blob_id stringlengths 40 40 | bodies listlengths 2 6 | bodies_text stringlengths 196 7.73k | class_docstring stringlengths 0 700 | class_name stringlengths 1 86 | detected_licenses listlengths 0 45 | format_version stringclasses 1
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value | snapshot_total_rows int64 75.8k 75.8k | solution stringlengths 242 8.3k | source stringclasses 1
value | source_path stringlengths 4 177 | source_repo stringlengths 6 110 | split stringclasses 1
value | star_events_count int64 0 209k |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
d45debe8a56d3dc2a51a91d08b4fde7ff48817ea | [
"for paper_id in fragment.split():\n try:\n paper_id, dummy, categories = self._parse_entry(paper_id)\n except AssertionError:\n warnings.warn(f'Failed parsing new (new style): {paper_id}')\n continue\n for category in categories:\n try:\n yield (Identifier(paper_id),... | <|body_start_0|>
for paper_id in fragment.split():
try:
paper_id, dummy, categories = self._parse_entry(paper_id)
except AssertionError:
warnings.warn(f'Failed parsing new (new style): {paper_id}')
continue
for category in categ... | Parses new-style daily log lines. Starting after 2007-04-02 (:const:`NEW_STYLE_CUTOVER_AFTER`), the format changed to put all announcement-related events on a given day on the same line. The three original sections of the line are preserved, but within each section are entries for e-prints from all archives. | NewStyleLineParser | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class NewStyleLineParser:
"""Parses new-style daily log lines. Starting after 2007-04-02 (:const:`NEW_STYLE_CUTOVER_AFTER`), the format changed to put all announcement-related events on a given day on the same line. The three original sections of the line are preserved, but within each section are entr... | stack_v2_sparse_classes_75kplus_train_065900 | 23,697 | permissive | [
{
"docstring": "Parse entries for new e-prints. Parameters ---------- archive : str Literally just ``\"arxiv\"``; this is a dummy place-holder, since new-style lines contain entries for all archives for which announcements occurred on a particular day. fragment : str Section of the line containing new e-print e... | 4 | stack_v2_sparse_classes_30k_train_011749 | Implement the Python class `NewStyleLineParser` described below.
Class description:
Parses new-style daily log lines. Starting after 2007-04-02 (:const:`NEW_STYLE_CUTOVER_AFTER`), the format changed to put all announcement-related events on a given day on the same line. The three original sections of the line are pres... | Implement the Python class `NewStyleLineParser` described below.
Class description:
Parses new-style daily log lines. Starting after 2007-04-02 (:const:`NEW_STYLE_CUTOVER_AFTER`), the format changed to put all announcement-related events on a given day on the same line. The three original sections of the line are pres... | 407cb0b2cef83c7f653dabdf998e797b18475b13 | <|skeleton|>
class NewStyleLineParser:
"""Parses new-style daily log lines. Starting after 2007-04-02 (:const:`NEW_STYLE_CUTOVER_AFTER`), the format changed to put all announcement-related events on a given day on the same line. The three original sections of the line are preserved, but within each section are entr... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class NewStyleLineParser:
"""Parses new-style daily log lines. Starting after 2007-04-02 (:const:`NEW_STYLE_CUTOVER_AFTER`), the format changed to put all announcement-related events on a given day on the same line. The three original sections of the line are preserved, but within each section are entries for e-pri... | the_stack_v2_python_sparse | arxiv/canonical/classic/daily.py | arXiv/arxiv-canonical | train | 5 |
dbf36004be77fc1c80331e3a64a7ec8fde1ff267 | [
"partname = package.next_partname('/word/header%d.xml')\ncontent_type = CT.WML_HEADER\nelement = parse_xml(cls._default_header_xml())\nreturn cls(partname, content_type, element, package)",
"path = os.path.join(os.path.split(__file__)[0], '..', 'templates', 'default-header.xml')\nwith open(path, 'rb') as f:\n ... | <|body_start_0|>
partname = package.next_partname('/word/header%d.xml')
content_type = CT.WML_HEADER
element = parse_xml(cls._default_header_xml())
return cls(partname, content_type, element, package)
<|end_body_0|>
<|body_start_1|>
path = os.path.join(os.path.split(__file__)[0]... | Definition of a section header. | HeaderPart | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class HeaderPart:
"""Definition of a section header."""
def new(cls, package):
"""Return newly created header part."""
<|body_0|>
def _default_header_xml(cls):
"""Return bytes containing XML for a default header part."""
<|body_1|>
<|end_skeleton|>
<|body_sta... | stack_v2_sparse_classes_75kplus_train_065901 | 1,717 | permissive | [
{
"docstring": "Return newly created header part.",
"name": "new",
"signature": "def new(cls, package)"
},
{
"docstring": "Return bytes containing XML for a default header part.",
"name": "_default_header_xml",
"signature": "def _default_header_xml(cls)"
}
] | 2 | stack_v2_sparse_classes_30k_train_013606 | Implement the Python class `HeaderPart` described below.
Class description:
Definition of a section header.
Method signatures and docstrings:
- def new(cls, package): Return newly created header part.
- def _default_header_xml(cls): Return bytes containing XML for a default header part. | Implement the Python class `HeaderPart` described below.
Class description:
Definition of a section header.
Method signatures and docstrings:
- def new(cls, package): Return newly created header part.
- def _default_header_xml(cls): Return bytes containing XML for a default header part.
<|skeleton|>
class HeaderPart... | 2bfcf6b9779bf1abd41e1bc42c27007127ddbefb | <|skeleton|>
class HeaderPart:
"""Definition of a section header."""
def new(cls, package):
"""Return newly created header part."""
<|body_0|>
def _default_header_xml(cls):
"""Return bytes containing XML for a default header part."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class HeaderPart:
"""Definition of a section header."""
def new(cls, package):
"""Return newly created header part."""
partname = package.next_partname('/word/header%d.xml')
content_type = CT.WML_HEADER
element = parse_xml(cls._default_header_xml())
return cls(partname, ... | the_stack_v2_python_sparse | anuvaad-etl/anuvaad-extractor/file_translator/etl-file-translator/docx/parts/hdrftr.py | project-anuvaad/anuvaad | train | 41 |
53f8244b18ef273093a100985455ca8c84da1970 | [
"model = model or MEGNetModel.from_file(model_filename)\ngaussian_cutoff = kwargs.get('gaussian_cutoff', 6)\nradius_cutoff = kwargs.get('radius_cutoff', 5)\nnpass = kwargs.get('npass', 2)\nself.reconstruct = reconstruct\nweights = model.get_weights()\nself.embedding = weights[0]\nif reconstruct:\n cg = CrystalGr... | <|body_start_0|>
model = model or MEGNetModel.from_file(model_filename)
gaussian_cutoff = kwargs.get('gaussian_cutoff', 6)
radius_cutoff = kwargs.get('radius_cutoff', 5)
npass = kwargs.get('npass', 2)
self.reconstruct = reconstruct
weights = model.get_weights()
se... | MEGNetModel wrapper. | MEGNet | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MEGNet:
"""MEGNetModel wrapper."""
def __init__(self, model=None, reconstruct=False, **kwargs):
"""Args: model: MEGNet energy model reconstruct: Whether to reconstruct the model (used in disordered model) **kwargs:"""
<|body_0|>
def predict_energy(self, structure: Struct... | stack_v2_sparse_classes_75kplus_train_065902 | 3,050 | permissive | [
{
"docstring": "Args: model: MEGNet energy model reconstruct: Whether to reconstruct the model (used in disordered model) **kwargs:",
"name": "__init__",
"signature": "def __init__(self, model=None, reconstruct=False, **kwargs)"
},
{
"docstring": "Predict energy from structure Args: structure: (... | 2 | stack_v2_sparse_classes_30k_train_011484 | Implement the Python class `MEGNet` described below.
Class description:
MEGNetModel wrapper.
Method signatures and docstrings:
- def __init__(self, model=None, reconstruct=False, **kwargs): Args: model: MEGNet energy model reconstruct: Whether to reconstruct the model (used in disordered model) **kwargs:
- def predic... | Implement the Python class `MEGNet` described below.
Class description:
MEGNetModel wrapper.
Method signatures and docstrings:
- def __init__(self, model=None, reconstruct=False, **kwargs): Args: model: MEGNet energy model reconstruct: Whether to reconstruct the model (used in disordered model) **kwargs:
- def predic... | 6ae3c7029b939e1183684358a3ae2fef41053be5 | <|skeleton|>
class MEGNet:
"""MEGNetModel wrapper."""
def __init__(self, model=None, reconstruct=False, **kwargs):
"""Args: model: MEGNet energy model reconstruct: Whether to reconstruct the model (used in disordered model) **kwargs:"""
<|body_0|>
def predict_energy(self, structure: Struct... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class MEGNet:
"""MEGNetModel wrapper."""
def __init__(self, model=None, reconstruct=False, **kwargs):
"""Args: model: MEGNet energy model reconstruct: Whether to reconstruct the model (used in disordered model) **kwargs:"""
model = model or MEGNetModel.from_file(model_filename)
gaussian... | the_stack_v2_python_sparse | maml/apps/bowsr/model/megnet.py | materialsvirtuallab/maml | train | 266 |
b314ba76d2652c98bad9c2506019c5095f6e603f | [
"try:\n cls.abrir_conexion()\n sql = 'SELECT idValor, fecha, valor FROM valoresTipArt WHERE idTipoArticulo = {};'.format(id)\n cls.cursor.execute(sql)\n valores = cls.cursor.fetchall()\n max_date = valores[0]\n for v in valores:\n if v[1] > max_date[1... | <|body_start_0|>
try:
cls.abrir_conexion()
sql = 'SELECT idValor, fecha, valor FROM valoresTipArt WHERE idTipoArticulo = {};'.format(id)
cls.cursor.execute(sql)
valores = cls.cursor.fetchall()
max_date = valores[... | DatosValor | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DatosValor:
def get_from_TAid(cls, id, noClose=False):
"""Obtiene el valor de un tipo articulo de la BD"""
<|body_0|>
def add(cls, idArt, fecha, valor):
"""Da de alta un nuevo valor de un articulo en el sistema."""
<|body_1|>
<|end_skeleton|>
<|body_start_0... | stack_v2_sparse_classes_75kplus_train_065903 | 1,869 | no_license | [
{
"docstring": "Obtiene el valor de un tipo articulo de la BD",
"name": "get_from_TAid",
"signature": "def get_from_TAid(cls, id, noClose=False)"
},
{
"docstring": "Da de alta un nuevo valor de un articulo en el sistema.",
"name": "add",
"signature": "def add(cls, idArt, fecha, valor)"
... | 2 | stack_v2_sparse_classes_30k_train_013005 | Implement the Python class `DatosValor` described below.
Class description:
Implement the DatosValor class.
Method signatures and docstrings:
- def get_from_TAid(cls, id, noClose=False): Obtiene el valor de un tipo articulo de la BD
- def add(cls, idArt, fecha, valor): Da de alta un nuevo valor de un articulo en el s... | Implement the Python class `DatosValor` described below.
Class description:
Implement the DatosValor class.
