1(self, array: List[int]) -> List[int]:
"""方法：左右指针 【两遍遍历】"""
<|body_0|>
def subSort(self, array: List[int]) -> List[int]:
"""方法：左右指针 【一遍遍历】"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
array_len = len(array)
if array_... | stack_v2_sparse_classes_36k_train_027268 | 1,820 | no_license | [
{
"docstring": "方法：左右指针 【两遍遍历】",
"name": "subSort1",
"signature": "def subSort1(self, array: List[int]) -> List[int]"
},
{
"docstring": "方法：左右指针 【一遍遍历】",
"name": "subSort",
"signature": "def subSort(self, array: List[int]) -> List[int]"
}
] | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def subSort1(self, array: List[int]) -> List[int]: 方法：左右指针 【两遍遍历】
- def subSort(self, array: List[int]) -> List[int]: 方法：左右指针 【一遍遍历】 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def subSort1(self, array: List[int]) -> List[int]: 方法：左右指针 【两遍遍历】
- def subSort(self, array: List[int]) -> List[int]: 方法：左右指针 【一遍遍历】
<|skeleton|>
class Solution:
def subSor... | f831fd9603592ae5bee3679924f962a3ebce381c | <|skeleton|>
class Solution:
def subSort1(self, array: List[int]) -> List[int]:
"""方法：左右指针 【两遍遍历】"""
<|body_0|>
def subSort(self, array: List[int]) -> List[int]:
"""方法：左右指针 【一遍遍历】"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def subSort1(self, array: List[int]) -> List[int]:
"""方法：左右指针 【两遍遍历】"""
array_len = len(array)
if array_len == 0:
return [-1, -1]
max_val = -9999
min_val = 9999
first_pos = -1
last_pos = -1
for i in range(array_len):
... | the_stack_v2_python_sparse | topic25_left_right_pointer/MS1616_subSort1/interview.py | GongFuXiong/leetcode | train | 0 | |
e28302e637fe6c223cf456d6e9fdbba18ebdef25 | [
"if not root:\n return\nself.flatten(root.left)\nself.flatten(root.right)\ntmp = root.right\nroot.right = root.left\nroot.left = None\nwhile root.right:\n root = root.right\nroot.right = tmp",
"cur = root\nwhile cur:\n if cur.left:\n p = cur.left\n while p.right:\n p = p.right\n ... | <|body_start_0|>
if not root:
return
self.flatten(root.left)
self.flatten(root.right)
tmp = root.right
root.right = root.left
root.left = None
while root.right:
root = root.right
root.right = tmp
<|end_body_0|>
<|body_start_1|>
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def flatten(self, root):
"""Do not return anything, modify root in-place instead."""
<|body_0|>
def flatten(self, root) -> None:
"""Do not return anything, modify root in-place instead."""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
if no... | stack_v2_sparse_classes_36k_train_027269 | 841 | no_license | [
{
"docstring": "Do not return anything, modify root in-place instead.",
"name": "flatten",
"signature": "def flatten(self, root)"
},
{
"docstring": "Do not return anything, modify root in-place instead.",
"name": "flatten",
"signature": "def flatten(self, root) -> None"
}
] | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def flatten(self, root): Do not return anything, modify root in-place instead.
- def flatten(self, root) -> None: Do not return anything, modify root in-place instead. | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def flatten(self, root): Do not return anything, modify root in-place instead.
- def flatten(self, root) -> None: Do not return anything, modify root in-place instead.
<|skeleto... | 18e6ac79573b3f535ca5e3eaa477eac0e60bf510 | <|skeleton|>
class Solution:
def flatten(self, root):
"""Do not return anything, modify root in-place instead."""
<|body_0|>
def flatten(self, root) -> None:
"""Do not return anything, modify root in-place instead."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def flatten(self, root):
"""Do not return anything, modify root in-place instead."""
if not root:
return
self.flatten(root.left)
self.flatten(root.right)
tmp = root.right
root.right = root.left
root.left = None
while root.ri... | the_stack_v2_python_sparse | leetcode_树_二叉树展开为链表.py | cmychina/Leetcode | train | 0 | |
e0f8954cac756eec29f1296797de34f8e8f65155 | [
"if variable in self:\n return super(_VariableBackups, self).__getitem__(variable)\nvalue = self[variable] = _RegisteredAddons(variable)\nreturn value",
"if not variable in self:\n return\nServerVar(variable).set(self[variable].default)\nsuper(_VariableBackups, self).__delitem__(variable)"
] | <|body_start_0|>
if variable in self:
return super(_VariableBackups, self).__getitem__(variable)
value = self[variable] = _RegisteredAddons(variable)
return value
<|end_body_0|>
<|body_start_1|>
if not variable in self:
return
ServerVar(variable).set(self... | Class used to store variables with their default value | _VariableBackups | [
"Artistic-1.0",
"LicenseRef-scancode-public-domain"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class _VariableBackups:
"""Class used to store variables with their default value"""
def __getitem__(self, variable):
"""Gets the variable's instance and adds it if not in the dictionary"""
<|body_0|>
def __delitem__(self, variable):
"""Removes the variable from the di... | stack_v2_sparse_classes_36k_train_027270 | 2,938 | permissive | [
{
"docstring": "Gets the variable's instance and adds it if not in the dictionary",
"name": "__getitem__",
"signature": "def __getitem__(self, variable)"
},
{
"docstring": "Removes the variable from the dictionary, and sets it back to the default value",
"name": "__delitem__",
"signature... | 2 | stack_v2_sparse_classes_30k_train_010931 | Implement the Python class `_VariableBackups` described below.
Class description:
Class used to store variables with their default value
Method signatures and docstrings:
- def __getitem__(self, variable): Gets the variable's instance and adds it if not in the dictionary
- def __delitem__(self, variable): Removes the... | Implement the Python class `_VariableBackups` described below.
Class description:
Class used to store variables with their default value
Method signatures and docstrings:
- def __getitem__(self, variable): Gets the variable's instance and adds it if not in the dictionary
- def __delitem__(self, variable): Removes the... | ebf4624626266f552189a32612b8d09cd5b4c5a3 | <|skeleton|>
class _VariableBackups:
"""Class used to store variables with their default value"""
def __getitem__(self, variable):
"""Gets the variable's instance and adds it if not in the dictionary"""
<|body_0|>
def __delitem__(self, variable):
"""Removes the variable from the di... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class _VariableBackups:
"""Class used to store variables with their default value"""
def __getitem__(self, variable):
"""Gets the variable's instance and adds it if not in the dictionary"""
if variable in self:
return super(_VariableBackups, self).__getitem__(variable)
value... | the_stack_v2_python_sparse | cstrike/addons/eventscripts/gungame51/modules/backups.py | GunGame-Dev-Team/GunGame51 | train | 0 |
0529cef78c2e3222f88562fe3bec9d906b7c2a79 | [
"self.cluster = cluster\nself.kubeconfig_path = os.path.join(config.ENV_DATA['cluster_path'], config.RUN['kubeconfig_location'])\nself.kubeadmin_password_path = os.path.join(config.ENV_DATA['cluster_path'], config.RUN['password_location'])\nabs_path = os.path.expanduser(self.kubeconfig_path)\nbase_path = os.path.di... | <|body_start_0|>
self.cluster = cluster
self.kubeconfig_path = os.path.join(config.ENV_DATA['cluster_path'], config.RUN['kubeconfig_location'])
self.kubeadmin_password_path = os.path.join(config.ENV_DATA['cluster_path'], config.RUN['password_location'])
abs_path = os.path.expanduser(self... | A helper class for Managed Service ROSA Cluster in Production Environment | ROSAProdEnvCluster | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ROSAProdEnvCluster:
"""A helper class for Managed Service ROSA Cluster in Production Environment"""
def __init__(self, cluster):
"""Initialize required variables Args: cluster (str): Name of the cluster in production environment"""
<|body_0|>
def create_admin_and_login(s... | stack_v2_sparse_classes_36k_train_027271 | 4,092 | permissive | [
{
"docstring": "Initialize required variables Args: cluster (str): Name of the cluster in production environment",
"name": "__init__",
"signature": "def __init__(self, cluster)"
},
{
"docstring": "creates admin account for cluster and login",
"name": "create_admin_and_login",
"signature"... | 6 | null | Implement the Python class `ROSAProdEnvCluster` described below.
Class description:
A helper class for Managed Service ROSA Cluster in Production Environment
Method signatures and docstrings:
- def __init__(self, cluster): Initialize required variables Args: cluster (str): Name of the cluster in production environmen... | Implement the Python class `ROSAProdEnvCluster` described below.
Class description:
A helper class for Managed Service ROSA Cluster in Production Environment
Method signatures and docstrings:
- def __init__(self, cluster): Initialize required variables Args: cluster (str): Name of the cluster in production environmen... | 5e9e504957403148e413326f65c3769bf9d8eb39 | <|skeleton|>
class ROSAProdEnvCluster:
"""A helper class for Managed Service ROSA Cluster in Production Environment"""
def __init__(self, cluster):
"""Initialize required variables Args: cluster (str): Name of the cluster in production environment"""
<|body_0|>
def create_admin_and_login(s... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ROSAProdEnvCluster:
"""A helper class for Managed Service ROSA Cluster in Production Environment"""
def __init__(self, cluster):
"""Initialize required variables Args: cluster (str): Name of the cluster in production environment"""
self.cluster = cluster
self.kubeconfig_path = os.... | the_stack_v2_python_sparse | ocs_ci/deployment/helpers/rosa_prod_cluster_helpers.py | red-hat-storage/ocs-ci | train | 146 |
6b6a862dff02b9df8b39debdaf08a21de4b7e589 | [
"self.validate_parameters(consumer_id=consumer_id, portfolio_id=portfolio_id, accept=accept, content_type=content_type)\n_url_path = '/decisioning/v1/consumers/{consumerId}/portfolios/{portfolioId}'\n_url_path = APIHelper.append_url_with_template_parameters(_url_path, {'consumerId': consumer_id, 'portfolioId': port... | <|body_start_0|>
self.validate_parameters(consumer_id=consumer_id, portfolio_id=portfolio_id, accept=accept, content_type=content_type)
_url_path = '/decisioning/v1/consumers/{consumerId}/portfolios/{portfolioId}'
_url_path = APIHelper.append_url_with_template_parameters(_url_path, {'consumerId'... | A Controller to access Endpoints in the finicityapi API. | GetPortfoliosController | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GetPortfoliosController:
"""A Controller to access Endpoints in the finicityapi API."""
def get_portfolio_by_consumer(self, consumer_id, portfolio_id, accept, content_type):
"""Does a GET request to /decisioning/v1/consumers/{consumerId}/portfolios/{portfolioId}. Returns a portfolio ... | stack_v2_sparse_classes_36k_train_027272 | 7,011 | permissive | [
{
"docstring": "Does a GET request to /decisioning/v1/consumers/{consumerId}/portfolios/{portfolioId}. Returns a portfolio of most recently generated report for each report type for a specified consumer. If there are multiple reports that were generated for a report type (VOA, VOI, etc), only the most recently ... | 2 | stack_v2_sparse_classes_30k_train_001783 | Implement the Python class `GetPortfoliosController` described below.
Class description:
A Controller to access Endpoints in the finicityapi API.
Method signatures and docstrings:
- def get_portfolio_by_consumer(self, consumer_id, portfolio_id, accept, content_type): Does a GET request to /decisioning/v1/consumers/{c... | Implement the Python class `GetPortfoliosController` described below.
Class description:
A Controller to access Endpoints in the finicityapi API.
Method signatures and docstrings:
- def get_portfolio_by_consumer(self, consumer_id, portfolio_id, accept, content_type): Does a GET request to /decisioning/v1/consumers/{c... | b2ab1ded435db75c78d42261f5e4acd2a3061487 | <|skeleton|>
class GetPortfoliosController:
"""A Controller to access Endpoints in the finicityapi API."""
def get_portfolio_by_consumer(self, consumer_id, portfolio_id, accept, content_type):
"""Does a GET request to /decisioning/v1/consumers/{consumerId}/portfolios/{portfolioId}. Returns a portfolio ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class GetPortfoliosController:
"""A Controller to access Endpoints in the finicityapi API."""
def get_portfolio_by_consumer(self, consumer_id, portfolio_id, accept, content_type):
"""Does a GET request to /decisioning/v1/consumers/{consumerId}/portfolios/{portfolioId}. Returns a portfolio of most recen... | the_stack_v2_python_sparse | finicityapi/controllers/get_portfolios_controller.py | monarchmoney/finicity-python | train | 0 |
bf2f9dabaef386cfb111b6cf0ed56d5f13d1b638 | [
"if maxdate and (not isinstance(maxdate, datetime)):\n raise ValueError('Expected a datetime object')\nreturn self.filter(id__in=self.get_interaction_per_client_ids(maxdate, active_only))",
"from django.db import connection\ncursor = connection.cursor()\ncfilter = 'expiration is null'\nsql = 'select reports_in... | <|body_start_0|>
if maxdate and (not isinstance(maxdate, datetime)):
raise ValueError('Expected a datetime object')
return self.filter(id__in=self.get_interaction_per_client_ids(maxdate, active_only))
<|end_body_0|>
<|body_start_1|>
from django.db import connection
cursor = ... | Manages interactions objects. | InteractiveManager | [
"LicenseRef-scancode-unknown-license-reference",
"mpich2",
"BSD-2-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class InteractiveManager:
"""Manages interactions objects."""
def interaction_per_client(self, maxdate=None, active_only=True):
"""Returns the most recent interactions for clients as of a date Arguments: maxdate -- datetime object. Most recent date to pull. (dafault None) active_only -- In... | stack_v2_sparse_classes_36k_train_027273 | 14,270 | permissive | [
{
"docstring": "Returns the most recent interactions for clients as of a date Arguments: maxdate -- datetime object. Most recent date to pull. (dafault None) active_only -- Include only active clients (default True)",
"name": "interaction_per_client",
"signature": "def interaction_per_client(self, maxda... | 2 | stack_v2_sparse_classes_30k_train_011167 | Implement the Python class `InteractiveManager` described below.
Class description:
Manages interactions objects.
Method signatures and docstrings:
- def interaction_per_client(self, maxdate=None, active_only=True): Returns the most recent interactions for clients as of a date Arguments: maxdate -- datetime object. M... | Implement the Python class `InteractiveManager` described below.
Class description:
Manages interactions objects.
Method signatures and docstrings:
- def interaction_per_client(self, maxdate=None, active_only=True): Returns the most recent interactions for clients as of a date Arguments: maxdate -- datetime object. M... | 8605cd3d0cb4d549cb8b43de945d447f6d82892a | <|skeleton|>
class InteractiveManager:
"""Manages interactions objects."""
def interaction_per_client(self, maxdate=None, active_only=True):
"""Returns the most recent interactions for clients as of a date Arguments: maxdate -- datetime object. Most recent date to pull. (dafault None) active_only -- In... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class InteractiveManager:
"""Manages interactions objects."""
def interaction_per_client(self, maxdate=None, active_only=True):
"""Returns the most recent interactions for clients as of a date Arguments: maxdate -- datetime object. Most recent date to pull. (dafault None) active_only -- Include only ac... | the_stack_v2_python_sparse | src/lib/Bcfg2/Server/Reports/reports/models.py | Bcfg2/bcfg2 | train | 56 |
f1f8b1d6f0a0890fb394dcec08c17e282adbd96b | [
"super().__init__(ccdata, *args, **kwargs)\nself.required_attrs = ('natom', 'atomcoords', 'atomnos')\nself.do_firstgeom = firstgeom\nself.do_lastgeom = lastgeom\nself.do_allgeom = allgeom\nself.natom = str(self.ccdata.natom)\nself.element_list = [self.pt.element[Z] for Z in self.ccdata.atomnos]",
"xyzblock = []\n... | <|body_start_0|>
super().__init__(ccdata, *args, **kwargs)
self.required_attrs = ('natom', 'atomcoords', 'atomnos')
self.do_firstgeom = firstgeom
self.do_lastgeom = lastgeom
self.do_allgeom = allgeom
self.natom = str(self.ccdata.natom)
self.element_list = [self.pt... | A writer for XYZ (Cartesian coordinate) files. | XYZ | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class XYZ:
"""A writer for XYZ (Cartesian coordinate) files."""
def __init__(self, ccdata: ccData, splitfiles: bool=False, firstgeom: bool=False, lastgeom: bool=False, allgeom: bool=False, *args, **kwargs) -> None:
"""Initialize the XYZ writer object. Inputs: ccdata - An instance of ccData... | stack_v2_sparse_classes_36k_train_027274 | 4,169 | permissive | [
{
"docstring": "Initialize the XYZ writer object. Inputs: ccdata - An instance of ccData, parse from a logfile. splitfiles - Boolean to write multiple files if multiple files are requested. [TODO] firstgeom - Boolean to write the first available geometry from the logfile. lastgeom - Boolean to write the last av... | 3 | stack_v2_sparse_classes_30k_train_007810 | Implement the Python class `XYZ` described below.
Class description:
A writer for XYZ (Cartesian coordinate) files.
Method signatures and docstrings:
- def __init__(self, ccdata: ccData, splitfiles: bool=False, firstgeom: bool=False, lastgeom: bool=False, allgeom: bool=False, *args, **kwargs) -> None: Initialize the ... | Implement the Python class `XYZ` described below.
Class description:
A writer for XYZ (Cartesian coordinate) files.
Method signatures and docstrings:
- def __init__(self, ccdata: ccData, splitfiles: bool=False, firstgeom: bool=False, lastgeom: bool=False, allgeom: bool=False, *args, **kwargs) -> None: Initialize the ... | b8d42a163ce9bafd4b660e2a933f56a8cc54fd9b | <|skeleton|>
class XYZ:
"""A writer for XYZ (Cartesian coordinate) files."""
def __init__(self, ccdata: ccData, splitfiles: bool=False, firstgeom: bool=False, lastgeom: bool=False, allgeom: bool=False, *args, **kwargs) -> None:
"""Initialize the XYZ writer object. Inputs: ccdata - An instance of ccData... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class XYZ:
"""A writer for XYZ (Cartesian coordinate) files."""
def __init__(self, ccdata: ccData, splitfiles: bool=False, firstgeom: bool=False, lastgeom: bool=False, allgeom: bool=False, *args, **kwargs) -> None:
"""Initialize the XYZ writer object. Inputs: ccdata - An instance of ccData, parse from ... | the_stack_v2_python_sparse | cclib/io/xyzwriter.py | cclib/cclib | train | 285 |
30f9d4225e51ca2e53fa89b8bc1fbbf65d449b3a | [
"q = DatabaseSessionManager.get_session().query(db.Node)\nauth_org_id = self.obtain_organization_id()\nargs = request.args\nfor param in ['organization_id', 'collaboration_id', 'status', 'ip']:\n if param in args:\n q = q.filter(getattr(db.Node, param) == args[param])\nif 'name' in args:\n q = q.filter... | <|body_start_0|>
q = DatabaseSessionManager.get_session().query(db.Node)
auth_org_id = self.obtain_organization_id()
args = request.args
for param in ['organization_id', 'collaboration_id', 'status', 'ip']:
if param in args:
q = q.filter(getattr(db.Node, param... | Nodes | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Nodes:
def get(self):
"""Returns a list of nodes --- description: >- Returns a list of nodes which are part of the organization to which the user or node belongs. In case an administrator account makes this request, all nodes from all organizations are returned. ### Permission Table |Rul... | stack_v2_sparse_classes_36k_train_027275 | 13,811 | permissive | [
{
"docstring": "Returns a list of nodes --- description: >- Returns a list of nodes which are part of the organization to which the user or node belongs. In case an administrator account makes this request, all nodes from all organizations are returned. ### Permission Table |Rule name|Scope|Operation|Node|Conta... | 2 | stack_v2_sparse_classes_30k_train_015518 | Implement the Python class `Nodes` described below.
Class description:
Implement the Nodes class.
Method signatures and docstrings:
- def get(self): Returns a list of nodes --- description: >- Returns a list of nodes which are part of the organization to which the user or node belongs. In case an administrator accoun... | Implement the Python class `Nodes` described below.
Class description:
Implement the Nodes class.
Method signatures and docstrings:
- def get(self): Returns a list of nodes --- description: >- Returns a list of nodes which are part of the organization to which the user or node belongs. In case an administrator accoun... | 3326c51a56e5a6be7c67954aaf65a7d07b07ae74 | <|skeleton|>
class Nodes:
def get(self):
"""Returns a list of nodes --- description: >- Returns a list of nodes which are part of the organization to which the user or node belongs. In case an administrator account makes this request, all nodes from all organizations are returned. ### Permission Table |Rul... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Nodes:
def get(self):
"""Returns a list of nodes --- description: >- Returns a list of nodes which are part of the organization to which the user or node belongs. In case an administrator account makes this request, all nodes from all organizations are returned. ### Permission Table |Rule name|Scope|O... | the_stack_v2_python_sparse | vantage6/server/resource/node.py | IKNL/vantage6-server | train | 2 | |
08093afd721ae8ecc2e218e36588a86febae0bd9 | [
"tests = [('foo[[1-5]]', ['foo1', 'foo2', 'foo5'], ['foo', 'foo0', 'foo10']), ('[[10-99]]foo', ['10foo', '99foo', '25foo'], ['foo', '1foo', '999foo', '110foo']), ('foo[[1,3,5-10]]bar', ['foo1bar', 'foo7bar', 'foo10bar'], ['foo2bar', 'foobar', 'foo3', '5bar']), ('[[9-15]]foo[[16-20]]', ['9foo18', '13foo17'], ['8foo2... | <|body_start_0|>
tests = [('foo[[1-5]]', ['foo1', 'foo2', 'foo5'], ['foo', 'foo0', 'foo10']), ('[[10-99]]foo', ['10foo', '99foo', '25foo'], ['foo', '1foo', '999foo', '110foo']), ('foo[[1,3,5-10]]bar', ['foo1bar', 'foo7bar', 'foo10bar'], ['foo2bar', 'foobar', 'foo3', '5bar']), ('[[9-15]]foo[[16-20]]', ['9foo18',... | TestPatternMap | [
"mpich2",
"BSD-2-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestPatternMap:
def test_ranges(self):
"""test processing NameRange patterns"""
<|body_0|>
def test_simple_patterns(self):
"""test processing NamePatterns without backreferences"""
<|body_1|>
def test_backref_patterns(self):
"""test NamePatterns ... | stack_v2_sparse_classes_36k_train_027276 | 5,288 | permissive | [
{
"docstring": "test processing NameRange patterns",
"name": "test_ranges",
"signature": "def test_ranges(self)"
},
{
"docstring": "test processing NamePatterns without backreferences",
"name": "test_simple_patterns",
"signature": "def test_simple_patterns(self)"
},
{
"docstring"... | 3 | stack_v2_sparse_classes_30k_train_003533 | Implement the Python class `TestPatternMap` described below.