Method signatures and docstrings:
- def get_from_TAid(cls, id, noClose=False): Obtiene el valor de un tipo articulo de la BD
- def add(cls, idArt, fecha, valor): Da de alta un nuevo valor de un articulo en el s... | 57ca674dba4dabd2526c450ba7210933240f19c5 | <|skeleton|>
class DatosValor:
def get_from_TAid(cls, id, noClose=False):
"""Obtiene el valor de un tipo articulo de la BD"""
<|body_0|>
def add(cls, idArt, fecha, valor):
"""Da de alta un nuevo valor de un articulo en el sistema."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class DatosValor:
def get_from_TAid(cls, id, noClose=False):
"""Obtiene el valor de un tipo articulo de la BD"""
try:
cls.abrir_conexion()
sql = 'SELECT idValor, fecha, valor FROM valoresTipArt WHERE idTipoArticulo = {};'.format(id)
... | the_stack_v2_python_sparse | data/data_valor.py | JoaquinCardonaRuiz/proyecto-final | train | 0 | |
11b66ac9b813ef1f4d41b4893a16ef742bce79c2 | [
"if not is_administrator(self.context['request'].user):\n raise serializers.ValidationError(constants.PERMISSION_ADMINISTRATOR_REQUIRED)\nreturn data",
"if is_attendants(user):\n raise serializers.ValidationError('User already has attendants permissions')\nreturn user"
] | <|body_start_0|>
if not is_administrator(self.context['request'].user):
raise serializers.ValidationError(constants.PERMISSION_ADMINISTRATOR_REQUIRED)
return data
<|end_body_0|>
<|body_start_1|>
if is_attendants(user):
raise serializers.ValidationError('User already has ... | AttendantSerializerCreate | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AttendantSerializerCreate:
def validate(self, data):
"""Administrator permissions needed"""
<|body_0|>
def validate_user(self, user):
"""Ensure user is not already attendant or higher"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
if not is_adminis... | stack_v2_sparse_classes_75kplus_train_065904 | 1,156 | no_license | [
{
"docstring": "Administrator permissions needed",
"name": "validate",
"signature": "def validate(self, data)"
},
{
"docstring": "Ensure user is not already attendant or higher",
"name": "validate_user",
"signature": "def validate_user(self, user)"
}
] | 2 | null | Implement the Python class `AttendantSerializerCreate` described below.
Class description:
Implement the AttendantSerializerCreate class.
Method signatures and docstrings:
- def validate(self, data): Administrator permissions needed
- def validate_user(self, user): Ensure user is not already attendant or higher | Implement the Python class `AttendantSerializerCreate` described below.
Class description:
Implement the AttendantSerializerCreate class.
Method signatures and docstrings:
- def validate(self, data): Administrator permissions needed
- def validate_user(self, user): Ensure user is not already attendant or higher
<|sk... | d17fcc79b175831bae9c2e0a3a536a384b1a562b | <|skeleton|>
class AttendantSerializerCreate:
def validate(self, data):
"""Administrator permissions needed"""
<|body_0|>
def validate_user(self, user):
"""Ensure user is not already attendant or higher"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class AttendantSerializerCreate:
def validate(self, data):
"""Administrator permissions needed"""
if not is_administrator(self.context['request'].user):
raise serializers.ValidationError(constants.PERMISSION_ADMINISTRATOR_REQUIRED)
return data
def validate_user(self, user):
... | the_stack_v2_python_sparse | app/users/serializers/attendants.py | Empire-Synergy-Solutions/Altaviz_Backend | train | 0 | |
a517c275124d88153efbcb7f91030161519aae0e | [
"x_headers = 'x-ms-date:' + date\nstring_to_hash = method + '\\n' + str(content_length) + '\\n' + content_type + '\\n' + x_headers + '\\n' + resource\nbytes_to_hash = bytes(string_to_hash, encoding='utf-8')\ndecoded_key = base64.b64decode(shared_key)\nencoded_hash = base64.b64encode(hmac.new(decoded_key, bytes_to_h... | <|body_start_0|>
x_headers = 'x-ms-date:' + date
string_to_hash = method + '\n' + str(content_length) + '\n' + content_type + '\n' + x_headers + '\n' + resource
bytes_to_hash = bytes(string_to_hash, encoding='utf-8')
decoded_key = base64.b64decode(shared_key)
encoded_hash = base6... | AzureSentinel is Used to post data to log analytics. | AzureSentinel | [
"MIT",
"LicenseRef-scancode-generic-cla"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AzureSentinel:
"""AzureSentinel is Used to post data to log analytics."""
def build_signature(self, date, content_length, method, content_type, resource):
"""To build the signature."""
<|body_0|>
def post_data(self, customer_id, body, log_type):
"""Build and send... | stack_v2_sparse_classes_75kplus_train_065905 | 7,907 | permissive | [
{
"docstring": "To build the signature.",
"name": "build_signature",
"signature": "def build_signature(self, date, content_length, method, content_type, resource)"
},
{
"docstring": "Build and send a request to the POST API.",
"name": "post_data",
"signature": "def post_data(self, custom... | 2 | stack_v2_sparse_classes_30k_val_002579 | Implement the Python class `AzureSentinel` described below.
Class description:
AzureSentinel is Used to post data to log analytics.
Method signatures and docstrings:
- def build_signature(self, date, content_length, method, content_type, resource): To build the signature.
- def post_data(self, customer_id, body, log_... | Implement the Python class `AzureSentinel` described below.
Class description:
AzureSentinel is Used to post data to log analytics.
Method signatures and docstrings:
- def build_signature(self, date, content_length, method, content_type, resource): To build the signature.
- def post_data(self, customer_id, body, log_... | 4536a3f6b9bdef902312b3d96f9c2e66b8bf52c1 | <|skeleton|>
class AzureSentinel:
"""AzureSentinel is Used to post data to log analytics."""
def build_signature(self, date, content_length, method, content_type, resource):
"""To build the signature."""
<|body_0|>
def post_data(self, customer_id, body, log_type):
"""Build and send... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class AzureSentinel:
"""AzureSentinel is Used to post data to log analytics."""
def build_signature(self, date, content_length, method, content_type, resource):
"""To build the signature."""
x_headers = 'x-ms-date:' + date
string_to_hash = method + '\n' + str(content_length) + '\n' + co... | the_stack_v2_python_sparse | Solutions/SecurityScorecard Cybersecurity Ratings/Data Connectors/SecurityScorecardIssue/SecurityScorecardIssueSentinelConnector/writers.py | Azure/Azure-Sentinel | train | 3,697 |
da4b0c4c9bdb0fd012ca268a1a2b37051b7592ae | [
"unlabelled = self.labels.mask.nonzero()[0]\nif len(unlabelled):\n index = numpy.random.choice(unlabelled)\n return index\nreturn 0",
"indices = set()\nunlabelled = self.labels.mask.nonzero()[0]\nif len(unlabelled) < n:\n return unlabelled\nwhile len(indices) < n:\n index = numpy.random.choice(unlabel... | <|body_start_0|>
unlabelled = self.labels.mask.nonzero()[0]
if len(unlabelled):
index = numpy.random.choice(unlabelled)
return index
return 0
<|end_body_0|>
<|body_start_1|>
indices = set()
unlabelled = self.labels.mask.nonzero()[0]
if len(unlabel... | Pool-based learning with random sampling. | RandomSampler | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RandomSampler:
"""Pool-based learning with random sampling."""
def sample_index(self):
"""Finds index of a random unlabelled point."""
<|body_0|>
def sample_indices(self, n):
"""Finds indices of n random unlabelled points."""
<|body_1|>
<|end_skeleton|>
... | stack_v2_sparse_classes_75kplus_train_065906 | 2,413 | permissive | [
{
"docstring": "Finds index of a random unlabelled point.",
"name": "sample_index",
"signature": "def sample_index(self)"
},
{
"docstring": "Finds indices of n random unlabelled points.",
"name": "sample_indices",
"signature": "def sample_indices(self, n)"
}
] | 2 | stack_v2_sparse_classes_30k_train_037359 | Implement the Python class `RandomSampler` described below.
Class description:
Pool-based learning with random sampling.
Method signatures and docstrings:
- def sample_index(self): Finds index of a random unlabelled point.
- def sample_indices(self, n): Finds indices of n random unlabelled points. | Implement the Python class `RandomSampler` described below.
Class description:
Pool-based learning with random sampling.
Method signatures and docstrings:
- def sample_index(self): Finds index of a random unlabelled point.
- def sample_indices(self, n): Finds indices of n random unlabelled points.
<|skeleton|>
class... | ce14432c36de0574b73d813304365b74446a61f8 | <|skeleton|>
class RandomSampler:
"""Pool-based learning with random sampling."""
def sample_index(self):
"""Finds index of a random unlabelled point."""
<|body_0|>
def sample_indices(self, n):
"""Finds indices of n random unlabelled points."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class RandomSampler:
"""Pool-based learning with random sampling."""
def sample_index(self):
"""Finds index of a random unlabelled point."""
unlabelled = self.labels.mask.nonzero()[0]
if len(unlabelled):
index = numpy.random.choice(unlabelled)
return index
... | the_stack_v2_python_sparse | crowdastro/active_learning/random_sampler.py | vishalbelsare/crowdastro | train | 0 |
0ddbf94e467228697a54fbf0a4349c5c6bf9ad6f | [
"self.conf = {}\nself.ini = ini_file\nself.verbose = verbose\nself.load_config()",
"parser = SafeConfigParser()\nparser.read(self.ini, encoding='utf-8')\nfor section_name in parser.sections():\n self.conf[section_name] = {}\n for key, value in parser.items(section_name):\n self.conf[section_name][key... | <|body_start_0|>
self.conf = {}
self.ini = ini_file
self.verbose = verbose
self.load_config()
<|end_body_0|>
<|body_start_1|>
parser = SafeConfigParser()
parser.read(self.ini, encoding='utf-8')
for section_name in parser.sections():
self.conf[section_... | Class de gestion d'une configuration: Charge la configuration depuis le fichier *.ini, sauve les changements de configuration, enregistre les changements par section, clé. | MyConfig | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MyConfig:
"""Class de gestion d'une configuration: Charge la configuration depuis le fichier *.ini, sauve les changements de configuration, enregistre les changements par section, clé."""
def __init__(self, ini_file, verbose=1):
"""Charge la config depuis un fichier *.ini Le chemin d... | stack_v2_sparse_classes_75kplus_train_065907 | 2,213 | no_license | [
{
"docstring": "Charge la config depuis un fichier *.ini Le chemin devrait être donné avec son chemin absolu.",
"name": "__init__",
"signature": "def __init__(self, ini_file, verbose=1)"
},
{
"docstring": "Lit le fichier *.ini, et copie la config dans un dictionnaire.",
"name": "load_config"... | 3 | null | Implement the Python class `MyConfig` described below.
Class description:
Class de gestion d'une configuration: Charge la configuration depuis le fichier *.ini, sauve les changements de configuration, enregistre les changements par section, clé.
Method signatures and docstrings:
- def __init__(self, ini_file, verbose... | Implement the Python class `MyConfig` described below.
Class description:
Class de gestion d'une configuration: Charge la configuration depuis le fichier *.ini, sauve les changements de configuration, enregistre les changements par section, clé.
Method signatures and docstrings:
- def __init__(self, ini_file, verbose... | b931caf107457aea4caea2b0ce821e981d19bd79 | <|skeleton|>
class MyConfig:
"""Class de gestion d'une configuration: Charge la configuration depuis le fichier *.ini, sauve les changements de configuration, enregistre les changements par section, clé."""
def __init__(self, ini_file, verbose=1):
"""Charge la config depuis un fichier *.ini Le chemin d... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class MyConfig:
"""Class de gestion d'une configuration: Charge la configuration depuis le fichier *.ini, sauve les changements de configuration, enregistre les changements par section, clé."""
def __init__(self, ini_file, verbose=1):
"""Charge la config depuis un fichier *.ini Le chemin devrait être d... | the_stack_v2_python_sparse | py_9_divers/py_80_exo_class_bad/py_84_config/py_84_config_response.py | sergeLabo/formation_python | train | 0 |
d397b6a6f9d2486960f255425febc1717c95e09b | [
"conflict = fake_conflict('fool\\nbar\\neggs\\n', 'foo\\nbar\\neggs\\n', 'foo\\nbaz\\negg\\n')\nhandle_conflict(conflict)\nself.assertTrue(conflict.is_resolved())\nself.assertEqual(conflict.content, 'fool\\nbaz\\negg\\n')",
"conflict = fake_conflict('fool\\nbar\\neggs\\nspam\\n', 'foo\\nbar\\neggs\\n', 'foo\\nbaz... | <|body_start_0|>
conflict = fake_conflict('fool\nbar\neggs\n', 'foo\nbar\neggs\n', 'foo\nbaz\negg\n')
handle_conflict(conflict)
self.assertTrue(conflict.is_resolved())
self.assertEqual(conflict.content, 'fool\nbaz\negg\n')
<|end_body_0|>
<|body_start_1|>
conflict = fake_conflict... | SolverTest | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SolverTest:
def test_simple(self):
"""Test a conflict with woven changes"""
<|body_0|>
def test_not_woven_1(self):
"""Test a conflict with woven changes + additions"""
<|body_1|>
def test_not_woven_2(self):
"""Test a conflict with woven changes +... | stack_v2_sparse_classes_75kplus_train_065908 | 2,887 | permissive | [
{
"docstring": "Test a conflict with woven changes",
"name": "test_simple",
"signature": "def test_simple(self)"
},
{
"docstring": "Test a conflict with woven changes + additions",
"name": "test_not_woven_1",
"signature": "def test_not_woven_1(self)"
},
{
"docstring": "Test a con... | 6 | stack_v2_sparse_classes_30k_val_002367 | Implement the Python class `SolverTest` described below.