Class description:
Implement the TestPatternMap class.
Method signatures and docstrings:
- def test_ranges(self): test processing NameRange patterns
- def test_simple_patterns(self): test processing NamePatterns without backreferences
- def test_backref_pat... | Implement the Python class `TestPatternMap` described below.
Class description:
Implement the TestPatternMap class.
Method signatures and docstrings:
- def test_ranges(self): test processing NameRange patterns
- def test_simple_patterns(self): test processing NamePatterns without backreferences
- def test_backref_pat... | dc5ec0f8899f2a15aea34468899b7b9237874828 | <|skeleton|>
class TestPatternMap:
def test_ranges(self):
"""test processing NameRange patterns"""
<|body_0|>
def test_simple_patterns(self):
"""test processing NamePatterns without backreferences"""
<|body_1|>
def test_backref_patterns(self):
"""test NamePatterns ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TestPatternMap:
def test_ranges(self):
"""test processing NameRange patterns"""
tests = [('foo[[1-5]]', ['foo1', 'foo2', 'foo5'], ['foo', 'foo0', 'foo10']), ('[[10-99]]foo', ['10foo', '99foo', '25foo'], ['foo', '1foo', '999foo', '110foo']), ('foo[[1,3,5-10]]bar', ['foo1bar', 'foo7bar', 'foo10b... | the_stack_v2_python_sparse | testsuite/Testsrc/Testlib/TestServer/TestPlugins/TestGroupPatterns.py | dikim33/bcfg2 | train | 0 | |
b7a6db7d5160ee083d221fa7e6384e1b238defa1 | [
"super().save_model(request, obj, form, change)\nfrom celery_tasks.tasks import generate_static_index_html\ngenerate_static_index_html.delay()\ncache.delete('index_page_data')",
"super().delete_model(request, obj)\nfrom celery_tasks.tasks import generate_static_index_html\ngenerate_static_index_html.delay()\ncach... | <|body_start_0|>
super().save_model(request, obj, form, change)
from celery_tasks.tasks import generate_static_index_html
generate_static_index_html.delay()
cache.delete('index_page_data')
<|end_body_0|>
<|body_start_1|>
super().delete_model(request, obj)
from celery_tas... | BaseModelAdmin | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BaseModelAdmin:
def save_model(self, request, obj, form, change):
"""新增或更新表中的数据时调用"""
<|body_0|>
def delete_model(self, request, obj):
"""删除表中的数据时调用"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
super().save_model(request, obj, form, change)
... | stack_v2_sparse_classes_36k_train_027277 | 2,214 | no_license | [
{
"docstring": "新增或更新表中的数据时调用",
"name": "save_model",
"signature": "def save_model(self, request, obj, form, change)"
},
{
"docstring": "删除表中的数据时调用",
"name": "delete_model",
"signature": "def delete_model(self, request, obj)"
}
] | 2 | stack_v2_sparse_classes_30k_train_008098 | Implement the Python class `BaseModelAdmin` described below.
Class description:
Implement the BaseModelAdmin class.
Method signatures and docstrings:
- def save_model(self, request, obj, form, change): 新增或更新表中的数据时调用
- def delete_model(self, request, obj): 删除表中的数据时调用 | Implement the Python class `BaseModelAdmin` described below.
Class description:
Implement the BaseModelAdmin class.
Method signatures and docstrings:
- def save_model(self, request, obj, form, change): 新增或更新表中的数据时调用
- def delete_model(self, request, obj): 删除表中的数据时调用
<|skeleton|>
class BaseModelAdmin:
def save_m... | ae2b067da0394470269de97e1df52b33465d0de4 | <|skeleton|>
class BaseModelAdmin:
def save_model(self, request, obj, form, change):
"""新增或更新表中的数据时调用"""
<|body_0|>
def delete_model(self, request, obj):
"""删除表中的数据时调用"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class BaseModelAdmin:
def save_model(self, request, obj, form, change):
"""新增或更新表中的数据时调用"""
super().save_model(request, obj, form, change)
from celery_tasks.tasks import generate_static_index_html
generate_static_index_html.delay()
cache.delete('index_page_data')
def del... | the_stack_v2_python_sparse | untitled/6.0-全栈开发阶段/Django框架/dailyfresh/apps/goods/admin.py | giant-xf/python | train | 0 | |
9d318f494b535b2c707b19efd886542b4ec0ba0d | [
"super(DQN, self).__init__()\nself.fc1 = nn.Linear(state_size, fc1_units)\nself.fc2 = nn.Linear(fc1_units, fc2_units)\nself.fc3 = nn.Linear(fc2_units, action_size)",
"x = F.relu(self.fc1(state))\nx = F.relu(self.fc2(x))\nx = self.fc3(x)\nreturn x"
] | <|body_start_0|>
super(DQN, self).__init__()
self.fc1 = nn.Linear(state_size, fc1_units)
self.fc2 = nn.Linear(fc1_units, fc2_units)
self.fc3 = nn.Linear(fc2_units, action_size)
<|end_body_0|>
<|body_start_1|>
x = F.relu(self.fc1(state))
x = F.relu(self.fc2(x))
x ... | DQN | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DQN:
def __init__(self, state_size, action_size, fc1_units=64, fc2_units=64):
"""Initialize parameters and build model. Args: state_size (int): Dimension of each state action_size (int): Dimension of each action fc1_units (int): Number of nodes in first hidden layer fc2_units (int): Numb... | stack_v2_sparse_classes_36k_train_027278 | 11,094 | no_license | [
{
"docstring": "Initialize parameters and build model. Args: state_size (int): Dimension of each state action_size (int): Dimension of each action fc1_units (int): Number of nodes in first hidden layer fc2_units (int): Number of nodes in second hidden layer",
"name": "__init__",
"signature": "def __init... | 2 | stack_v2_sparse_classes_30k_train_008205 | Implement the Python class `DQN` described below.
Class description:
Implement the DQN class.
Method signatures and docstrings:
- def __init__(self, state_size, action_size, fc1_units=64, fc2_units=64): Initialize parameters and build model. Args: state_size (int): Dimension of each state action_size (int): Dimension... | Implement the Python class `DQN` described below.
Class description:
Implement the DQN class.
Method signatures and docstrings:
- def __init__(self, state_size, action_size, fc1_units=64, fc2_units=64): Initialize parameters and build model. Args: state_size (int): Dimension of each state action_size (int): Dimension... | 533380c7207f66d3a31d0bf744448ec2d4eb4c8f | <|skeleton|>
class DQN:
def __init__(self, state_size, action_size, fc1_units=64, fc2_units=64):
"""Initialize parameters and build model. Args: state_size (int): Dimension of each state action_size (int): Dimension of each action fc1_units (int): Number of nodes in first hidden layer fc2_units (int): Numb... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DQN:
def __init__(self, state_size, action_size, fc1_units=64, fc2_units=64):
"""Initialize parameters and build model. Args: state_size (int): Dimension of each state action_size (int): Dimension of each action fc1_units (int): Number of nodes in first hidden layer fc2_units (int): Number of nodes in... | the_stack_v2_python_sparse | rl/net/net.py | haonguyen1915/RL | train | 2 | |
7939ac8a4964012ed3504b395d8e172c8787c94f | [
"completed_process = subprocess.run(command, executable='/bin/bash', shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE, universal_newlines=True, timeout=timeout)\nif completed_process.returncode != 0:\n raise Exception(completed_process.stderr)\nreturn completed_process.stdout.strip('\\n')",
"process ... | <|body_start_0|>
completed_process = subprocess.run(command, executable='/bin/bash', shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE, universal_newlines=True, timeout=timeout)
if completed_process.returncode != 0:
raise Exception(completed_process.stderr)
return completed_... | Wrapper class of subprocess, with CliException integrated. | Subprocess | [
"LicenseRef-scancode-generic-cla",
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Subprocess:
"""Wrapper class of subprocess, with CliException integrated."""
def run(command: str, timeout: int=None) -> str:
"""Run one-time command with subprocess.run(). Args: command (str): command to be executed. timeout (int): timeout in seconds. Returns: str: return stdout of ... | stack_v2_sparse_classes_36k_train_027279 | 1,834 | permissive | [
{
"docstring": "Run one-time command with subprocess.run(). Args: command (str): command to be executed. timeout (int): timeout in seconds. Returns: str: return stdout of the command.",
"name": "run",
"signature": "def run(command: str, timeout: int=None) -> str"
},
{
"docstring": "Run one-time ... | 2 | null | Implement the Python class `Subprocess` described below.
Class description:
Wrapper class of subprocess, with CliException integrated.
Method signatures and docstrings:
- def run(command: str, timeout: int=None) -> str: Run one-time command with subprocess.run(). Args: command (str): command to be executed. timeout (... | Implement the Python class `Subprocess` described below.
Class description:
Wrapper class of subprocess, with CliException integrated.
Method signatures and docstrings:
- def run(command: str, timeout: int=None) -> str: Run one-time command with subprocess.run(). Args: command (str): command to be executed. timeout (... | b3c6a589ad9036b03221e776a6929b2bc1eb4680 | <|skeleton|>
class Subprocess:
"""Wrapper class of subprocess, with CliException integrated."""
def run(command: str, timeout: int=None) -> str:
"""Run one-time command with subprocess.run(). Args: command (str): command to be executed. timeout (int): timeout in seconds. Returns: str: return stdout of ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Subprocess:
"""Wrapper class of subprocess, with CliException integrated."""
def run(command: str, timeout: int=None) -> str:
"""Run one-time command with subprocess.run(). Args: command (str): command to be executed. timeout (int): timeout in seconds. Returns: str: return stdout of the command."... | the_stack_v2_python_sparse | maro/cli/grass/lib/scripts/utils/subprocess.py | microsoft/maro | train | 764 |
522ad99d82653dba2719507d8e27aa692ab49f1f | [
"self.key = key\nif L is None:\n self.head = None\n return\nif not len(L[:1]):\n self.head = None\n return\nnode = Node(L[0])\nself.head = node\nfor e in L[1:]:\n node.next_node = Node(e)\n node = node.next_node",
"if not self.head:\n self.head = Node(value)\n return\nnode = self.head\nwhi... | <|body_start_0|>
self.key = key
if L is None:
self.head = None
return
if not len(L[:1]):
self.head = None
return
node = Node(L[0])
self.head = node
for e in L[1:]:
node.next_node = Node(e)
node = node... | LinkedList | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LinkedList:
def __init__(self, L=None, key=lambda x: x):
"""Creates an empty list or a list built from a subscriptable object, the key of each value being by default the value itself. >>> LinkedList().print() >>> LinkedList([]).print() >>> LinkedList((0,)).print() 0 >>> LinkedList(range(... | stack_v2_sparse_classes_36k_train_027280 | 2,815 | no_license | [
{
"docstring": "Creates an empty list or a list built from a subscriptable object, the key of each value being by default the value itself. >>> LinkedList().print() >>> LinkedList([]).print() >>> LinkedList((0,)).print() 0 >>> LinkedList(range(4)).print() 0, 1, 2, 3",
"name": "__init__",
"signature": "d... | 4 | stack_v2_sparse_classes_30k_train_020522 | Implement the Python class `LinkedList` described below.