Class description:
Implement the SolverTest class.
Method signatures and docstrings:
- def test_simple(self): Test a conflict with woven changes
- def test_not_woven_1(self): Test a conflict with woven changes + additions
- def test_not_woven_2(self): Test a co... | Implement the Python class `SolverTest` described below.
Class description:
Implement the SolverTest class.
Method signatures and docstrings:
- def test_simple(self): Test a conflict with woven changes
- def test_not_woven_1(self): Test a conflict with woven changes + additions
- def test_not_woven_2(self): Test a co... | 81ef5e9636f264221b8f8b6f6aaf4cfb5b6a79b6 | <|skeleton|>
class SolverTest:
def test_simple(self):
"""Test a conflict with woven changes"""
<|body_0|>
def test_not_woven_1(self):
"""Test a conflict with woven changes + additions"""
<|body_1|>
def test_not_woven_2(self):
"""Test a conflict with woven changes +... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class SolverTest:
def test_simple(self):
"""Test a conflict with woven changes"""
conflict = fake_conflict('fool\nbar\neggs\n', 'foo\nbar\neggs\n', 'foo\nbaz\negg\n')
handle_conflict(conflict)
self.assertTrue(conflict.is_resolved())
self.assertEqual(conflict.content, 'fool\nb... | the_stack_v2_python_sparse | _gen_woven_test.py | clmoreno/AutoMergeTool | train | 1 | |
e8673af90874393be570446b34e7178597effd77 | [
"cutoffs = sorted(cutoffs)\nnum_groups = len(cutoffs)\nif i <= cutoffs[0]:\n return '小于等于{}'.format(cutoffs[0])\nelif i > cutoffs[-1]:\n return '大于{}'.format(cutoffs[-1])\nelse:\n for j in range(1, num_groups):\n if cutoffs[j - 1] < i <= cutoffs[j]:\n return '大于%s小于等于%s' % (cutoffs[j - 1]... | <|body_start_0|>
cutoffs = sorted(cutoffs)
num_groups = len(cutoffs)
if i <= cutoffs[0]:
return '小于等于{}'.format(cutoffs[0])
elif i > cutoffs[-1]:
return '大于{}'.format(cutoffs[-1])
else:
for j in range(1, num_groups):
if cutoffs[... | 1. value2group(self, x, cutoffs) 2. group_add_df(self, df, X, cutoffs, var, inplace) | Value2Group | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Value2Group:
"""1. value2group(self, x, cutoffs) 2. group_add_df(self, df, X, cutoffs, var, inplace)"""
def value2group(self, i, cutoffs):
"""Args: i:某个待分组的元素 cutoffs: 切分点的列表 Returns: 这个元素对应的组别"""
<|body_0|>
def group_add_df(self, df, var, cutoffs, new_var, inplace):
... | stack_v2_sparse_classes_75kplus_train_065909 | 9,326 | no_license | [
{
"docstring": "Args: i:某个待分组的元素 cutoffs: 切分点的列表 Returns: 这个元素对应的组别",
"name": "value2group",
"signature": "def value2group(self, i, cutoffs)"
},
{
"docstring": "对某个变量进行分组处理 Args: df: 待分组的数据框 var: 待分组的变量名 cutoffs: 分组的切分点 new_var: 分组后的变量名 inplace: 是否删除原变量 Returns: 分组后的数据框",
"name": "group_add_... | 2 | null | Implement the Python class `Value2Group` described below.
Class description:
1. value2group(self, x, cutoffs) 2. group_add_df(self, df, X, cutoffs, var, inplace)
Method signatures and docstrings:
- def value2group(self, i, cutoffs): Args: i:某个待分组的元素 cutoffs: 切分点的列表 Returns: 这个元素对应的组别
- def group_add_df(self, df, var,... | Implement the Python class `Value2Group` described below.
Class description:
1. value2group(self, x, cutoffs) 2. group_add_df(self, df, X, cutoffs, var, inplace)
Method signatures and docstrings:
- def value2group(self, i, cutoffs): Args: i:某个待分组的元素 cutoffs: 切分点的列表 Returns: 这个元素对应的组别
- def group_add_df(self, df, var,... | cc25cb60f1c1c89b4591bbdaec8db1eeba818377 | <|skeleton|>
class Value2Group:
"""1. value2group(self, x, cutoffs) 2. group_add_df(self, df, X, cutoffs, var, inplace)"""
def value2group(self, i, cutoffs):
"""Args: i:某个待分组的元素 cutoffs: 切分点的列表 Returns: 这个元素对应的组别"""
<|body_0|>
def group_add_df(self, df, var, cutoffs, new_var, inplace):
... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Value2Group:
"""1. value2group(self, x, cutoffs) 2. group_add_df(self, df, X, cutoffs, var, inplace)"""
def value2group(self, i, cutoffs):
"""Args: i:某个待分组的元素 cutoffs: 切分点的列表 Returns: 这个元素对应的组别"""
cutoffs = sorted(cutoffs)
num_groups = len(cutoffs)
if i <= cutoffs[0]:
... | the_stack_v2_python_sparse | bus_drop/loans_drop/feature_engineering/autobin.py | xeon-ye/dgg-pro | train | 0 |
08d6d950974a21c19ecf1ae273ee63ceb2f47534 | [
"stdout, stderr = self.redirect_streams(lambda: show.list_files([]))\nself.assertEqual(stdout, pathlist(FILES))\nself.assertEqual(stderr, '')",
"stdout, stderr = self.redirect_streams(lambda: show.list_files(['']))\nself.assertEqual(stdout, pathlist(FILES))\nself.assertEqual(stderr, '')",
"stdout, stderr = self... | <|body_start_0|>
stdout, stderr = self.redirect_streams(lambda: show.list_files([]))
self.assertEqual(stdout, pathlist(FILES))
self.assertEqual(stderr, '')
<|end_body_0|>
<|body_start_1|>
stdout, stderr = self.redirect_streams(lambda: show.list_files(['']))
self.assertEqual(stdo... | Basic tests of list_files | TestListFiles | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestListFiles:
"""Basic tests of list_files"""
def test_list_files_empty_list(self):
"""an empty list in should list all files"""
<|body_0|>
def test_list_files_empty_string(self):
"""an empty string in should list all files"""
<|body_1|>
def test_li... | stack_v2_sparse_classes_75kplus_train_065910 | 5,423 | no_license | [
{
"docstring": "an empty list in should list all files",
"name": "test_list_files_empty_list",
"signature": "def test_list_files_empty_list(self)"
},
{
"docstring": "an empty string in should list all files",
"name": "test_list_files_empty_string",
"signature": "def test_list_files_empty... | 4 | stack_v2_sparse_classes_30k_train_023186 | Implement the Python class `TestListFiles` described below.
Class description:
Basic tests of list_files
Method signatures and docstrings:
- def test_list_files_empty_list(self): an empty list in should list all files
- def test_list_files_empty_string(self): an empty string in should list all files
- def test_list_f... | Implement the Python class `TestListFiles` described below.
Class description:
Basic tests of list_files
Method signatures and docstrings:
- def test_list_files_empty_list(self): an empty list in should list all files
- def test_list_files_empty_string(self): an empty string in should list all files
- def test_list_f... | 539868dab2041b7694c0d53e8e74cf1b5b033653 | <|skeleton|>
class TestListFiles:
"""Basic tests of list_files"""
def test_list_files_empty_list(self):
"""an empty list in should list all files"""
<|body_0|>
def test_list_files_empty_string(self):
"""an empty string in should list all files"""
<|body_1|>
def test_li... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class TestListFiles:
"""Basic tests of list_files"""
def test_list_files_empty_list(self):
"""an empty list in should list all files"""
stdout, stderr = self.redirect_streams(lambda: show.list_files([]))
self.assertEqual(stdout, pathlist(FILES))
self.assertEqual(stderr, '')
... | the_stack_v2_python_sparse | test_igseq/test_show.py | ShawHahnLab/igseq | train | 1 |
6e96d7de05b50715e87df5270ef3fda7390c1046 | [
"while num >= 10:\n string = str(num)\n num = 0\n for i in range(len(string)):\n num += int(string[i])\nreturn num",
"if num == 0:\n return 0\nelif num % 9 == 0:\n return 9\nelse:\n return num % 9"
] | <|body_start_0|>
while num >= 10:
string = str(num)
num = 0
for i in range(len(string)):
num += int(string[i])
return num
<|end_body_0|>
<|body_start_1|>
if num == 0:
return 0
elif num % 9 == 0:
return 9
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def addDigits(self, num):
""":type num: int :rtype: int"""
<|body_0|>
def addDigits2(self, num):
""":type num: int :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
while num >= 10:
string = str(num)
num =... | stack_v2_sparse_classes_75kplus_train_065911 | 673 | no_license | [
{
"docstring": ":type num: int :rtype: int",
"name": "addDigits",
"signature": "def addDigits(self, num)"
},
{
"docstring": ":type num: int :rtype: int",
"name": "addDigits2",
"signature": "def addDigits2(self, num)"
}
] | 2 | stack_v2_sparse_classes_30k_train_048905 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def addDigits(self, num): :type num: int :rtype: int
- def addDigits2(self, num): :type num: int :rtype: int | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def addDigits(self, num): :type num: int :rtype: int
- def addDigits2(self, num): :type num: int :rtype: int
<|skeleton|>
class Solution:
def addDigits(self, num):
... | 0fc4c7af59246e3064db41989a45d9db413a624b | <|skeleton|>
class Solution:
def addDigits(self, num):
""":type num: int :rtype: int"""
<|body_0|>
def addDigits2(self, num):
""":type num: int :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Solution:
def addDigits(self, num):
""":type num: int :rtype: int"""
while num >= 10:
string = str(num)
num = 0
for i in range(len(string)):
num += int(string[i])
return num
def addDigits2(self, num):
""":type num: int :r... | the_stack_v2_python_sparse | 258. Add Digits/addDigits.py | Macielyoung/LeetCode | train | 1 | |
4a5224aa4828debaa4142235558bf35b9e75e097 | [
"sdram = SDRAMResource(128 * 2 ** 20)\nself.assertEqual(sdram.get_value(), 128 * 2 ** 20)\nsdram = SDRAMResource(128 * 2 ** 19)\nself.assertEqual(sdram.get_value(), 128 * 2 ** 19)\nsdram = SDRAMResource(128 * 2 ** 21)\nself.assertEqual(sdram.get_value(), 128 * 2 ** 21)",
"dtcm = DTCMResource(128 * 2 ** 20)\nself.... | <|body_start_0|>
sdram = SDRAMResource(128 * 2 ** 20)
self.assertEqual(sdram.get_value(), 128 * 2 ** 20)
sdram = SDRAMResource(128 * 2 ** 19)
self.assertEqual(sdram.get_value(), 128 * 2 ** 19)
sdram = SDRAMResource(128 * 2 ** 21)
self.assertEqual(sdram.get_value(), 128 * ... | unit tests on the resources object | TestResourceModels | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestResourceModels:
"""unit tests on the resources object"""
def test_sdram(self):
"""test that adding a sdram resource to a resoruce container works correctly :return:"""
<|body_0|>
def test_dtcm(self):
"""test that adding a dtcm resource to a resoruce container... | stack_v2_sparse_classes_75kplus_train_065912 | 3,274 | no_license | [
{
"docstring": "test that adding a sdram resource to a resoruce container works correctly :return:",
"name": "test_sdram",
"signature": "def test_sdram(self)"
},
{
"docstring": "test that adding a dtcm resource to a resoruce container works correctly :return:",
"name": "test_dtcm",
"sign... | 4 | stack_v2_sparse_classes_30k_val_001385 | Implement the Python class `TestResourceModels` described below.