Class description:
Implement the LinkedList class.
Method signatures and docstrings:
- def __init__(self, L=None, key=lambda x: x): Creates an empty list or a list built from a subscriptable object, the key of each value being by default the value itself. >>> L... | Implement the Python class `LinkedList` described below.
Class description:
Implement the LinkedList class.
Method signatures and docstrings:
- def __init__(self, L=None, key=lambda x: x): Creates an empty list or a list built from a subscriptable object, the key of each value being by default the value itself. >>> L... | 40066e70922c912bb24c5add6e3678fadbb3343d | <|skeleton|>
class LinkedList:
def __init__(self, L=None, key=lambda x: x):
"""Creates an empty list or a list built from a subscriptable object, the key of each value being by default the value itself. >>> LinkedList().print() >>> LinkedList([]).print() >>> LinkedList((0,)).print() 0 >>> LinkedList(range(... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class LinkedList:
def __init__(self, L=None, key=lambda x: x):
"""Creates an empty list or a list built from a subscriptable object, the key of each value being by default the value itself. >>> LinkedList().print() >>> LinkedList([]).print() >>> LinkedList((0,)).print() 0 >>> LinkedList(range(4)).print() 0,... | the_stack_v2_python_sparse | lecture/Lecture_8/test1.py | DARRENSKY/COMP9021 | train | 4 | |
39269808181188a1b7a3e12542475db7d6bd7cec | [
"dp = [float('inf') for _ in range(amount + 1)]\ndp[0] = 0\nfor i in range(1, amount + 1):\n if i in coins:\n dp[i] = 1\n else:\n for j in range(1, i / 2 + 1):\n dp[i] = min(dp[i], dp[j] + dp[i - j])\nif dp[amount] == float('inf'):\n return -1\nelse:\n return dp[amount]",
"dp ... | <|body_start_0|>
dp = [float('inf') for _ in range(amount + 1)]
dp[0] = 0
for i in range(1, amount + 1):
if i in coins:
dp[i] = 1
else:
for j in range(1, i / 2 + 1):
dp[i] = min(dp[i], dp[j] + dp[i - j])
if dp[am... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def coinChange(self, coins, amount):
""":type coins: List[int] :type amount: int :rtype: int"""
<|body_0|>
def coinChangeSol(self, coins, amount):
""":type coins: List[int] :type amount: int :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_... | stack_v2_sparse_classes_36k_train_027281 | 1,308 | no_license | [
{
"docstring": ":type coins: List[int] :type amount: int :rtype: int",
"name": "coinChange",
"signature": "def coinChange(self, coins, amount)"
},
{
"docstring": ":type coins: List[int] :type amount: int :rtype: int",
"name": "coinChangeSol",
"signature": "def coinChangeSol(self, coins, ... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def coinChange(self, coins, amount): :type coins: List[int] :type amount: int :rtype: int
- def coinChangeSol(self, coins, amount): :type coins: List[int] :type amount: int :rtyp... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def coinChange(self, coins, amount): :type coins: List[int] :type amount: int :rtype: int
- def coinChangeSol(self, coins, amount): :type coins: List[int] :type amount: int :rtyp... | 7fa160362ebb58e7286b490012542baa2d51e5c9 | <|skeleton|>
class Solution:
def coinChange(self, coins, amount):
""":type coins: List[int] :type amount: int :rtype: int"""
<|body_0|>
def coinChangeSol(self, coins, amount):
""":type coins: List[int] :type amount: int :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def coinChange(self, coins, amount):
""":type coins: List[int] :type amount: int :rtype: int"""
dp = [float('inf') for _ in range(amount + 1)]
dp[0] = 0
for i in range(1, amount + 1):
if i in coins:
dp[i] = 1
else:
... | the_stack_v2_python_sparse | dp/coin_change.py | gerrycfchang/leetcode-python | train | 2 | |
611b86dcd5d5c9047a307205d910633053ff4893 | [
"super().__init__(coordinator)\nself.entity_description = description\nself._attr_unique_id = f'{coordinator.unique_id}_{description.key}'\nself._attr_device_info = DeviceInfo(identifiers={(DOMAIN, coordinator.unique_id)}, manufacturer='Solar-Log', name=coordinator.name, configuration_url=coordinator.host)",
"raw... | <|body_start_0|>
super().__init__(coordinator)
self.entity_description = description
self._attr_unique_id = f'{coordinator.unique_id}_{description.key}'
self._attr_device_info = DeviceInfo(identifiers={(DOMAIN, coordinator.unique_id)}, manufacturer='Solar-Log', name=coordinator.name, con... | Representation of a Sensor. | SolarlogSensor | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SolarlogSensor:
"""Representation of a Sensor."""
def __init__(self, coordinator: SolarlogData, description: SolarLogSensorEntityDescription) -> None:
"""Initialize the sensor."""
<|body_0|>
def native_value(self):
"""Return the native sensor value."""
<|... | stack_v2_sparse_classes_36k_train_027282 | 8,831 | permissive | [
{
"docstring": "Initialize the sensor.",
"name": "__init__",
"signature": "def __init__(self, coordinator: SolarlogData, description: SolarLogSensorEntityDescription) -> None"
},
{
"docstring": "Return the native sensor value.",
"name": "native_value",
"signature": "def native_value(self... | 2 | null | Implement the Python class `SolarlogSensor` described below.
Class description:
Representation of a Sensor.
Method signatures and docstrings:
- def __init__(self, coordinator: SolarlogData, description: SolarLogSensorEntityDescription) -> None: Initialize the sensor.
- def native_value(self): Return the native sensor... | Implement the Python class `SolarlogSensor` described below.
Class description:
Representation of a Sensor.
Method signatures and docstrings:
- def __init__(self, coordinator: SolarlogData, description: SolarLogSensorEntityDescription) -> None: Initialize the sensor.
- def native_value(self): Return the native sensor... | 80caeafcb5b6e2f9da192d0ea6dd1a5b8244b743 | <|skeleton|>
class SolarlogSensor:
"""Representation of a Sensor."""
def __init__(self, coordinator: SolarlogData, description: SolarLogSensorEntityDescription) -> None:
"""Initialize the sensor."""
<|body_0|>
def native_value(self):
"""Return the native sensor value."""
<|... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SolarlogSensor:
"""Representation of a Sensor."""
def __init__(self, coordinator: SolarlogData, description: SolarLogSensorEntityDescription) -> None:
"""Initialize the sensor."""
super().__init__(coordinator)
self.entity_description = description
self._attr_unique_id = f'... | the_stack_v2_python_sparse | homeassistant/components/solarlog/sensor.py | home-assistant/core | train | 35,501 |
caa6a6ac0fe59627a6ea889972f66422774f7bb8 | [
"params = super().get_default_params(with_embedding=True, with_multi_layer_perceptron=True)\nparams.add(engine.Param(name='mask_value', value=-1, desc='The value to be masked from inputs.'))\nparams['optimizer'] = 'adam'\nparams['input_shapes'] = [(5,), (5, 30)]\nreturn params",
"query = keras.layers.Input(name='... | <|body_start_0|>
params = super().get_default_params(with_embedding=True, with_multi_layer_perceptron=True)
params.add(engine.Param(name='mask_value', value=-1, desc='The value to be masked from inputs.'))
params['optimizer'] = 'adam'
params['input_shapes'] = [(5,), (5, 30)]
retu... | DRMM Model. Examples: >>> model = DRMM() >>> model.params['mlp_num_layers'] = 1 >>> model.params['mlp_num_units'] = 5 >>> model.params['mlp_num_fan_out'] = 1 >>> model.params['mlp_activation_func'] = 'tanh' >>> model.guess_and_fill_missing_params(verbose=0) >>> model.build() >>> model.compile() | DRMM | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DRMM:
"""DRMM Model. Examples: >>> model = DRMM() >>> model.params['mlp_num_layers'] = 1 >>> model.params['mlp_num_units'] = 5 >>> model.params['mlp_num_fan_out'] = 1 >>> model.params['mlp_activation_func'] = 'tanh' >>> model.guess_and_fill_missing_params(verbose=0) >>> model.build() >>> model.co... | stack_v2_sparse_classes_36k_train_027283 | 4,114 | permissive | [
{
"docstring": ":return: model default parameters.",
"name": "get_default_params",
"signature": "def get_default_params(cls) -> engine.ParamTable"
},
{
"docstring": "Build model structure.",
"name": "build",
"signature": "def build(self)"
},
{
"docstring": "Performs attention on ... | 3 | stack_v2_sparse_classes_30k_train_000787 | Implement the Python class `DRMM` described below.
Class description:
DRMM Model. Examples: >>> model = DRMM() >>> model.params['mlp_num_layers'] = 1 >>> model.params['mlp_num_units'] = 5 >>> model.params['mlp_num_fan_out'] = 1 >>> model.params['mlp_activation_func'] = 'tanh' >>> model.guess_and_fill_missing_params(ve... | Implement the Python class `DRMM` described below.
Class description:
DRMM Model. Examples: >>> model = DRMM() >>> model.params['mlp_num_layers'] = 1 >>> model.params['mlp_num_units'] = 5 >>> model.params['mlp_num_fan_out'] = 1 >>> model.params['mlp_activation_func'] = 'tanh' >>> model.guess_and_fill_missing_params(ve... | 1fe2afca7bc2aa0fd8af8f80df84a2665367d13c | <|skeleton|>
class DRMM:
"""DRMM Model. Examples: >>> model = DRMM() >>> model.params['mlp_num_layers'] = 1 >>> model.params['mlp_num_units'] = 5 >>> model.params['mlp_num_fan_out'] = 1 >>> model.params['mlp_activation_func'] = 'tanh' >>> model.guess_and_fill_missing_params(verbose=0) >>> model.build() >>> model.co... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DRMM:
"""DRMM Model. Examples: >>> model = DRMM() >>> model.params['mlp_num_layers'] = 1 >>> model.params['mlp_num_units'] = 5 >>> model.params['mlp_num_fan_out'] = 1 >>> model.params['mlp_activation_func'] = 'tanh' >>> model.guess_and_fill_missing_params(verbose=0) >>> model.build() >>> model.compile()"""
... | the_stack_v2_python_sparse | matchzoo/models/drmm.py | zhanzecheng/MatchZoo | train | 2 |
5dc5837ff9dca9fd1a89e86f56e844740d996ce7 | [
"database = None\nhost = 'localhost'\nport = 5432\nuse_ssl = self._config.get('use_ssl', False)\nusername = None\npassword = None\nif 'database_host' in self._config:\n host = self._config['database_host']\nif 'database_port' in self._config:\n port = self._config['database_port']\nif 'database_name' in self.... | <|body_start_0|>
database = None
host = 'localhost'
port = 5432
use_ssl = self._config.get('use_ssl', False)
username = None
password = None
if 'database_host' in self._config:
host = self._config['database_host']
if 'database_port' in self._co... | # PostgreSQL Import performance and usage data from a PostgreSQL server. An [Agent Plugin](https://app.scalyr.com/help/scalyr-agent#plugins) is a component of the Scalyr Agent, enabling the collection of more data. The source code for each plugin is available on [Github](https://github.com/scalyr/scalyr-agent-2/tree/ma... | PostgresMonitor | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PostgresMonitor:
"""# PostgreSQL Import performance and usage data from a PostgreSQL server. An [Agent Plugin](https://app.scalyr.com/help/scalyr-agent#plugins) is a component of the Scalyr Agent, enabling the collection of more data. The source code for each plugin is available on [Github](https... | stack_v2_sparse_classes_36k_train_027284 | 22,940 | permissive | [
{
"docstring": "Performs monitor-specific initialization.",
"name": "_initialize",
"signature": "def _initialize(self)"
},
{
"docstring": "Invoked once per sample interval to gather a statistic.",
"name": "gather_sample",
"signature": "def gather_sample(self)"
}
] | 2 | null | Implement the Python class `PostgresMonitor` described below.
Class description:
# PostgreSQL Import performance and usage data from a PostgreSQL server. An [Agent Plugin](https://app.scalyr.com/help/scalyr-agent#plugins) is a component of the Scalyr Agent, enabling the collection of more data. The source code for eac... | Implement the Python class `PostgresMonitor` described below.
Class description:
# PostgreSQL Import performance and usage data from a PostgreSQL server. An [Agent Plugin](https://app.scalyr.com/help/scalyr-agent#plugins) is a component of the Scalyr Agent, enabling the collection of more data. The source code for eac... | 5099a498edc47ab841965b483c2c32af49eb7dae | <|skeleton|>
class PostgresMonitor:
"""# PostgreSQL Import performance and usage data from a PostgreSQL server. An [Agent Plugin](https://app.scalyr.com/help/scalyr-agent#plugins) is a component of the Scalyr Agent, enabling the collection of more data. The source code for each plugin is available on [Github](https... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PostgresMonitor:
"""# PostgreSQL Import performance and usage data from a PostgreSQL server. An [Agent Plugin](https://app.scalyr.com/help/scalyr-agent#plugins) is a component of the Scalyr Agent, enabling the collection of more data. The source code for each plugin is available on [Github](https://github.com... | the_stack_v2_python_sparse | scalyr_agent/builtin_monitors/postgres_monitor.py | scalyr/scalyr-agent-2 | train | 75 |
db9fe8b81beb54a31cd720913b4e27eb1c01e8d4 | [
"node_type = request.query_params.get('type')\nif node_type == 'parent':\n needed = ['parent']\nelif node_type == 'children':\n needed = ['children']\nelse:\n needed = ['children', 'parent']\nparent_dict = {}\nchildren_list = []\nif 'parent' in needed:\n parent = models.Node.objects.filter(agent=request... | <|body_start_0|>
node_type = request.query_params.get('type')
if node_type == 'parent':
needed = ['parent']
elif node_type == 'children':
needed = ['children']
else:
needed = ['children', 'parent']
parent_dict = {}
children_list = []
... | 系统中心 | SystemNodeList | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SystemNodeList:
"""系统中心"""
def get(self, request):
"""获取当前中心的上下级数据 :param request: :param type: 中心类型 :return:"""
<|body_0|>
def post(self, request):
"""添加 :param request: :return:"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
node_type = reque... | stack_v2_sparse_classes_36k_train_027285 | 29,724 | no_license | [
{
"docstring": "获取当前中心的上下级数据 :param request: :param type: 中心类型 :return:",
"name": "get",
"signature": "def get(self, request)"
},
{
"docstring": "添加 :param request: :return:",
"name": "post",
"signature": "def post(self, request)"
}
] | 2 | null | Implement the Python class `SystemNodeList` described below.
Class description:
系统中心
Method signatures and docstrings:
- def get(self, request): 获取当前中心的上下级数据 :param request: :param type: 中心类型 :return:
- def post(self, request): 添加 :param request: :return: | Implement the Python class `SystemNodeList` described below.
Class description:
系统中心
Method signatures and docstrings:
- def get(self, request): 获取当前中心的上下级数据 :param request: :param type: 中心类型 :return:
- def post(self, request): 添加 :param request: :return:
<|skeleton|>
class SystemNodeList:
"""系统中心"""
def ge... | d6e025d7e9d9e3aecfd399c77f376130edd8a2df | <|skeleton|>
class SystemNodeList:
"""系统中心"""
def get(self, request):
"""获取当前中心的上下级数据 :param request: :param type: 中心类型 :return:"""
<|body_0|>
def post(self, request):
"""添加 :param request: :return:"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SystemNodeList:
"""系统中心"""
def get(self, request):
"""获取当前中心的上下级数据 :param request: :param type: 中心类型 :return:"""
node_type = request.query_params.get('type')
if node_type == 'parent':
needed = ['parent']
elif node_type == 'children':
needed = ['chil... | the_stack_v2_python_sparse | soc_system/views/node_views.py | sundw2015/841 | train | 4 |
4e005d18fac4e5616b1779e7848b850439ad9694 | [
"super().__init__()\nGatedGraphConv.global_count += 1\nself.name = name if name else 'GatedGraphConv_{}'.format(GatedGraphConv.global_count)\nself.output_channels = output_channels\nself.rnn = lbann.modules.GRU(output_channels)\nself.num_layers = num_layers\nself.data_layout = data_layout\nself.weights = []\nfor i ... | <|body_start_0|>
super().__init__()
GatedGraphConv.global_count += 1
self.name = name if name else 'GatedGraphConv_{}'.format(GatedGraphConv.global_count)
self.output_channels = output_channels
self.rnn = lbann.modules.GRU(output_channels)
self.num_layers = num_layers
... | Gated Graph Convolution layer. For kernel details, see: https://arxiv.org/abs/1511.05493 Implementation in the spirit of: https://github.com/rusty1s/pytorch_geometric/blob/ master/torch_geometric/nn/conv/gated_graph_conv.py | GatedGraphConv | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GatedGraphConv:
"""Gated Graph Convolution layer. For kernel details, see: https://arxiv.org/abs/1511.05493 Implementation in the spirit of: https://github.com/rusty1s/pytorch_geometric/blob/ master/torch_geometric/nn/conv/gated_graph_conv.py"""
def __init__(self, output_channels, num_layers... | stack_v2_sparse_classes_36k_train_027286 | 3,942 | permissive | [
{
"docstring": "Initialize GatedGraph layer Args: output_channels (int): The output size of the node features num_layers (int): Number of passes through the GRU (default: 1) name (str): Name of the layers and prefix to use for the layers. data_layout (str): Data layout (default: data parallel)",
"name": "__... | 2 | stack_v2_sparse_classes_30k_train_015727 | Implement the Python class `GatedGraphConv` described below.
Class description:
Gated Graph Convolution layer. For kernel details, see: https://arxiv.org/abs/1511.05493 Implementation in the spirit of: https://github.com/rusty1s/pytorch_geometric/blob/ master/torch_geometric/nn/conv/gated_graph_conv.py
Method signatu... | Implement the Python class `GatedGraphConv` described below.
Class description:
Gated Graph Convolution layer. For kernel details, see: https://arxiv.org/abs/1511.05493 Implementation in the spirit of: https://github.com/rusty1s/pytorch_geometric/blob/ master/torch_geometric/nn/conv/gated_graph_conv.py
Method signatu... | 57116ecc030c0d17bc941f81131c1a335bc2c4ad | <|skeleton|>
class GatedGraphConv:
"""Gated Graph Convolution layer. For kernel details, see: https://arxiv.org/abs/1511.05493 Implementation in the spirit of: https://github.com/rusty1s/pytorch_geometric/blob/ master/torch_geometric/nn/conv/gated_graph_conv.py"""
def __init__(self, output_channels, num_layers... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class GatedGraphConv:
"""Gated Graph Convolution layer. For kernel details, see: https://arxiv.org/abs/1511.05493 Implementation in the spirit of: https://github.com/rusty1s/pytorch_geometric/blob/ master/torch_geometric/nn/conv/gated_graph_conv.py"""
def __init__(self, output_channels, num_layers=1, name=None... | the_stack_v2_python_sparse | python/lbann/modules/graph/sparse/GatedGraphConv.py | oyamay/lbann | train | 0 |
565556e3aa2d5aa33593ebf13e18d8a2043670b4 | [
"data = tfds.load('ted_hrlr_translate/pt_to_en', as_supervised=True)\nself.data_train = data['train']\nself.data_valid = data['validation']\ntokenizer_pt, tokenizer_en = self.tokenize_dataset(self.data_train)\nself.tokenizer_pt = tokenizer_pt\nself.tokenizer_en = tokenizer_en",
"encoder = tfds.deprecated.text.Sub... | <|body_start_0|>
data = tfds.load('ted_hrlr_translate/pt_to_en', as_supervised=True)
self.data_train = data['train']
self.data_valid = data['validation']
tokenizer_pt, tokenizer_en = self.tokenize_dataset(self.data_train)
self.tokenizer_pt = tokenizer_pt
self.tokenizer_en... | Class to load an prepare a dataset for machine translation | Dataset | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Dataset:
"""Class to load an prepare a dataset for machine translation"""
def __init__(self):
"""Init"""
<|body_0|>
def tokenize_dataset(self, data):
"""Creates sub-word tokenizers for a dataset data: tf.data.Dataset whose examples are formatted as a table (pt, e... | stack_v2_sparse_classes_36k_train_027287 | 1,427 | no_license | [
{
"docstring": "Init",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Creates sub-word tokenizers for a dataset data: tf.data.Dataset whose examples are formatted as a table (pt, en) pt: tf.Tensor containing the Portuguese sentence en: tf.Tensor containing the English s... | 2 | null | Implement the Python class `Dataset` described below.
Class description:
Class to load an prepare a dataset for machine translation
Method signatures and docstrings:
- def __init__(self): Init
- def tokenize_dataset(self, data): Creates sub-word tokenizers for a dataset data: tf.data.Dataset whose examples are format... | Implement the Python class `Dataset` described below.
Class description:
Class to load an prepare a dataset for machine translation
Method signatures and docstrings:
- def __init__(self): Init
- def tokenize_dataset(self, data): Creates sub-word tokenizers for a dataset data: tf.data.Dataset whose examples are format... | 2757c8526290197d45a4de33cda71e686ddcbf1c | <|skeleton|>
class Dataset:
"""Class to load an prepare a dataset for machine translation"""
def __init__(self):
"""Init"""
<|body_0|>
def tokenize_dataset(self, data):
"""Creates sub-word tokenizers for a dataset data: tf.data.Dataset whose examples are formatted as a table (pt, e... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Dataset:
"""Class to load an prepare a dataset for machine translation"""
def __init__(self):
"""Init"""
data = tfds.load('ted_hrlr_translate/pt_to_en', as_supervised=True)
self.data_train = data['train']
self.data_valid = data['validation']
tokenizer_pt, tokenizer... | the_stack_v2_python_sparse | supervised_learning/0x12-transformer_apps/0-dataset.py | 95ktsmith/holbertonschool-machine_learning | train | 0 |
b6880a38e4b3c47987ace83bf5be200767f17ec0 | [
"self.name = name\nself.type = type\nself.label = label\nself.desc = desc\nself.default = default",
"para = OrderedDict()\npara[self.TYPE] = self.type\nif self.type:\n para[self.TYPE] = self.type\nif self.desc:\n para[self.DESCRIPTION] = self.desc\nif self.default:\n para[self.DEFAULT] = self.default\nif... | <|body_start_0|>
self.name = name
self.type = type
self.label = label
self.desc = desc
self.default = default
<|end_body_0|>
<|body_start_1|>
para = OrderedDict()
para[self.TYPE] = self.type
if self.type:
para[self.TYPE] = self.type
if... | HOT Parameter Attr | Parameter | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Parameter:
"""HOT Parameter Attr"""
def __init__(self, name, type, label=None, desc=None, default=None):
"""Init a HOT parameter"""
<|body_0|>
def get_output_dict(self):
"""Output a parameter as a nested dict"""
<|body_1|>
<|end_skeleton|>
<|body_start_... | stack_v2_sparse_classes_36k_train_027288 | 6,215 | no_license | [
{
"docstring": "Init a HOT parameter",
"name": "__init__",
"signature": "def __init__(self, name, type, label=None, desc=None, default=None)"
},
{
"docstring": "Output a parameter as a nested dict",
"name": "get_output_dict",
"signature": "def get_output_dict(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_003169 | Implement the Python class `Parameter` described below.