Class description:
unit tests on the resources object
Method signatures and docstrings:
- def test_sdram(self): test that adding a sdram resource to a resoruce container works correctly :return:
- def test_dtcm(self): test that adding a dtcm resource to... | Implement the Python class `TestResourceModels` described below.
Class description:
unit tests on the resources object
Method signatures and docstrings:
- def test_sdram(self): test that adding a sdram resource to a resoruce container works correctly :return:
- def test_dtcm(self): test that adding a dtcm resource to... | 5c2faba4d823e9341e5c18f61ea9bf8c6e15b687 | <|skeleton|>
class TestResourceModels:
"""unit tests on the resources object"""
def test_sdram(self):
"""test that adding a sdram resource to a resoruce container works correctly :return:"""
<|body_0|>
def test_dtcm(self):
"""test that adding a dtcm resource to a resoruce container... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class TestResourceModels:
"""unit tests on the resources object"""
def test_sdram(self):
"""test that adding a sdram resource to a resoruce container works correctly :return:"""
sdram = SDRAMResource(128 * 2 ** 20)
self.assertEqual(sdram.get_value(), 128 * 2 ** 20)
sdram = SDRAM... | the_stack_v2_python_sparse | unittests/model_tests/resources_tests/test_resources_model.py | kfriesth/PACMAN | train | 0 |
879c388195df8a5db2c433a6f31f6c3783e9f86d | [
"super(ConvNet, self).__init__(torch_ref=torch_ref)\nself.conv1 = self.torch_ref.nn.Conv2d(in_channels=1, out_channels=5, kernel_size=5)\nself.fc1 = self.torch_ref.nn.Linear(2880, 10)\nself.fc2 = self.torch_ref.nn.Linear(10, 5)",
"x = self.conv1(x)\nx = self.torch_ref.nn.functional.relu(x)\nx = x.view(1, -1)\nx =... | <|body_start_0|>
super(ConvNet, self).__init__(torch_ref=torch_ref)
self.conv1 = self.torch_ref.nn.Conv2d(in_channels=1, out_channels=5, kernel_size=5)
self.fc1 = self.torch_ref.nn.Linear(2880, 10)
self.fc2 = self.torch_ref.nn.Linear(10, 5)
<|end_body_0|>
<|body_start_1|>
x = se... | Simple convolutional network. | ConvNet | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ConvNet:
"""Simple convolutional network."""
def __init__(self, torch_ref):
"""Initialize convolutional network. Arguments: torch_ref: Reference to the torch library"""
<|body_0|>
def forward(self, x):
"""Do a feedforward through the layer. Args: x (MPCTensor): t... | stack_v2_sparse_classes_75kplus_train_065913 | 2,433 | permissive | [
{
"docstring": "Initialize convolutional network. Arguments: torch_ref: Reference to the torch library",
"name": "__init__",
"signature": "def __init__(self, torch_ref)"
},
{
"docstring": "Do a feedforward through the layer. Args: x (MPCTensor): the input Returns: An MPCTensor representing the l... | 2 | stack_v2_sparse_classes_30k_test_002332 | Implement the Python class `ConvNet` described below.
Class description:
Simple convolutional network.
Method signatures and docstrings:
- def __init__(self, torch_ref): Initialize convolutional network. Arguments: torch_ref: Reference to the torch library
- def forward(self, x): Do a feedforward through the layer. A... | Implement the Python class `ConvNet` described below.
Class description:
Simple convolutional network.
Method signatures and docstrings:
- def __init__(self, torch_ref): Initialize convolutional network. Arguments: torch_ref: Reference to the torch library
- def forward(self, x): Do a feedforward through the layer. A... | ee6ac74050acd03c3088104855d0b8e4ab3e03fa | <|skeleton|>
class ConvNet:
"""Simple convolutional network."""
def __init__(self, torch_ref):
"""Initialize convolutional network. Arguments: torch_ref: Reference to the torch library"""
<|body_0|>
def forward(self, x):
"""Do a feedforward through the layer. Args: x (MPCTensor): t... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class ConvNet:
"""Simple convolutional network."""
def __init__(self, torch_ref):
"""Initialize convolutional network. Arguments: torch_ref: Reference to the torch library"""
super(ConvNet, self).__init__(torch_ref=torch_ref)
self.conv1 = self.torch_ref.nn.Conv2d(in_channels=1, out_chan... | the_stack_v2_python_sparse | benchmarks/module/conv_model.py | shubhank-saxena/SyMPC | train | 1 |
e9fe2c55b9826956f722e3b0e5caf5230b618fc7 | [
"if not root:\n return '[]'\nlevel = [root]\nres = []\nwhile level:\n next = []\n for node in level:\n if node:\n res.append(node.val)\n if node.left:\n next.append(node.left)\n else:\n next.append(None)\n if node.right:\n ... | <|body_start_0|>
if not root:
return '[]'
level = [root]
res = []
while level:
next = []
for node in level:
if node:
res.append(node.val)
if node.left:
next.append(node.lef... | Codec | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
<|body_0|>
def deserialize(self, data):
"""Decodes your encoded data to tree. :type data: str :rtype: TreeNode"""
<|body_1|>
<|end_skeleton|>
<|body_... | stack_v2_sparse_classes_75kplus_train_065914 | 1,974 | no_license | [
{
"docstring": "Encodes a tree to a single string. :type root: TreeNode :rtype: str",
"name": "serialize",
"signature": "def serialize(self, root)"
},
{
"docstring": "Decodes your encoded data to tree. :type data: str :rtype: TreeNode",
"name": "deserialize",
"signature": "def deserializ... | 2 | stack_v2_sparse_classes_30k_train_023044 | 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:... | 0f45a05d258d7dced844c657da2362d87bee15a8 | <|skeleton|>
class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
<|body_0|>
def deserialize(self, data):
"""Decodes your encoded data to tree. :type data: str :rtype: TreeNode"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
if not root:
return '[]'
level = [root]
res = []
while level:
next = []
for node in level:
if node:
... | the_stack_v2_python_sparse | 297-Serialize-and-Deserialize-Binary-Tree/solution.py | zmxrice/leetcodetraining | train | 0 | |
cfd9772680a75ded54eedb6b7e4271393d03b9b1 | [
"self.params = {}\nself.reg = reg\nself.dtype = dtype\nC, H, W = input_dim\nconv_stride, conv_pad = (1, int((filter_size - 1) / 2))\nconv_H = int(1 + (H + 2 * conv_pad - filter_size) / conv_stride)\nconv_W = int(1 + (W + 2 * conv_pad - filter_size) / conv_stride)\nself.params['W1'] = np.random.normal(0, weight_scal... | <|body_start_0|>
self.params = {}
self.reg = reg
self.dtype = dtype
C, H, W = input_dim
conv_stride, conv_pad = (1, int((filter_size - 1) / 2))
conv_H = int(1 + (H + 2 * conv_pad - filter_size) / conv_stride)
conv_W = int(1 + (W + 2 * conv_pad - filter_size) / con... | A three-layer convolutional network with the following architecture: conv - relu - 2x2 max pool - affine - relu - affine - softmax The network operates on minibatches of data that have shape (N, C, H, W) consisting of N images, each with height H and width W and with C input channels. | ThreeLayerConvNet | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ThreeLayerConvNet:
"""A three-layer convolutional network with the following architecture: conv - relu - 2x2 max pool - affine - relu - affine - softmax The network operates on minibatches of data that have shape (N, C, H, W) consisting of N images, each with height H and width W and with C input... | stack_v2_sparse_classes_75kplus_train_065915 | 3,757 | no_license | [
{
"docstring": "Initialize a new network. Inputs: - input_dim: Tuple (C, H, W) giving size of input data - num_filters: Number of filters to use in the convolutional layer - filter_size: Size of filters to use in the convolutional layer - hidden_dim: Number of units to use in the fully-connected hidden layer - ... | 2 | null | Implement the Python class `ThreeLayerConvNet` described below.
Class description:
A three-layer convolutional network with the following architecture: conv - relu - 2x2 max pool - affine - relu - affine - softmax The network operates on minibatches of data that have shape (N, C, H, W) consisting of N images, each wit... | Implement the Python class `ThreeLayerConvNet` described below.
Class description:
A three-layer convolutional network with the following architecture: conv - relu - 2x2 max pool - affine - relu - affine - softmax The network operates on minibatches of data that have shape (N, C, H, W) consisting of N images, each wit... | fb69e5944f00247c1d0a7aabdf425c553baf3dc9 | <|skeleton|>
class ThreeLayerConvNet:
"""A three-layer convolutional network with the following architecture: conv - relu - 2x2 max pool - affine - relu - affine - softmax The network operates on minibatches of data that have shape (N, C, H, W) consisting of N images, each with height H and width W and with C input... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class ThreeLayerConvNet:
"""A three-layer convolutional network with the following architecture: conv - relu - 2x2 max pool - affine - relu - affine - softmax The network operates on minibatches of data that have shape (N, C, H, W) consisting of N images, each with height H and width W and with C input channels."""... | the_stack_v2_python_sparse | cs231n/assignment2/cs231n/classifiers/cnn.py | bcrusu/ml | train | 2 |
6514e19df785c7de1b3420bf7bb00e5e7e55f60f | [
"self.verbose = verbose\nself.valid_indices = valid_indices\nself.test_indices = test_indices",
"num_datapoints = len(dataset)\nindices = np.arange(num_datapoints).tolist()\ntrain_indices = []\nif self.valid_indices is None:\n self.valid_indices = []\nif self.test_indices is None:\n self.test_indices = []\n... | <|body_start_0|>
self.verbose = verbose
self.valid_indices = valid_indices
self.test_indices = test_indices
<|end_body_0|>
<|body_start_1|>
num_datapoints = len(dataset)
indices = np.arange(num_datapoints).tolist()
train_indices = []
if self.valid_indices is None... | Class for splits based on input order. | IndiceSplitter | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class IndiceSplitter:
"""Class for splits based on input order."""
def __init__(self, verbose=False, valid_indices=None, test_indices=None):
"""Parameters ----------- valid_indices: list of int indices of samples in the valid set test_indices: list of int indices of samples in the test set... | stack_v2_sparse_classes_75kplus_train_065916 | 1,989 | no_license | [
{
"docstring": "Parameters ----------- valid_indices: list of int indices of samples in the valid set test_indices: list of int indices of samples in the test set",
"name": "__init__",
"signature": "def __init__(self, verbose=False, valid_indices=None, test_indices=None)"
},
{
"docstring": "Spli... | 2 | stack_v2_sparse_classes_30k_train_044017 | Implement the Python class `IndiceSplitter` described below.
Class description:
Class for splits based on input order.
Method signatures and docstrings:
- def __init__(self, verbose=False, valid_indices=None, test_indices=None): Parameters ----------- valid_indices: list of int indices of samples in the valid set tes... | Implement the Python class `IndiceSplitter` described below.
Class description:
Class for splits based on input order.