Class description:
HOT Parameter Attr
Method signatures and docstrings:
- def __init__(self, name, type, label=None, desc=None, default=None): Init a HOT parameter
- def get_output_dict(self): Output a parameter as a nested dict | Implement the Python class `Parameter` described below.
Class description:
HOT Parameter Attr
Method signatures and docstrings:
- def __init__(self, name, type, label=None, desc=None, default=None): Init a HOT parameter
- def get_output_dict(self): Output a parameter as a nested dict
<|skeleton|>
class Parameter:
... | 103d9ffb67e98c6e912a5861b73ad0f06d9df80c | <|skeleton|>
class Parameter:
"""HOT Parameter Attr"""
def __init__(self, name, type, label=None, desc=None, default=None):
"""Init a HOT parameter"""
<|body_0|>
def get_output_dict(self):
"""Output a parameter as a nested dict"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Parameter:
"""HOT Parameter Attr"""
def __init__(self, name, type, label=None, desc=None, default=None):
"""Init a HOT parameter"""
self.name = name
self.type = type
self.label = label
self.desc = desc
self.default = default
def get_output_dict(self):
... | the_stack_v2_python_sparse | sfc-ostack/sfcostack/hot.py | stevelorenz/sfc-ostack | train | 4 |
b04ac777fedc2201e0f2e3d0aba2a9e62f45b686 | [
"self.database = database\nself.notifications = set()\nself.last_update = 0\nself.update_notification_set()\nself.database.create_table('user_notification_id', ['`chat_id` INT UNSIGNED PRIMARY KEY NOT NULL', '`notification_id` INT UNSIGNED NOT NULL'])",
"now = int(time.time())\nparams = db.get_request_struct()\np... | <|body_start_0|>
self.database = database
self.notifications = set()
self.last_update = 0
self.update_notification_set()
self.database.create_table('user_notification_id', ['`chat_id` INT UNSIGNED PRIMARY KEY NOT NULL', '`notification_id` INT UNSIGNED NOT NULL'])
<|end_body_0|>
... | Class for online monitoring users notifications | NotificationDaemon | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class NotificationDaemon:
"""Class for online monitoring users notifications"""
def __init__(self, database):
"""Initialize class :param database.Database database: database"""
<|body_0|>
def update_notification_set(self):
"""Loads notifications from the database and p... | stack_v2_sparse_classes_36k_train_027289 | 2,859 | no_license | [
{
"docstring": "Initialize class :param database.Database database: database",
"name": "__init__",
"signature": "def __init__(self, database)"
},
{
"docstring": "Loads notifications from the database and puts them in the class structure",
"name": "update_notification_set",
"signature": "... | 3 | stack_v2_sparse_classes_30k_train_020489 | Implement the Python class `NotificationDaemon` described below.
Class description:
Class for online monitoring users notifications
Method signatures and docstrings:
- def __init__(self, database): Initialize class :param database.Database database: database
- def update_notification_set(self): Loads notifications fr... | Implement the Python class `NotificationDaemon` described below.
Class description:
Class for online monitoring users notifications
Method signatures and docstrings:
- def __init__(self, database): Initialize class :param database.Database database: database
- def update_notification_set(self): Loads notifications fr... | 460ab221b6afff976dea4527ca357a756c817d95 | <|skeleton|>
class NotificationDaemon:
"""Class for online monitoring users notifications"""
def __init__(self, database):
"""Initialize class :param database.Database database: database"""
<|body_0|>
def update_notification_set(self):
"""Loads notifications from the database and p... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class NotificationDaemon:
"""Class for online monitoring users notifications"""
def __init__(self, database):
"""Initialize class :param database.Database database: database"""
self.database = database
self.notifications = set()
self.last_update = 0
self.update_notificat... | the_stack_v2_python_sparse | notification_daemon.py | rust379/bot_tgm | train | 0 |
defce6771f3c747bb26af5b3e0de29b0f1874a5e | [
"cross = tensor([[[0, 1, 0], [1, 1, 1], [0, 1, 0]]])\nkernel = cross * 0.2\nreturn kernel[None]",
"if pred.dim() != 4:\n raise ValueError(f'Only 2D images supported. Got {pred.dim()}.')\nif not (target.max() < pred.size(1) and target.min() >= 0 and (target.dtype == torch.long)):\n raise ValueError(f'Expect ... | <|body_start_0|>
cross = tensor([[[0, 1, 0], [1, 1, 1], [0, 1, 0]]])
kernel = cross * 0.2
return kernel[None]
<|end_body_0|>
<|body_start_1|>
if pred.dim() != 4:
raise ValueError(f'Only 2D images supported. Got {pred.dim()}.')
if not (target.max() < pred.size(1) and ... | Binary Hausdorff loss based on morphological erosion. Hausdorff Distance loss measures the maximum distance of a predicted segmentation boundary to the nearest ground-truth edge pixel. For two segmentation point sets X and Y , the one-sided HD from X to Y is defined as: .. math:: hd(X,Y) = \\max_{x \\in X} \\min_{y \\i... | HausdorffERLoss | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class HausdorffERLoss:
"""Binary Hausdorff loss based on morphological erosion. Hausdorff Distance loss measures the maximum distance of a predicted segmentation boundary to the nearest ground-truth edge pixel. For two segmentation point sets X and Y , the one-sided HD from X to Y is defined as: .. mat... | stack_v2_sparse_classes_36k_train_027290 | 9,826 | permissive | [
{
"docstring": "Get kernel for image morphology convolution.",
"name": "get_kernel",
"signature": "def get_kernel(self) -> Tensor"
},
{
"docstring": "Compute Hausdorff loss. Args: pred: predicted tensor with a shape of :math:`(B, C, H, W)`. Each channel is as binary as: 1 -> fg, 0 -> bg. target:... | 2 | stack_v2_sparse_classes_30k_train_020512 | Implement the Python class `HausdorffERLoss` described below.
Class description:
Binary Hausdorff loss based on morphological erosion. Hausdorff Distance loss measures the maximum distance of a predicted segmentation boundary to the nearest ground-truth edge pixel. For two segmentation point sets X and Y , the one-sid... | Implement the Python class `HausdorffERLoss` described below.
Class description:
Binary Hausdorff loss based on morphological erosion. Hausdorff Distance loss measures the maximum distance of a predicted segmentation boundary to the nearest ground-truth edge pixel. For two segmentation point sets X and Y , the one-sid... | 1e0f8baa7318c05b17ea6dbb48605691bca8972f | <|skeleton|>
class HausdorffERLoss:
"""Binary Hausdorff loss based on morphological erosion. Hausdorff Distance loss measures the maximum distance of a predicted segmentation boundary to the nearest ground-truth edge pixel. For two segmentation point sets X and Y , the one-sided HD from X to Y is defined as: .. mat... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class HausdorffERLoss:
"""Binary Hausdorff loss based on morphological erosion. Hausdorff Distance loss measures the maximum distance of a predicted segmentation boundary to the nearest ground-truth edge pixel. For two segmentation point sets X and Y , the one-sided HD from X to Y is defined as: .. math:: hd(X,Y) =... | the_stack_v2_python_sparse | kornia/losses/hausdorff.py | kornia/kornia | train | 7,351 |
16fd747ee5f16cf98994a4d8e07665d345c83694 | [
"if not matrix:\n return 0\nm, n = (len(matrix), len(matrix[0]))\ncache = [[-1 for _ in range(n)] for _ in range(m)]\nlong = 0\nfor i in range(m):\n for j in range(n):\n long = max(long, self.longest(matrix, cache, m, n, i, j))\nreturn long",
"dirs = ((-1, 0), (1, 0), (0, -1), (0, 1))\nif cache[i][j]... | <|body_start_0|>
if not matrix:
return 0
m, n = (len(matrix), len(matrix[0]))
cache = [[-1 for _ in range(n)] for _ in range(m)]
long = 0
for i in range(m):
for j in range(n):
long = max(long, self.longest(matrix, cache, m, n, i, j))
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def longestIncreasingPath(self, matrix):
""":type matrix: List[List[int]] :rtype: int"""
<|body_0|>
def longest(self, matrix, cache, m, n, i, j):
"""Strictly increasing, thus no need to have a visited matrix"""
<|body_1|>
<|end_skeleton|>
<|body_s... | stack_v2_sparse_classes_36k_train_027291 | 1,691 | no_license | [
{
"docstring": ":type matrix: List[List[int]] :rtype: int",
"name": "longestIncreasingPath",
"signature": "def longestIncreasingPath(self, matrix)"
},
{
"docstring": "Strictly increasing, thus no need to have a visited matrix",
"name": "longest",
"signature": "def longest(self, matrix, c... | 2 | stack_v2_sparse_classes_30k_train_002059 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def longestIncreasingPath(self, matrix): :type matrix: List[List[int]] :rtype: int
- def longest(self, matrix, cache, m, n, i, j): Strictly increasing, thus no need to have a vis... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def longestIncreasingPath(self, matrix): :type matrix: List[List[int]] :rtype: int
- def longest(self, matrix, cache, m, n, i, j): Strictly increasing, thus no need to have a vis... | 810575368ecffa97677bdb51744d1f716140bbb1 | <|skeleton|>
class Solution:
def longestIncreasingPath(self, matrix):
""":type matrix: List[List[int]] :rtype: int"""
<|body_0|>
def longest(self, matrix, cache, m, n, i, j):
"""Strictly increasing, thus no need to have a visited matrix"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def longestIncreasingPath(self, matrix):
""":type matrix: List[List[int]] :rtype: int"""
if not matrix:
return 0
m, n = (len(matrix), len(matrix[0]))
cache = [[-1 for _ in range(n)] for _ in range(m)]
long = 0
for i in range(m):
... | the_stack_v2_python_sparse | L/LongestIncreasingPathinaMatrix.py | bssrdf/pyleet | train | 2 | |
14a16f0e3cf291476ee60a8a73f61bb4100d20c1 | [
"self.Wh = np.random.normal(size=(i + h, h))\nself.Wy = np.random.normal(size=(h, o))\nself.bh = np.zeros((1, h))\nself.by = np.zeros((1, o))",
"h_x = np.concatenate((h_prev, x_t), axis=1)\nh_next = np.tanh(np.matmul(h_x, self.Wh) + self.bh)\ny = np.matmul(h_next, self.Wy) + self.by\ny = np.exp(y) / np.sum(np.exp... | <|body_start_0|>
self.Wh = np.random.normal(size=(i + h, h))
self.Wy = np.random.normal(size=(h, o))
self.bh = np.zeros((1, h))
self.by = np.zeros((1, o))
<|end_body_0|>
<|body_start_1|>
h_x = np.concatenate((h_prev, x_t), axis=1)
h_next = np.tanh(np.matmul(h_x, self.Wh)... | RNN class | RNNCell | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RNNCell:
"""RNN class"""
def __init__(self, i, h, o):
"""class constructor: - i: dimensionality of the data - h: dimensionality of the hidden state - o: dimensionality of the outputs Creates the public instance attributes Wh, Wy, bh, by that represent the weights and biases of the ce... | stack_v2_sparse_classes_36k_train_027292 | 1,753 | no_license | [
{
"docstring": "class constructor: - i: dimensionality of the data - h: dimensionality of the hidden state - o: dimensionality of the outputs Creates the public instance attributes Wh, Wy, bh, by that represent the weights and biases of the cell - Wh and bh are for the concatenated hidden state and input data -... | 2 | null | Implement the Python class `RNNCell` described below.
Class description:
RNN class
Method signatures and docstrings:
- def __init__(self, i, h, o): class constructor: - i: dimensionality of the data - h: dimensionality of the hidden state - o: dimensionality of the outputs Creates the public instance attributes Wh, W... | Implement the Python class `RNNCell` described below.
Class description:
RNN class
Method signatures and docstrings:
- def __init__(self, i, h, o): class constructor: - i: dimensionality of the data - h: dimensionality of the hidden state - o: dimensionality of the outputs Creates the public instance attributes Wh, W... | cd386dd814ccb4869d33551b3ab8c3dd774fddf9 | <|skeleton|>
class RNNCell:
"""RNN class"""
def __init__(self, i, h, o):
"""class constructor: - i: dimensionality of the data - h: dimensionality of the hidden state - o: dimensionality of the outputs Creates the public instance attributes Wh, Wy, bh, by that represent the weights and biases of the ce... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RNNCell:
"""RNN class"""
def __init__(self, i, h, o):
"""class constructor: - i: dimensionality of the data - h: dimensionality of the hidden state - o: dimensionality of the outputs Creates the public instance attributes Wh, Wy, bh, by that represent the weights and biases of the cell - Wh and b... | the_stack_v2_python_sparse | supervised_learning/0x0D-RNNs/0-rnn_cell.py | Noeuclides/holbertonschool-machine_learning | train | 0 |
8710286d65041308dffa1e0cf81ef916ca3e8f70 | [
"parser.add_argument('BUCKET_ID', help='The id of the bucket to update.')\nparser.add_argument('--retention-days', type=int, help='A new retention period for the bucket.')\nparser.add_argument('--display-name', help='A new display name for the bucket.')\nparser.add_argument('--description', help='A new description ... | <|body_start_0|>
parser.add_argument('BUCKET_ID', help='The id of the bucket to update.')
parser.add_argument('--retention-days', type=int, help='A new retention period for the bucket.')
parser.add_argument('--display-name', help='A new display name for the bucket.')
parser.add_argument(... | Updates a bucket. Changes one or more proprties associated with a bucket. | Update | [
"MIT",
"LicenseRef-scancode-unknown-license-reference",
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Update:
"""Updates a bucket. Changes one or more proprties associated with a bucket."""
def Args(parser):
"""Register flags for this command."""
<|body_0|>
def Run(self, args):
"""This is what gets called when the user runs this command. Args: args: an argparse n... | stack_v2_sparse_classes_36k_train_027293 | 3,052 | permissive | [
{
"docstring": "Register flags for this command.",
"name": "Args",
"signature": "def Args(parser)"
},
{
"docstring": "This is what gets called when the user runs this command. Args: args: an argparse namespace. All the arguments that were provided to this command invocation. Returns: The updated... | 2 | stack_v2_sparse_classes_30k_train_010788 | Implement the Python class `Update` described below.
Class description:
Updates a bucket. Changes one or more proprties associated with a bucket.
Method signatures and docstrings:
- def Args(parser): Register flags for this command.
- def Run(self, args): This is what gets called when the user runs this command. Args... | Implement the Python class `Update` described below.
Class description:
Updates a bucket. Changes one or more proprties associated with a bucket.
Method signatures and docstrings:
- def Args(parser): Register flags for this command.
- def Run(self, args): This is what gets called when the user runs this command. Args... | 85bb264e273568b5a0408f733b403c56373e2508 | <|skeleton|>
class Update:
"""Updates a bucket. Changes one or more proprties associated with a bucket."""
def Args(parser):
"""Register flags for this command."""
<|body_0|>
def Run(self, args):
"""This is what gets called when the user runs this command. Args: args: an argparse n... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Update:
"""Updates a bucket. Changes one or more proprties associated with a bucket."""
def Args(parser):
"""Register flags for this command."""
parser.add_argument('BUCKET_ID', help='The id of the bucket to update.')
parser.add_argument('--retention-days', type=int, help='A new r... | the_stack_v2_python_sparse | google-cloud-sdk/lib/surface/logging/buckets/update.py | bopopescu/socialliteapp | train | 0 |
1594a09317edb4bc228dbb1cfb1cf4998336d3d8 | [
"try:\n respone = super().post(request, *args, **kwargs)\n complaint = request.data['complaint']\n comp = ComplaintModel.objects.get(id=complaint)\n comp.like_count += 1\n comp.save()\nexcept Exception as e:\n raise e\nelse:\n return respone",
"complaint_like = self.queryset.filter(complaint=... | <|body_start_0|>
try:
respone = super().post(request, *args, **kwargs)
complaint = request.data['complaint']
comp = ComplaintModel.objects.get(id=complaint)
comp.like_count += 1
comp.save()
except Exception as e:
raise e
els... | 吐槽点赞 | ComplaintLikeView | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ComplaintLikeView:
"""吐槽点赞"""
def post(self, request, *args, **kwargs):
"""点赞"""
<|body_0|>
def destroy(self, request, complaint, *args, **kwargs):
"""取消点赞"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
try:
respone = super().post(r... | stack_v2_sparse_classes_36k_train_027294 | 9,460 | no_license | [
{
"docstring": "点赞",
"name": "post",
"signature": "def post(self, request, *args, **kwargs)"
},
{
"docstring": "取消点赞",
"name": "destroy",
"signature": "def destroy(self, request, complaint, *args, **kwargs)"
}
] | 2 | stack_v2_sparse_classes_30k_train_005389 | Implement the Python class `ComplaintLikeView` described below.
Class description:
吐槽点赞
Method signatures and docstrings:
- def post(self, request, *args, **kwargs): 点赞
- def destroy(self, request, complaint, *args, **kwargs): 取消点赞 | Implement the Python class `ComplaintLikeView` described below.
Class description:
吐槽点赞
Method signatures and docstrings:
- def post(self, request, *args, **kwargs): 点赞
- def destroy(self, request, complaint, *args, **kwargs): 取消点赞
<|skeleton|>
class ComplaintLikeView:
"""吐槽点赞"""
def post(self, request, *ar... | c0df44858d0951e345de245505ae8f71f8b5e1b6 | <|skeleton|>
class ComplaintLikeView:
"""吐槽点赞"""
def post(self, request, *args, **kwargs):
"""点赞"""
<|body_0|>
def destroy(self, request, complaint, *args, **kwargs):
"""取消点赞"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ComplaintLikeView:
"""吐槽点赞"""
def post(self, request, *args, **kwargs):
"""点赞"""
try:
respone = super().post(request, *args, **kwargs)
complaint = request.data['complaint']
comp = ComplaintModel.objects.get(id=complaint)
comp.like_count += 1... | the_stack_v2_python_sparse | mlh/apps/complaints/views.py | AmirHuang/mlh | train | 0 |
b378de7e2afbe5990b28523d265c89072028d95a | [
"super(YearModel, self).__init__()\nself.config = config\nself.vocab = vocab\nself.dim = 1",
"if len(m1['y']) == 1 and len(m2['y']) == 1:\n return abs(int(list(m1['y'])[0]) - int(list(m2['y'])[0]))\nelse:\n return 0.0",
"if len(m1['y']) == 1 and len(m2['y']) == 0:\n return 1.0\nelif len(m1['y']) == 0 a... | <|body_start_0|>
super(YearModel, self).__init__()
self.config = config
self.vocab = vocab
self.dim = 1
<|end_body_0|>
<|body_start_1|>
if len(m1['y']) == 1 and len(m2['y']) == 1:
return abs(int(list(m1['y'])[0]) - int(list(m2['y'])[0]))
else:
ret... | YearModel | [
"Apache-2.0",
"LicenseRef-scancode-unknown-license-reference"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class YearModel:
def __init__(self, config, vocab):
"""Init."""
<|body_0|>
def year_span(self, m1, m2):
"""(max year - min year)"""
<|body_1|>
def one_year_missing(self, m1, m2):
"""Return 1.0 if one of the mentions doesn't have a year."""
<|bo... | stack_v2_sparse_classes_36k_train_027295 | 1,967 | permissive | [
{
"docstring": "Init.",
"name": "__init__",
"signature": "def __init__(self, config, vocab)"
},
{
"docstring": "(max year - min year)",
"name": "year_span",
"signature": "def year_span(self, m1, m2)"
},
{
"docstring": "Return 1.0 if one of the mentions doesn't have a year.",
... | 5 | null | Implement the Python class `YearModel` described below.
Class description:
Implement the YearModel class.
Method signatures and docstrings:
- def __init__(self, config, vocab): Init.
- def year_span(self, m1, m2): (max year - min year)
- def one_year_missing(self, m1, m2): Return 1.0 if one of the mentions doesn't ha... | Implement the Python class `YearModel` described below.
Class description:
Implement the YearModel class.
Method signatures and docstrings:
- def __init__(self, config, vocab): Init.
- def year_span(self, m1, m2): (max year - min year)
- def one_year_missing(self, m1, m2): Return 1.0 if one of the mentions doesn't ha... | 542659170897ad05f7612639cb918886859ae9d6 | <|skeleton|>
class YearModel:
def __init__(self, config, vocab):
"""Init."""
<|body_0|>
def year_span(self, m1, m2):
"""(max year - min year)"""
<|body_1|>
def one_year_missing(self, m1, m2):
"""Return 1.0 if one of the mentions doesn't have a year."""