Method signatures and docstrings:
- def __init__(self, verbose=False, valid_indices=None, test_indices=None): Parameters ----------- valid_indices: list of int indices of samples in the valid set tes... | 57e40d04181059ca39890d22361606edfadcc930 | <|skeleton|>
class IndiceSplitter:
"""Class for splits based on input order."""
def __init__(self, verbose=False, valid_indices=None, test_indices=None):
"""Parameters ----------- valid_indices: list of int indices of samples in the valid set test_indices: list of int indices of samples in the test set... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class IndiceSplitter:
"""Class for splits based on input order."""
def __init__(self, verbose=False, valid_indices=None, test_indices=None):
"""Parameters ----------- valid_indices: list of int indices of samples in the valid set test_indices: list of int indices of samples in the test set"""
s... | the_stack_v2_python_sparse | utils/splitter/indexsplitter.py | moguizhizi/Adaptive-Graph-Convolutional-Network | train | 0 |
f1aec19ea3bd63dbc774b17f6d551b6c7adc45ac | [
"if len(nums) == 0:\n return 0\nt = nums[0]\nn = 0\nfor i in range(1, len(nums)):\n t, n = (n + nums[i], max(t, n))\nreturn max(t, n)",
"if len(nums) == 0:\n return 0\nif len(nums) == 1:\n return nums[0]\nif len(nums) == 2:\n return max(nums)\nreturn max(self.sub_rob(nums[1:]), self.sub_rob(nums[:-... | <|body_start_0|>
if len(nums) == 0:
return 0
t = nums[0]
n = 0
for i in range(1, len(nums)):
t, n = (n + nums[i], max(t, n))
return max(t, n)
<|end_body_0|>
<|body_start_1|>
if len(nums) == 0:
return 0
if len(nums) == 1:
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def sub_rob(self, nums):
""":type nums: List[int] :rtype: int"""
<|body_0|>
def rob(self, nums):
""":type nums: List[int] :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
if len(nums) == 0:
return 0
t = nums[... | stack_v2_sparse_classes_75kplus_train_065917 | 716 | no_license | [
{
"docstring": ":type nums: List[int] :rtype: int",
"name": "sub_rob",
"signature": "def sub_rob(self, nums)"
},
{
"docstring": ":type nums: List[int] :rtype: int",
"name": "rob",
"signature": "def rob(self, nums)"
}
] | 2 | stack_v2_sparse_classes_30k_val_001052 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def sub_rob(self, nums): :type nums: List[int] :rtype: int
- def rob(self, nums): :type nums: List[int] :rtype: int | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def sub_rob(self, nums): :type nums: List[int] :rtype: int
- def rob(self, nums): :type nums: List[int] :rtype: int
<|skeleton|>
class Solution:
def sub_rob(self, nums):
... | c9a53ef2fc1fd1fea7377c3633689fa87601dba6 | <|skeleton|>
class Solution:
def sub_rob(self, nums):
""":type nums: List[int] :rtype: int"""
<|body_0|>
def rob(self, nums):
""":type nums: List[int] :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Solution:
def sub_rob(self, nums):
""":type nums: List[int] :rtype: int"""
if len(nums) == 0:
return 0
t = nums[0]
n = 0
for i in range(1, len(nums)):
t, n = (n + nums[i], max(t, n))
return max(t, n)
def rob(self, nums):
""":... | the_stack_v2_python_sparse | leetcode213.py | yuchien302/LeetCode | train | 2 | |
e3ad00fe65376b1a8e895d3b70e28d91a817aff0 | [
"features_ser = [port.serialize() for port in self.ports]\ncapabilities_ser = (OFPC_FLOW_STATS if self.cap_flow_stats else 0) | (OFPC_TABLE_STATS if self.cap_table_stats else 0) | (OFPC_PORT_STATS if self.cap_port_stats else 0) | (OFPC_STP if self.cap_stp else 0) | (OFPC_IP_REASM if self.cap_ip_reasm else 0) | (OFP... | <|body_start_0|>
features_ser = [port.serialize() for port in self.ports]
capabilities_ser = (OFPC_FLOW_STATS if self.cap_flow_stats else 0) | (OFPC_TABLE_STATS if self.cap_table_stats else 0) | (OFPC_PORT_STATS if self.cap_port_stats else 0) | (OFPC_STP if self.cap_stp else 0) | (OFPC_IP_REASM if self.... | The features of an OpenFlow switch. The attributes of a datapath are: datapath_id: The datapath's unique ID, as a binary string. n_buffers: The maximum number of packets buffered at once. n_tables: The number of tables supported by the datapath. cap_*: Boolean flags indicating each the support of not of a capability: c... | SwitchFeatures | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SwitchFeatures:
"""The features of an OpenFlow switch. The attributes of a datapath are: datapath_id: The datapath's unique ID, as a binary string. n_buffers: The maximum number of packets buffered at once. n_tables: The number of tables supported by the datapath. cap_*: Boolean flags indicating ... | stack_v2_sparse_classes_75kplus_train_065918 | 22,976 | permissive | [
{
"docstring": "Serialize this object into an OpenFlow ofp_switch_features. The returned string can be passed to deserialize() to recreate a copy of this object. Returns: A new list of binary strings that is a serialized form of this object into an OpenFlow ofp_switch_features. The ofp_header structure is not i... | 2 | stack_v2_sparse_classes_30k_train_046165 | Implement the Python class `SwitchFeatures` described below.
Class description:
The features of an OpenFlow switch. The attributes of a datapath are: datapath_id: The datapath's unique ID, as a binary string. n_buffers: The maximum number of packets buffered at once. n_tables: The number of tables supported by the dat... | Implement the Python class `SwitchFeatures` described below.
Class description:
The features of an OpenFlow switch. The attributes of a datapath are: datapath_id: The datapath's unique ID, as a binary string. n_buffers: The maximum number of packets buffered at once. n_tables: The number of tables supported by the dat... | 4ef1783fc74320e66ee7a71576dc91511f238a81 | <|skeleton|>
class SwitchFeatures:
"""The features of an OpenFlow switch. The attributes of a datapath are: datapath_id: The datapath's unique ID, as a binary string. n_buffers: The maximum number of packets buffered at once. n_tables: The number of tables supported by the datapath. cap_*: Boolean flags indicating ... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class SwitchFeatures:
"""The features of an OpenFlow switch. The attributes of a datapath are: datapath_id: The datapath's unique ID, as a binary string. n_buffers: The maximum number of packets buffered at once. n_tables: The number of tables supported by the datapath. cap_*: Boolean flags indicating each the supp... | the_stack_v2_python_sparse | src/openfaucet/ofconfig.py | bharathi26/openfaucet | train | 0 |
004d4556399354b2e7880bd247f57814c21f3644 | [
"ret = super().to_representation(instance)\nif ret.get('parameters', False):\n ret['parameters'] = json.loads(ret['parameters'])\nreturn ret",
"if data.get('parameters', False):\n data['parameters'] = json.dumps(data['parameters'])\nif data.get('score_info', False):\n data['score_info'] = json.dumps(data... | <|body_start_0|>
ret = super().to_representation(instance)
if ret.get('parameters', False):
ret['parameters'] = json.loads(ret['parameters'])
return ret
<|end_body_0|>
<|body_start_1|>
if data.get('parameters', False):
data['parameters'] = json.dumps(data['parame... | Serialize and deserialized trial instances. Ensure to pass from the serializer for adjusting the parameters. | TrialSerializer | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TrialSerializer:
"""Serialize and deserialized trial instances. Ensure to pass from the serializer for adjusting the parameters."""
def to_representation(self, instance):
"""Convert the serialized parameters into a real dictionary. Args: :param instance: instance to be deserialized :... | stack_v2_sparse_classes_75kplus_train_065919 | 5,050 | no_license | [
{
"docstring": "Convert the serialized parameters into a real dictionary. Args: :param instance: instance to be deserialized :return: Deserialized instance",
"name": "to_representation",
"signature": "def to_representation(self, instance)"
},
{
"docstring": "Convert the parameter dictionary into... | 3 | stack_v2_sparse_classes_30k_train_046101 | Implement the Python class `TrialSerializer` described below.
Class description:
Serialize and deserialized trial instances. Ensure to pass from the serializer for adjusting the parameters.
Method signatures and docstrings:
- def to_representation(self, instance): Convert the serialized parameters into a real diction... | Implement the Python class `TrialSerializer` described below.
Class description:
Serialize and deserialized trial instances. Ensure to pass from the serializer for adjusting the parameters.
Method signatures and docstrings:
- def to_representation(self, instance): Convert the serialized parameters into a real diction... | 27f861c09615aedfd96cffdebf7d9653f72b4d7b | <|skeleton|>
class TrialSerializer:
"""Serialize and deserialized trial instances. Ensure to pass from the serializer for adjusting the parameters."""
def to_representation(self, instance):
"""Convert the serialized parameters into a real dictionary. Args: :param instance: instance to be deserialized :... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class TrialSerializer:
"""Serialize and deserialized trial instances. Ensure to pass from the serializer for adjusting the parameters."""
def to_representation(self, instance):
"""Convert the serialized parameters into a real dictionary. Args: :param instance: instance to be deserialized :return: Deser... | the_stack_v2_python_sparse | API/serializers.py | AndreaCorsini1/Ahmet | train | 1 |
215d39e3f6579de07ec9544650e262eab5d77373 | [
"if x < 0:\n return None\nelif x <= 1:\n return x\nleft = 1\nright = x // 2 + 1\nif x > 10000:\n bits = 1\n cx = x\n while cx > 0:\n cx //= 10\n bits += 1\n left = 10 ** (bits // 2 - 1)\n right = left * 10\nwhile left < right:\n mid = left + (right - left) // 2\n mv = mid * ... | <|body_start_0|>
if x < 0:
return None
elif x <= 1:
return x
left = 1
right = x // 2 + 1
if x > 10000:
bits = 1
cx = x
while cx > 0:
cx //= 10
bits += 1
left = 10 ** (bits // 2... | Solution | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def mySqrt(self, x):
"""只要求返回整数解， 二叉查找即可 :param x: :return:"""
<|body_0|>
def sqrt(self, x):
"""平方根公式， 利用斜率计算 X(k+1) = (X(k) + n/X(k)) / 2 :param x: :return:"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
if x < 0:
return None... | stack_v2_sparse_classes_75kplus_train_065920 | 1,623 | permissive | [
{
"docstring": "只要求返回整数解， 二叉查找即可 :param x: :return:",
"name": "mySqrt",
"signature": "def mySqrt(self, x)"
},
{
"docstring": "平方根公式， 利用斜率计算 X(k+1) = (X(k) + n/X(k)) / 2 :param x: :return:",
"name": "sqrt",
"signature": "def sqrt(self, x)"
}
] | 2 | stack_v2_sparse_classes_30k_train_053790 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def mySqrt(self, x): 只要求返回整数解， 二叉查找即可 :param x: :return:
- def sqrt(self, x): 平方根公式， 利用斜率计算 X(k+1) = (X(k) + n/X(k)) / 2 :param x: :return: | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def mySqrt(self, x): 只要求返回整数解， 二叉查找即可 :param x: :return:
- def sqrt(self, x): 平方根公式， 利用斜率计算 X(k+1) = (X(k) + n/X(k)) / 2 :param x: :return:
<|skeleton|>
class Solution:
def... | 2830c7e2ada8dfd3dcdda7c06846116d4f944a27 | <|skeleton|>
class Solution:
def mySqrt(self, x):
"""只要求返回整数解， 二叉查找即可 :param x: :return:"""
<|body_0|>
def sqrt(self, x):
"""平方根公式， 利用斜率计算 X(k+1) = (X(k) + n/X(k)) / 2 :param x: :return:"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Solution:
def mySqrt(self, x):
"""只要求返回整数解， 二叉查找即可 :param x: :return:"""
if x < 0:
return None
elif x <= 1:
return x
left = 1
right = x // 2 + 1
if x > 10000:
bits = 1
cx = x
while cx > 0:
... | the_stack_v2_python_sparse | leetcode/medium/Sqrt.py | shhuan/algorithms | train | 0 | |
2593881bf3e264c82d735895ce6e1da7e942d5e8 | [
"queryset = self.queryset.filter(type='student').order_by('-created_at')[0]\nserializer = self.get_serializer(queryset, many=False)\nreturn Response(serializer.data)",
"try:\n cache_key = 'apk_release_student'\n data = cache.get(cache_key)\n if data:\n return Response(data)\nexcept Exception as e:... | <|body_start_0|>
queryset = self.queryset.filter(type='student').order_by('-created_at')[0]
serializer = self.get_serializer(queryset, many=False)
return Response(serializer.data)
<|end_body_0|>
<|body_start_1|>
try:
cache_key = 'apk_release_student'
data = cache... | ApkReleaseViewSet | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ApkReleaseViewSet:
def list(self, request, *args, **kwargs):
"""this function expire in new version of server :param request: :return: application address"""
<|body_0|>
def student(self, request):
"""last app from student :param request: :return: application address"... | stack_v2_sparse_classes_75kplus_train_065921 | 8,937 | no_license | [
{
"docstring": "this function expire in new version of server :param request: :return: application address",
"name": "list",
"signature": "def list(self, request, *args, **kwargs)"
},
{
"docstring": "last app from student :param request: :return: application address",
"name": "student",
... | 4 | stack_v2_sparse_classes_30k_train_054270 | Implement the Python class `ApkReleaseViewSet` described below.
Class description:
Implement the ApkReleaseViewSet class.
Method signatures and docstrings:
- def list(self, request, *args, **kwargs): this function expire in new version of server :param request: :return: application address
- def student(self, request... | Implement the Python class `ApkReleaseViewSet` described below.
Class description:
Implement the ApkReleaseViewSet class.
Method signatures and docstrings:
- def list(self, request, *args, **kwargs): this function expire in new version of server :param request: :return: application address
- def student(self, request... | a95addbca0b25b99da920721c078f1c01466f09b | <|skeleton|>
class ApkReleaseViewSet:
def list(self, request, *args, **kwargs):
"""this function expire in new version of server :param request: :return: application address"""
<|body_0|>
def student(self, request):
"""last app from student :param request: :return: application address"... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class ApkReleaseViewSet:
def list(self, request, *args, **kwargs):
"""this function expire in new version of server :param request: :return: application address"""
queryset = self.queryset.filter(type='student').order_by('-created_at')[0]
serializer = self.get_serializer(queryset, many=False... | the_stack_v2_python_sparse | apps/common/viewsets.py | mehdi1361/backend_aghigh | train | 0 | |
fb42951a51294cfff88ca441eb07fc9529dc8ddd | [
"super(RpcPikaIncomingMessage, self).__init__(pika_engine, channel, method, properties, body)\nself.reply_q = properties.reply_to\nself.msg_id = properties.correlation_id",
"if self.reply_q is None:\n return\nreply_outgoing_message = RpcReplyPikaOutgoingMessage(self._pika_engine, self.msg_id, reply=reply, fail... | <|body_start_0|>
super(RpcPikaIncomingMessage, self).__init__(pika_engine, channel, method, properties, body)
self.reply_q = properties.reply_to
self.msg_id = properties.correlation_id
<|end_body_0|>
<|body_start_1|>
if self.reply_q is None:
return
reply_outgoing_mes... | PikaIncomingMessage implementation for RPC messages. It expects extra RPC related fields in message body (msg_id and reply_q). Also 'reply' method added to allow consumer to send RPC reply back to the RPC client | RpcPikaIncomingMessage | [
"Python-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RpcPikaIncomingMessage:
"""PikaIncomingMessage implementation for RPC messages. It expects extra RPC related fields in message body (msg_id and reply_q). Also 'reply' method added to allow consumer to send RPC reply back to the RPC client"""
def __init__(self, pika_engine, channel, method, p... | stack_v2_sparse_classes_75kplus_train_065922 | 24,684 | permissive | [
{
"docstring": "Defines default values of msg_id and reply_q fields and just call super.__init__ method :param pika_engine: PikaEngine, shared object with configuration and shared driver functionality :param channel: Channel, RabbitMQ channel which was used for this message delivery, used for sending ack back. ... | 2 | stack_v2_sparse_classes_30k_test_001962 | Implement the Python class `RpcPikaIncomingMessage` described below.
Class description:
PikaIncomingMessage implementation for RPC messages. It expects extra RPC related fields in message body (msg_id and reply_q). Also 'reply' method added to allow consumer to send RPC reply back to the RPC client
Method signatures ... | Implement the Python class `RpcPikaIncomingMessage` described below.
Class description:
PikaIncomingMessage implementation for RPC messages. It expects extra RPC related fields in message body (msg_id and reply_q). Also 'reply' method added to allow consumer to send RPC reply back to the RPC client
Method signatures ... | c01951b33e278de9e769c2d0609c0be61d2cb26b | <|skeleton|>
class RpcPikaIncomingMessage:
"""PikaIncomingMessage implementation for RPC messages. It expects extra RPC related fields in message body (msg_id and reply_q). Also 'reply' method added to allow consumer to send RPC reply back to the RPC client"""
def __init__(self, pika_engine, channel, method, p... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class RpcPikaIncomingMessage:
"""PikaIncomingMessage implementation for RPC messages. It expects extra RPC related fields in message body (msg_id and reply_q). Also 'reply' method added to allow consumer to send RPC reply back to the RPC client"""
def __init__(self, pika_engine, channel, method, properties, bo... | the_stack_v2_python_sparse | filesystems/vnx_rootfs_lxc_ubuntu64-16.04-v025-openstack-compute/rootfs/usr/lib/python2.7/dist-packages/oslo_messaging/_drivers/pika_driver/pika_message.py | juancarlosdiaztorres/Ansible-OpenStack | train | 0 |
e4d6990132cbd4287e0b83e4172ace5f00169e8b | [
"message = \"Processing type order: order ID is '%(order)s' and request ID is '%(request)s'\"\nLOG.info(message, {'order': order_id, 'request': request_id})\nreturn resources.BeginTypeOrder().process_and_suppress_exceptions(order_id, project_id)",
"message = \"Processing check certificate status on order: order I... | <|body_start_0|>
message = "Processing type order: order ID is '%(order)s' and request ID is '%(request)s'"
LOG.info(message, {'order': order_id, 'request': request_id})
return resources.BeginTypeOrder().process_and_suppress_exceptions(order_id, project_id)
<|end_body_0|>
<|body_start_1|>
... | Tasks that can be invoked asynchronously in Barbican. Only place task methods and implementations on this class, as they can be called directly from the client side for non-asynchronous standalone single-node operation. If a new method is added that can be retried, please also add its method name to MAP_RETRY_TASKS abo... | Tasks | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Tasks:
"""Tasks that can be invoked asynchronously in Barbican. Only place task methods and implementations on this class, as they can be called directly from the client side for non-asynchronous standalone single-node operation. If a new method is added that can be retried, please also add its m... | stack_v2_sparse_classes_75kplus_train_065923 | 9,386 | permissive | [
{
"docstring": "Process TypeOrder.",
"name": "process_type_order",
"signature": "def process_type_order(self, context, order_id, project_id, request_id)"
},
{
"docstring": "Check the status of a certificate order.",
"name": "check_certificate_status",
"signature": "def check_certificate_... | 2 | null | Implement the Python class `Tasks` described below.
Class description:
Tasks that can be invoked asynchronously in Barbican. Only place task methods and implementations on this class, as they can be called directly from the client side for non-asynchronous standalone single-node operation. If a new method is added tha... | Implement the Python class `Tasks` described below.
Class description:
Tasks that can be invoked asynchronously in Barbican. Only place task methods and implementations on this class, as they can be called directly from the client side for non-asynchronous standalone single-node operation. If a new method is added tha... | c8e3dc14e6225f1d400131434e8afec0aa410ae7 | <|skeleton|>
class Tasks:
"""Tasks that can be invoked asynchronously in Barbican. Only place task methods and implementations on this class, as they can be called directly from the client side for non-asynchronous standalone single-node operation. If a new method is added that can be retried, please also add its m... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Tasks:
"""Tasks that can be invoked asynchronously in Barbican. Only place task methods and implementations on this class, as they can be called directly from the client side for non-asynchronous standalone single-node operation. If a new method is added that can be retried, please also add its method name to... | the_stack_v2_python_sparse | barbican/queue/server.py | openstack/barbican | train | 189 |
9078ecd85d4393f632fe55c3fc786ad38016c5e6 | [
"assert alpha >= 0\nsuper(PrioritizedReplayBuffer, self).__init__(obs_dim, size, batch_size, n_step, gamma)\nself.max_priority, self.tree_ptr = (1.0, 0)\nself.alpha = alpha\ntree_capacity = 1\nwhile tree_capacity < self.max_size:\n tree_capacity *= 2\nself.sum_tree = SumSegmentTree(tree_capacity)\nself.min_tree ... | <|body_start_0|>
assert alpha >= 0
super(PrioritizedReplayBuffer, self).__init__(obs_dim, size, batch_size, n_step, gamma)
self.max_priority, self.tree_ptr = (1.0, 0)
self.alpha = alpha
tree_capacity = 1
while tree_capacity < self.max_size:
tree_capacity *= 2
... | Prioritized Replay buffer. Attributes: max_priority (float): max priority tree_ptr (int): next index of tree alpha (float): alpha parameter for prioritized replay buffer sum_tree (SumSegmentTree): sum tree for prior min_tree (MinSegmentTree): min tree for min prior to get max weight | PrioritizedReplayBuffer | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PrioritizedReplayBuffer:
"""Prioritized Replay buffer. Attributes: max_priority (float): max priority tree_ptr (int): next index of tree alpha (float): alpha parameter for prioritized replay buffer sum_tree (SumSegmentTree): sum tree for prior min_tree (MinSegmentTree): min tree for min prior to ... | stack_v2_sparse_classes_75kplus_train_065924 | 22,704 | no_license | [
{
"docstring": "Initialization.",
"name": "__init__",
"signature": "def __init__(self, obs_dim: int, size: int, batch_size: int=32, alpha: float=0.6, n_step: int=1, gamma: float=0.99)"
},
{
"docstring": "Store experience and priority.",
"name": "store",
"signature": "def store(self, obs:... | 6 | stack_v2_sparse_classes_30k_train_016350 | Implement the Python class `PrioritizedReplayBuffer` described below.
Class description:
Prioritized Replay buffer. Attributes: max_priority (float): max priority tree_ptr (int): next index of tree alpha (float): alpha parameter for prioritized replay buffer sum_tree (SumSegmentTree): sum tree for prior min_tree (MinS... | Implement the Python class `PrioritizedReplayBuffer` described below.
Class description:
Prioritized Replay buffer. Attributes: max_priority (float): max priority tree_ptr (int): next index of tree alpha (float): alpha parameter for prioritized replay buffer sum_tree (SumSegmentTree): sum tree for prior min_tree (MinS... | 99472f5f6b37bc421e31b145ace55bb175c5b2c4 | <|skeleton|>
class PrioritizedReplayBuffer:
"""Prioritized Replay buffer. Attributes: max_priority (float): max priority tree_ptr (int): next index of tree alpha (float): alpha parameter for prioritized replay buffer sum_tree (SumSegmentTree): sum tree for prior min_tree (MinSegmentTree): min tree for min prior to ... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class PrioritizedReplayBuffer:
"""Prioritized Replay buffer. Attributes: max_priority (float): max priority tree_ptr (int): next index of tree alpha (float): alpha parameter for prioritized replay buffer sum_tree (SumSegmentTree): sum tree for prior min_tree (MinSegmentTree): min tree for min prior to get max weigh... | the_stack_v2_python_sparse | agent/torch_agent/agent_Rainbow.py | ZJ96/City_Brain_TSC | train | 4 |
44461232e4b1f68e1c5904a454ae345dfa3f01b9 | [
"if type(dct) is str:\n return evalfn(dct)\nfor k, v in dct.items():\n dct[k] = self.evalfunc(v, evalfn)\nreturn dct",
"rs = {}\nfor par in parameter_str_list:\n cd = {}\n for name, comp in complist.items():\n comp = comp.replace('math.exp', 'exp')\n sd = sympy.diff(comp, par)\n s... | <|body_start_0|>
if type(dct) is str:
return evalfn(dct)
for k, v in dct.items():
dct[k] = self.evalfunc(v, evalfn)
return dct
<|end_body_0|>
<|body_start_1|>
rs = {}
for par in parameter_str_list:
cd = {}
for name, comp in complis... | StringEMTransistorModel | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class StringEMTransistorModel:
def evalfunc(self, dct, evalfn):
"""Рекурсивное вычисление словаря любой вложенности :param dct: строка или словарь, может быть словарь словарей и так далее :param evalfn: функция, которая принимает на вход строку, возвращает вычисленное значение :return: тот же ... | stack_v2_sparse_classes_75kplus_train_065925 | 6,653 | no_license | [
{
"docstring": "Рекурсивное вычисление словаря любой вложенности :param dct: строка или словарь, может быть словарь словарей и так далее :param evalfn: функция, которая принимает на вход строку, возвращает вычисленное значение :return: тот же словарь, но вместо строк нечто вычисленное",
"name": "evalfunc",
... | 5 | null | Implement the Python class `StringEMTransistorModel` described below.
Class description:
Implement the StringEMTransistorModel class.
Method signatures and docstrings:
- def evalfunc(self, dct, evalfn): Рекурсивное вычисление словаря любой вложенности :param dct: строка или словарь, может быть словарь словарей и так ... | Implement the Python class `StringEMTransistorModel` described below.