<|bo... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class YearModel:
def __init__(self, config, vocab):
"""Init."""
super(YearModel, self).__init__()
self.config = config
self.vocab = vocab
self.dim = 1
def year_span(self, m1, m2):
"""(max year - min year)"""
if len(m1['y']) == 1 and len(m2['y']) == 1:
... | the_stack_v2_python_sparse | src/python/coref/models/pw/YearModel.py | nmonath/coref_tools | train | 0 | |
83d52b4822fbf72ed90a62edf07fc51341102b8c | [
"self.rows = rows\nself.cols = cols\nself.body = []\nself.body.append(self.initialize_snake())\nself.directions = collections.deque()",
"snake_row = self.rows // 2\nsnake_col = 1\nreturn (snake_row, snake_col)",
"for i, pos in enumerate(self.body):\n direct = self.directions[i]\n if direct == 'left':\n ... | <|body_start_0|>
self.rows = rows
self.cols = cols
self.body = []
self.body.append(self.initialize_snake())
self.directions = collections.deque()
<|end_body_0|>
<|body_start_1|>
snake_row = self.rows // 2
snake_col = 1
return (snake_row, snake_col)
<|end_... | Class that represents the snake in Snake Game. It is instatiated from within the SnakeGame class Attributes: self.rows: The number of rows in the grid of the game self.cols: The number of columns in the grid of the game self.body: A list of poisitions in the grid of the Snake's body self.directions: A double-ended queu... | Snake | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Snake:
"""Class that represents the snake in Snake Game. It is instatiated from within the SnakeGame class Attributes: self.rows: The number of rows in the grid of the game self.cols: The number of columns in the grid of the game self.body: A list of poisitions in the grid of the Snake's body sel... | stack_v2_sparse_classes_36k_train_027296 | 2,882 | no_license | [
{
"docstring": "Initializes Snake class",
"name": "__init__",
"signature": "def __init__(self, rows, cols)"
},
{
"docstring": "Initializes the first position for the snake. Returns: A tuple representing the position for the snake in the format of (row, column).",
"name": "initialize_snake",
... | 4 | stack_v2_sparse_classes_30k_train_008191 | Implement the Python class `Snake` described below.
Class description:
Class that represents the snake in Snake Game. It is instatiated from within the SnakeGame class Attributes: self.rows: The number of rows in the grid of the game self.cols: The number of columns in the grid of the game self.body: A list of poisiti... | Implement the Python class `Snake` described below.
Class description:
Class that represents the snake in Snake Game. It is instatiated from within the SnakeGame class Attributes: self.rows: The number of rows in the grid of the game self.cols: The number of columns in the grid of the game self.body: A list of poisiti... | f9e23dad0beec83dde6fec3b3224b07f27f4bca9 | <|skeleton|>
class Snake:
"""Class that represents the snake in Snake Game. It is instatiated from within the SnakeGame class Attributes: self.rows: The number of rows in the grid of the game self.cols: The number of columns in the grid of the game self.body: A list of poisitions in the grid of the Snake's body sel... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Snake:
"""Class that represents the snake in Snake Game. It is instatiated from within the SnakeGame class Attributes: self.rows: The number of rows in the grid of the game self.cols: The number of columns in the grid of the game self.body: A list of poisitions in the grid of the Snake's body self.directions:... | the_stack_v2_python_sparse | helpers/snake.py | Shadowissobad/AI-for-Snake-Game | train | 0 |
c0a86a17a14e0e7ce30c8f49cd241e537dc88345 | [
"self.winners, self.times = ([], times)\nvote_count, counts = (0, collections.defaultdict(int))\nfor person in persons:\n counts[person] += 1\n if counts[person] >= vote_count:\n vote_count = counts[person]\n winner = person\n self.winners.append(winner)",
"i, j = (0, len(self.times))\nwhil... | <|body_start_0|>
self.winners, self.times = ([], times)
vote_count, counts = (0, collections.defaultdict(int))
for person in persons:
counts[person] += 1
if counts[person] >= vote_count:
vote_count = counts[person]
winner = person
... | TopVotedCandidate | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TopVotedCandidate:
def __init__(self, persons, times):
""":type persons: List[int] :type times: List[int]"""
<|body_0|>
def q(self, t):
""":type t: int :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
self.winners, self.times = ([], times... | stack_v2_sparse_classes_36k_train_027297 | 823 | no_license | [
{
"docstring": ":type persons: List[int] :type times: List[int]",
"name": "__init__",
"signature": "def __init__(self, persons, times)"
},
{
"docstring": ":type t: int :rtype: int",
"name": "q",
"signature": "def q(self, t)"
}
] | 2 | stack_v2_sparse_classes_30k_train_002439 | Implement the Python class `TopVotedCandidate` described below.
Class description:
Implement the TopVotedCandidate class.
Method signatures and docstrings:
- def __init__(self, persons, times): :type persons: List[int] :type times: List[int]
- def q(self, t): :type t: int :rtype: int | Implement the Python class `TopVotedCandidate` described below.
Class description:
Implement the TopVotedCandidate class.
Method signatures and docstrings:
- def __init__(self, persons, times): :type persons: List[int] :type times: List[int]
- def q(self, t): :type t: int :rtype: int
<|skeleton|>
class TopVotedCandi... | fa1ed20d266b9c226f10fdb64528ffae0a595aa4 | <|skeleton|>
class TopVotedCandidate:
def __init__(self, persons, times):
""":type persons: List[int] :type times: List[int]"""
<|body_0|>
def q(self, t):
""":type t: int :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TopVotedCandidate:
def __init__(self, persons, times):
""":type persons: List[int] :type times: List[int]"""
self.winners, self.times = ([], times)
vote_count, counts = (0, collections.defaultdict(int))
for person in persons:
counts[person] += 1
if count... | the_stack_v2_python_sparse | medium_911. Online Election.py | sdksfo/leetcode | train | 0 | |
3b41021e1434699f938f9064cfd501fddf627282 | [
"data = [graden, sleep]\nservo = FRICP(FRICP.Request.HARDWARE_SET_SERVO_POSITION, FRICP.Owner.PROCESSING, FRICP.Owner.HARDWARE, FRICP.Response.REQUEST, data)\nresponse = servo.send()\nreturn response.response",
"distance = FRICP(FRICP.Request.HARDWARE_GET_SERVO_POSITION, FRICP.Owner.PROCESSING, FRICP.Owner.HARDWA... | <|body_start_0|>
data = [graden, sleep]
servo = FRICP(FRICP.Request.HARDWARE_SET_SERVO_POSITION, FRICP.Owner.PROCESSING, FRICP.Owner.HARDWARE, FRICP.Response.REQUEST, data)
response = servo.send()
return response.response
<|end_body_0|>
<|body_start_1|>
distance = FRICP(FRICP.Re... | Servo | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Servo:
def set_position(graden: int, sleep: float=0.4) -> FRICP.Response:
"""verstuurd `HARDWARE_SET_SERVO_POSITION` naar de Hardware FRICP server. Draai de servo naar `graden`. kan een FRICP.ValidationError exception throwen Args: graden(int): naar welke graad moet hij draaien Returns: ... | stack_v2_sparse_classes_36k_train_027298 | 2,869 | no_license | [
{
"docstring": "verstuurd `HARDWARE_SET_SERVO_POSITION` naar de Hardware FRICP server. Draai de servo naar `graden`. kan een FRICP.ValidationError exception throwen Args: graden(int): naar welke graad moet hij draaien Returns: FRICP.Response: response van de server",
"name": "set_position",
"signature":... | 2 | stack_v2_sparse_classes_30k_train_013887 | Implement the Python class `Servo` described below.
Class description:
Implement the Servo class.
Method signatures and docstrings:
- def set_position(graden: int, sleep: float=0.4) -> FRICP.Response: verstuurd `HARDWARE_SET_SERVO_POSITION` naar de Hardware FRICP server. Draai de servo naar `graden`. kan een FRICP.Va... | Implement the Python class `Servo` described below.
Class description:
Implement the Servo class.
Method signatures and docstrings:
- def set_position(graden: int, sleep: float=0.4) -> FRICP.Response: verstuurd `HARDWARE_SET_SERVO_POSITION` naar de Hardware FRICP server. Draai de servo naar `graden`. kan een FRICP.Va... | 84e6f9c44876e71644ffa0786a6009b0f7f45843 | <|skeleton|>
class Servo:
def set_position(graden: int, sleep: float=0.4) -> FRICP.Response:
"""verstuurd `HARDWARE_SET_SERVO_POSITION` naar de Hardware FRICP server. Draai de servo naar `graden`. kan een FRICP.ValidationError exception throwen Args: graden(int): naar welke graad moet hij draaien Returns: ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Servo:
def set_position(graden: int, sleep: float=0.4) -> FRICP.Response:
"""verstuurd `HARDWARE_SET_SERVO_POSITION` naar de Hardware FRICP server. Draai de servo naar `graden`. kan een FRICP.ValidationError exception throwen Args: graden(int): naar welke graad moet hij draaien Returns: FRICP.Response... | the_stack_v2_python_sparse | src/thread/hardware.py | thomas-w-nl/fotoroulette | train | 0 | |
1c167d7a407a586d34d00d8b82634e28c27759a0 | [
"self.line_search = LineSearch(c1, c2, beta, tol)\nself.tad = TorchAutoDiff()\nself.f_all = []",
"x_k = x0\nfor k in range(maxit):\n self.f_all.append(fun(x_k).detach().numpy())\n grad_k = self.tad.compute_gradient(fun, x_k)\n if use_btls:\n alpha_k = self.line_search.btls(fun, x_k, -grad_k, grad_... | <|body_start_0|>
self.line_search = LineSearch(c1, c2, beta, tol)
self.tad = TorchAutoDiff()
self.f_all = []
<|end_body_0|>
<|body_start_1|>
x_k = x0
for k in range(maxit):
self.f_all.append(fun(x_k).detach().numpy())
grad_k = self.tad.compute_gradient(fu... | SteepestDescent | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SteepestDescent:
def __init__(self, c1, c2, beta, tol=1e-05):
"""Implementation of the steepest descent algorithm Input: c1 : parameter used for line search wolfe condition 1 c2 : parameter used for line search wolfe condition 2 beta : used in back tracking line search tol : value to che... | stack_v2_sparse_classes_36k_train_027299 | 2,463 | no_license | [
{
"docstring": "Implementation of the steepest descent algorithm Input: c1 : parameter used for line search wolfe condition 1 c2 : parameter used for line search wolfe condition 2 beta : used in back tracking line search tol : value to check equality condition (refer to line search for details)",
"name": "_... | 3 | stack_v2_sparse_classes_30k_val_000906 | Implement the Python class `SteepestDescent` described below.
Class description:
Implement the SteepestDescent class.
Method signatures and docstrings:
- def __init__(self, c1, c2, beta, tol=1e-05): Implementation of the steepest descent algorithm Input: c1 : parameter used for line search wolfe condition 1 c2 : para... | Implement the Python class `SteepestDescent` described below.
Class description:
Implement the SteepestDescent class.
Method signatures and docstrings:
- def __init__(self, c1, c2, beta, tol=1e-05): Implementation of the steepest descent algorithm Input: c1 : parameter used for line search wolfe condition 1 c2 : para... | 160f6bcef64d17c622fb9cb017bd4faa65afd858 | <|skeleton|>
class SteepestDescent:
def __init__(self, c1, c2, beta, tol=1e-05):
"""Implementation of the steepest descent algorithm Input: c1 : parameter used for line search wolfe condition 1 c2 : parameter used for line search wolfe condition 2 beta : used in back tracking line search tol : value to che... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SteepestDescent:
def __init__(self, c1, c2, beta, tol=1e-05):
"""Implementation of the steepest descent algorithm Input: c1 : parameter used for line search wolfe condition 1 c2 : parameter used for line search wolfe condition 2 beta : used in back tracking line search tol : value to check equality co... | the_stack_v2_python_sparse | python/py_solvers/unconstrained/gradient_descent.py | avadesh02/Non-Linear-Optimization-Solvers-Package | train | 4 |
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