Class description:
Implement the StringEMTransistorModel class.
Method signatures and docstrings:
- def evalfunc(self, dct, evalfn): Рекурсивное вычисление словаря любой вложенности :param dct: строка или словарь, может быть словарь словарей и так ... | b071d3084cdf2d9f18b3981c9884ec7310840e79 | <|skeleton|>
class StringEMTransistorModel:
def evalfunc(self, dct, evalfn):
"""Рекурсивное вычисление словаря любой вложенности :param dct: строка или словарь, может быть словарь словарей и так далее :param evalfn: функция, которая принимает на вход строку, возвращает вычисленное значение :return: тот же ... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class StringEMTransistorModel:
def evalfunc(self, dct, evalfn):
"""Рекурсивное вычисление словаря любой вложенности :param dct: строка или словарь, может быть словарь словарей и так далее :param evalfn: функция, которая принимает на вход строку, возвращает вычисленное значение :return: тот же словарь, но вм... | the_stack_v2_python_sparse | Fianora/Fianora_Derivative.py | reinerwaldmann/PHDLinearization | train | 0 | |
7ab37ab224045abd7cee10ff3e0815e662abc1af | [
"self.frame = frame\nsuper().__init__(self.frame)\nself.give_shape()\nself.brick_matrix = self.make_brick_matrix(self.frame)",
"for r in range(len(self.location_n_type_matrix)):\n for c in range(len(self.location_n_type_matrix[r])):\n if r == len(self.location_n_type_matrix) - 1:\n self.locat... | <|body_start_0|>
self.frame = frame
super().__init__(self.frame)
self.give_shape()
self.brick_matrix = self.make_brick_matrix(self.frame)
<|end_body_0|>
<|body_start_1|>
for r in range(len(self.location_n_type_matrix)):
for c in range(len(self.location_n_type_matrix[... | BrickLayout for stage | LayoutStage1 | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LayoutStage1:
"""BrickLayout for stage"""
def __init__(self, frame: Frame):
"""constructor for this class"""
<|body_0|>
def give_shape(self):
"""This function is from the parrent class. Now overriding it to give"""
<|body_1|>
<|end_skeleton|>
<|body_sta... | stack_v2_sparse_classes_75kplus_train_065926 | 8,421 | no_license | [
{
"docstring": "constructor for this class",
"name": "__init__",
"signature": "def __init__(self, frame: Frame)"
},
{
"docstring": "This function is from the parrent class. Now overriding it to give",
"name": "give_shape",
"signature": "def give_shape(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_040234 | Implement the Python class `LayoutStage1` described below.
Class description:
BrickLayout for stage
Method signatures and docstrings:
- def __init__(self, frame: Frame): constructor for this class
- def give_shape(self): This function is from the parrent class. Now overriding it to give | Implement the Python class `LayoutStage1` described below.
Class description:
BrickLayout for stage
Method signatures and docstrings:
- def __init__(self, frame: Frame): constructor for this class
- def give_shape(self): This function is from the parrent class. Now overriding it to give
<|skeleton|>
class LayoutStag... | c4cd10f631aba51d290395dec446850a0fbfe1b5 | <|skeleton|>
class LayoutStage1:
"""BrickLayout for stage"""
def __init__(self, frame: Frame):
"""constructor for this class"""
<|body_0|>
def give_shape(self):
"""This function is from the parrent class. Now overriding it to give"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class LayoutStage1:
"""BrickLayout for stage"""
def __init__(self, frame: Frame):
"""constructor for this class"""
self.frame = frame
super().__init__(self.frame)
self.give_shape()
self.brick_matrix = self.make_brick_matrix(self.frame)
def give_shape(self):
... | the_stack_v2_python_sparse | v1/brick_layout.py | ayushsharma-crypto/Brick-Breaker-Terminal-Based-Game | train | 0 |
8df2789715d75225492abf20fdc7ff074be9aab4 | [
"super(GaussianProcessModel, self).__init__(**kwargs)\nself.regression = regression\nself.gp_implementation = gp_implementation\nself.model = None\nself.num_tasks = num_tasks\nself.tasks = self.num_tasks",
"self.model = []\ntrain_labels = train_dataset.get_labels()\nval_labels = val_dataset.get_labels()\nX = self... | <|body_start_0|>
super(GaussianProcessModel, self).__init__(**kwargs)
self.regression = regression
self.gp_implementation = gp_implementation
self.model = None
self.num_tasks = num_tasks
self.tasks = self.num_tasks
<|end_body_0|>
<|body_start_1|>
self.model = []
... | GaussianProcessModel | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GaussianProcessModel:
def __init__(self, regression: bool, gp_implementation: str, num_tasks: int=1, **kwargs):
"""__init__. Args: gp_implementation (str): Which package to use to build these num_tasks (int) : Number of tasks kwargs: kwargs"""
<|body_0|>
def train(self, trai... | stack_v2_sparse_classes_75kplus_train_065927 | 37,814 | no_license | [
{
"docstring": "__init__. Args: gp_implementation (str): Which package to use to build these num_tasks (int) : Number of tasks kwargs: kwargs",
"name": "__init__",
"signature": "def __init__(self, regression: bool, gp_implementation: str, num_tasks: int=1, **kwargs)"
},
{
"docstring": "fit. Fit ... | 3 | null | Implement the Python class `GaussianProcessModel` described below.
Class description:
Implement the GaussianProcessModel class.
Method signatures and docstrings:
- def __init__(self, regression: bool, gp_implementation: str, num_tasks: int=1, **kwargs): __init__. Args: gp_implementation (str): Which package to use to... | Implement the Python class `GaussianProcessModel` described below.
Class description:
Implement the GaussianProcessModel class.
Method signatures and docstrings:
- def __init__(self, regression: bool, gp_implementation: str, num_tasks: int=1, **kwargs): __init__. Args: gp_implementation (str): Which package to use to... | 84c9026c78bec9a2267960a87080b71beba5c305 | <|skeleton|>
class GaussianProcessModel:
def __init__(self, regression: bool, gp_implementation: str, num_tasks: int=1, **kwargs):
"""__init__. Args: gp_implementation (str): Which package to use to build these num_tasks (int) : Number of tasks kwargs: kwargs"""
<|body_0|>
def train(self, trai... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class GaussianProcessModel:
def __init__(self, regression: bool, gp_implementation: str, num_tasks: int=1, **kwargs):
"""__init__. Args: gp_implementation (str): Which package to use to build these num_tasks (int) : Number of tasks kwargs: kwargs"""
super(GaussianProcessModel, self).__init__(**kwarg... | the_stack_v2_python_sparse | enzpred/models/dense_models.py | liudongliangHI/enz-pred | train | 0 | |
f1620989ed61be9a21630c9afabc0867551b62e2 | [
"self.reporting = reporting\nself.github = GitHubService(ghName, ghToken)\nself.artifacts = ArtifactService()\nself.monitoring = MonitoringService()\nself.idle = True\nself.started = 0",
"s = os.statvfs(config.prbuildsRoot)\nfreeSpaceMB = s.f_frsize * s.f_bavail / 1000 / 1000\nhealth = {'has free space': freeSpac... | <|body_start_0|>
self.reporting = reporting
self.github = GitHubService(ghName, ghToken)
self.artifacts = ArtifactService()
self.monitoring = MonitoringService()
self.idle = True
self.started = 0
<|end_body_0|>
<|body_start_1|>
s = os.statvfs(config.prbuildsRoot)... | Trousers | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Trousers:
def __init__(self, reporting, ghName, ghToken):
"""constructor"""
<|body_0|>
def is_healthy(self):
"""we are healthy under these conditions"""
<|body_1|>
def process(self, action, bucket, metricService):
"""process a message coming off ... | stack_v2_sparse_classes_75kplus_train_065928 | 3,641 | no_license | [
{
"docstring": "constructor",
"name": "__init__",
"signature": "def __init__(self, reporting, ghName, ghToken)"
},
{
"docstring": "we are healthy under these conditions",
"name": "is_healthy",
"signature": "def is_healthy(self)"
},
{
"docstring": "process a message coming off the... | 4 | stack_v2_sparse_classes_30k_train_002122 | Implement the Python class `Trousers` described below.
Class description:
Implement the Trousers class.
Method signatures and docstrings:
- def __init__(self, reporting, ghName, ghToken): constructor
- def is_healthy(self): we are healthy under these conditions
- def process(self, action, bucket, metricService): proc... | Implement the Python class `Trousers` described below.
Class description:
Implement the Trousers class.
Method signatures and docstrings:
- def __init__(self, reporting, ghName, ghToken): constructor
- def is_healthy(self): we are healthy under these conditions
- def process(self, action, bucket, metricService): proc... | 1dd8f0959fec8a3bb5e06ee0c4acdd43c509765b | <|skeleton|>
class Trousers:
def __init__(self, reporting, ghName, ghToken):
"""constructor"""
<|body_0|>
def is_healthy(self):
"""we are healthy under these conditions"""
<|body_1|>
def process(self, action, bucket, metricService):
"""process a message coming off ... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Trousers:
def __init__(self, reporting, ghName, ghToken):
"""constructor"""
self.reporting = reporting
self.github = GitHubService(ghName, ghToken)
self.artifacts = ArtifactService()
self.monitoring = MonitoringService()
self.idle = True
self.started = 0... | the_stack_v2_python_sparse | trousers/trouserlib/trousers.py | guardian/prbuilds | train | 6 | |
15f39eab02c2098df36033b90b43c0ea373a7cd1 | [
"n = len(prices)\ndp = [[0, -prices[0]]] + [[0, 0] for _ in range(n - 1)]\nfor i in range(1, n):\n dp[i][0] = max(dp[i - 1][0], dp[i - 1][1] + prices[i] - fee)\n dp[i][1] = max(dp[i - 1][1], dp[i - 1][0] - prices[i])\nreturn dp[-1][0]",
"if not prices:\n return 0\nn = len(prices)\ndp0 = 0\ndp1 = -prices[... | <|body_start_0|>
n = len(prices)
dp = [[0, -prices[0]]] + [[0, 0] for _ in range(n - 1)]
for i in range(1, n):
dp[i][0] = max(dp[i - 1][0], dp[i - 1][1] + prices[i] - fee)
dp[i][1] = max(dp[i - 1][1], dp[i - 1][0] - prices[i])
return dp[-1][0]
<|end_body_0|>
<|bo... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def maxProfit(self, prices, fee):
""":type prices: List[int] :rtype: int"""
<|body_0|>
def maxProfit(self, prices, fee):
""":type prices: List[int] :type fee: int :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
n = len(prices)
... | stack_v2_sparse_classes_75kplus_train_065929 | 1,003 | no_license | [
{
"docstring": ":type prices: List[int] :rtype: int",
"name": "maxProfit",
"signature": "def maxProfit(self, prices, fee)"
},
{
"docstring": ":type prices: List[int] :type fee: int :rtype: int",
"name": "maxProfit",
"signature": "def maxProfit(self, prices, fee)"
}
] | 2 | stack_v2_sparse_classes_30k_train_047683 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def maxProfit(self, prices, fee): :type prices: List[int] :rtype: int
- def maxProfit(self, prices, fee): :type prices: List[int] :type fee: int :rtype: int | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def maxProfit(self, prices, fee): :type prices: List[int] :rtype: int
- def maxProfit(self, prices, fee): :type prices: List[int] :type fee: int :rtype: int
<|skeleton|>
class S... | a509b383a42f54313970168d9faa11f088f18708 | <|skeleton|>
class Solution:
def maxProfit(self, prices, fee):
""":type prices: List[int] :rtype: int"""
<|body_0|>
def maxProfit(self, prices, fee):
""":type prices: List[int] :type fee: int :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Solution:
def maxProfit(self, prices, fee):
""":type prices: List[int] :rtype: int"""
n = len(prices)
dp = [[0, -prices[0]]] + [[0, 0] for _ in range(n - 1)]
for i in range(1, n):
dp[i][0] = max(dp[i - 1][0], dp[i - 1][1] + prices[i] - fee)
dp[i][1] = ma... | the_stack_v2_python_sparse | 0714_Best_Time_to_Buy_and_Sell_Stock_with_Transaction_Fee.py | bingli8802/leetcode | train | 0 | |
9695c6a5f271f73512b1e1afca367850975c6348 | [
"super().__init__(args, n_r, n_e, dataset_name)\nself.name = 'GCVAE'\nn = self.n\ninput_dim = n * n + n * n_e + n * n * n_r\nself.input_dim = input_dim\nn_feat = n_e + n * n_r\nself.encoder = GCN(n, n_feat, self.h_dim, 2 * self.z_dim).to(torch.double)\nself.decoder = RMLP(input_dim, self.h_dim, self.z_dim)",
"A, ... | <|body_start_0|>
super().__init__(args, n_r, n_e, dataset_name)
self.name = 'GCVAE'
n = self.n
input_dim = n * n + n * n_e + n * n * n_r
self.input_dim = input_dim
n_feat = n_e + n * n_r
self.encoder = GCN(n, n_feat, self.h_dim, 2 * self.z_dim).to(torch.double)
... | GCVAE | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GCVAE:
def __init__(self, args, n_r: int, n_e: int, dataset_name: str):
"""Graph Variational Auto Encoder :param n : Number of nodes :param n_e : Number of node attributes :param n_r : Number of edge attributes :param dataset_name : name of the dataset which the model will train on. :par... | stack_v2_sparse_classes_75kplus_train_065930 | 2,171 | permissive | [
{
"docstring": "Graph Variational Auto Encoder :param n : Number of nodes :param n_e : Number of node attributes :param n_r : Number of edge attributes :param dataset_name : name of the dataset which the model will train on. :param h_dim : Hidden dimension :param z_dim : latent dimension :param beta: for beta <... | 2 | stack_v2_sparse_classes_30k_train_048530 | Implement the Python class `GCVAE` described below.
Class description:
Implement the GCVAE class.
Method signatures and docstrings:
- def __init__(self, args, n_r: int, n_e: int, dataset_name: str): Graph Variational Auto Encoder :param n : Number of nodes :param n_e : Number of node attributes :param n_r : Number of... | Implement the Python class `GCVAE` described below.
Class description:
Implement the GCVAE class.
Method signatures and docstrings:
- def __init__(self, args, n_r: int, n_e: int, dataset_name: str): Graph Variational Auto Encoder :param n : Number of nodes :param n_e : Number of node attributes :param n_r : Number of... | 6e2554e79a71a7a8e5833c554fa9aee9a5309bef | <|skeleton|>
class GCVAE:
def __init__(self, args, n_r: int, n_e: int, dataset_name: str):
"""Graph Variational Auto Encoder :param n : Number of nodes :param n_e : Number of node attributes :param n_r : Number of edge attributes :param dataset_name : name of the dataset which the model will train on. :par... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class GCVAE:
def __init__(self, args, n_r: int, n_e: int, dataset_name: str):
"""Graph Variational Auto Encoder :param n : Number of nodes :param n_e : Number of node attributes :param n_r : Number of edge attributes :param dataset_name : name of the dataset which the model will train on. :param h_dim : Hid... | the_stack_v2_python_sparse | torch_rgvae/GCVAE.py | xixiareone/rgvae | train | 0 | |
c6c1ff53c73e6fbbb92f57162d5e0a99189a4ac6 | [
"user = get_jwt_identity()\ndata = request.get_json()\nbooking = {'flight_name': data.get('flight_name'), 'seat_number': data.get('seat_number'), 'payment': data.get('payment')}\ntry:\n cleaned_data = validate_data(**booking)\nexcept AssertionError as error:\n return (jsonify({'error': error.args[0]}), 409)\n... | <|body_start_0|>
user = get_jwt_identity()
data = request.get_json()
booking = {'flight_name': data.get('flight_name'), 'seat_number': data.get('seat_number'), 'payment': data.get('payment')}
try:
cleaned_data = validate_data(**booking)
except AssertionError as error:... | Controls all bookings and those of each flight | BookingController | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BookingController:
"""Controls all bookings and those of each flight"""
def create():
"""creat booking objects for logged in users"""
<|body_0|>
def bookings():
"""Returns all the bookings of a specific user"""
<|body_1|>
def flight_bookings(flight_i... | stack_v2_sparse_classes_75kplus_train_065931 | 2,523 | no_license | [
{
"docstring": "creat booking objects for logged in users",
"name": "create",
"signature": "def create()"
},
{
"docstring": "Returns all the bookings of a specific user",
"name": "bookings",
"signature": "def bookings()"
},
{
"docstring": "\"Returns all the flight bookings",
... | 3 | stack_v2_sparse_classes_30k_train_001883 | Implement the Python class `BookingController` described below.
Class description:
Controls all bookings and those of each flight
Method signatures and docstrings:
- def create(): creat booking objects for logged in users
- def bookings(): Returns all the bookings of a specific user
- def flight_bookings(flight_id): ... | Implement the Python class `BookingController` described below.
Class description:
Controls all bookings and those of each flight
Method signatures and docstrings:
- def create(): creat booking objects for logged in users
- def bookings(): Returns all the bookings of a specific user
- def flight_bookings(flight_id): ... | f4174888ebaf0902d842894bb48f51b9ec0c3e7e | <|skeleton|>
class BookingController:
"""Controls all bookings and those of each flight"""
def create():
"""creat booking objects for logged in users"""
<|body_0|>
def bookings():
"""Returns all the bookings of a specific user"""
<|body_1|>
def flight_bookings(flight_i... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class BookingController:
"""Controls all bookings and those of each flight"""
def create():
"""creat booking objects for logged in users"""
user = get_jwt_identity()
data = request.get_json()
booking = {'flight_name': data.get('flight_name'), 'seat_number': data.get('seat_number... | the_stack_v2_python_sparse | flights/controller/booking_controller.py | Bonifase/flight-reservation-api | train | 0 |
7576348745a1722575e4c4a2164b05aeca070a5f | [
"s1, s2 = ('', '')\nwhile l1:\n s1 = s1 + str(l1.val)\n l1 = l1.next\nwhile l2:\n s2 = s2 + str(l2.val)\n l2 = l2.next\nnum = int(s1[::-1]) + int(s2[::-1])\nnum = str(num)[::-1]\npivot = head = ListNode(num[0])\nfor x in num[1:]:\n head.next = ListNode(int(x))\n head = head.next\nreturn pivot",
... | <|body_start_0|>
s1, s2 = ('', '')
while l1:
s1 = s1 + str(l1.val)
l1 = l1.next
while l2:
s2 = s2 + str(l2.val)
l2 = l2.next
num = int(s1[::-1]) + int(s2[::-1])
num = str(num)[::-1]
pivot = head = ListNode(num[0])
fo... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def addTwoNumbers1(self, l1: ListNode, l2: ListNode) -> ListNode:
"""先遍历生成int,求值后再生成链表。"""
<|body_0|>
def addTwoNumbers2(self, l1: ListNode, l2: ListNode) -> ListNode:
"""内置函数divmod() x,y = divmod(m,n) x = m//n y = m%n"""
<|body_1|>
<|end_skeleton|... | stack_v2_sparse_classes_75kplus_train_065932 | 2,266 | no_license | [
{
"docstring": "先遍历生成int,求值后再生成链表。",
"name": "addTwoNumbers1",
"signature": "def addTwoNumbers1(self, l1: ListNode, l2: ListNode) -> ListNode"
},
{
"docstring": "内置函数divmod() x,y = divmod(m,n) x = m//n y = m%n",
"name": "addTwoNumbers2",
"signature": "def addTwoNumbers2(self, l1: ListNod... | 2 | stack_v2_sparse_classes_30k_train_025055 | 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: 先遍历生成int,求值后再生成链表。
- def addTwoNumbers2(self, l1: ListNode, l2: ListNode) -> ListNode: 内置函数divmod() x,y = divmod... | 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: 先遍历生成int,求值后再生成链表。
- def addTwoNumbers2(self, l1: ListNode, l2: ListNode) -> ListNode: 内置函数divmod() x,y = divmod... | 2bbb1640589aab34f2bc42489283033cc11fb885 | <|skeleton|>
class Solution:
def addTwoNumbers1(self, l1: ListNode, l2: ListNode) -> ListNode:
"""先遍历生成int,求值后再生成链表。"""
<|body_0|>
def addTwoNumbers2(self, l1: ListNode, l2: ListNode) -> ListNode:
"""内置函数divmod() x,y = divmod(m,n) x = m//n y = m%n"""
<|body_1|>
<|end_skeleton|... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Solution:
def addTwoNumbers1(self, l1: ListNode, l2: ListNode) -> ListNode:
"""先遍历生成int,求值后再生成链表。"""
s1, s2 = ('', '')
while l1:
s1 = s1 + str(l1.val)
l1 = l1.next
while l2:
s2 = s2 + str(l2.val)
l2 = l2.next
num = int(s1[... | the_stack_v2_python_sparse | 002_add-two-numbers.py | helloocc/algorithm | train | 1 | |
ad55e8ff6b27e12e479707a8c309b14ef1bc907a | [
"query = Query(Objective.collection, service_id=client.service_id, objective_group_id=objective_group_id)\nquery.limit(1000)\nreturn SequenceProxy(Objective, query, client=client)",
"collection: Final[str] = 'objective_set_to_objective'\nquery = Query(collection, service_id=client.service_id, objective_set_id=obj... | <|body_start_0|>
query = Query(Objective.collection, service_id=client.service_id, objective_group_id=objective_group_id)
query.limit(1000)
return SequenceProxy(Objective, query, client=client)
<|end_body_0|>
<|body_start_1|>
collection: Final[str] = 'objective_set_to_objective'
... | A objective presented to a character. .. attribute:: id :type: int The unique ID of this objective. In the API payload, this field is called ``objective_id``. .. attribute:: objective_type_id :type: int The associated :class:`ObjectiveType` for this objective. .. seealso:: :meth:`type` -- The type of objective. .. attr... | Objective | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Objective:
"""A objective presented to a character. .. attribute:: id :type: int The unique ID of this objective. In the API payload, this field is called ``objective_id``. .. attribute:: objective_type_id :type: int The associated :class:`ObjectiveType` for this objective. .. seealso:: :meth:`ty... | stack_v2_sparse_classes_75kplus_train_065933 | 4,410 | permissive | [
{
"docstring": "Return any rewards contained from the given reward group. This returns a :class:`auraxium.SequenceProxy`.",
"name": "get_by_objective_group",
"signature": "def get_by_objective_group(cls, objective_group_id: int, client: RequestClient) -> SequenceProxy['Objective']"
},
{
"docstri... | 3 | null | Implement the Python class `Objective` described below.
Class description:
A objective presented to a character. .. attribute:: id :type: int The unique ID of this objective. In the API payload, this field is called ``objective_id``. .. attribute:: objective_type_id :type: int The associated :class:`ObjectiveType` for... | Implement the Python class `Objective` described below.
Class description:
A objective presented to a character. .. attribute:: id :type: int The unique ID of this objective. In the API payload, this field is called ``objective_id``. .. attribute:: objective_type_id :type: int The associated :class:`ObjectiveType` for... | 23dcf927a199c8d7c917d89fe96b470a34cf4bba | <|skeleton|>
class Objective:
"""A objective presented to a character. .. attribute:: id :type: int The unique ID of this objective. In the API payload, this field is called ``objective_id``. .. attribute:: objective_type_id :type: int The associated :class:`ObjectiveType` for this objective. .. seealso:: :meth:`ty... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Objective:
"""A objective presented to a character. .. attribute:: id :type: int The unique ID of this objective. In the API payload, this field is called ``objective_id``. .. attribute:: objective_type_id :type: int The associated :class:`ObjectiveType` for this objective. .. seealso:: :meth:`type` -- The ty... | the_stack_v2_python_sparse | auraxium/ps2/_objective.py | leonhard-s/auraxium | train | 29 |
0d53f5b928af3c170c7644347445719b9fc01845 | [
"logger.info('Creating ReferenceCell...')\nsuper(ReferenceCell, self).__init__(mesh, pseudo_mesh, time_mesh, boundaries, **kwargs)\nlogger.info('Finding subdomains and indices...')\nself.neg_ind, self.sep_ind, self.pos_ind = subdomains(mesh[0:], [(0, 1), (1, 2), (2, 3)])\nself.neg, self.sep, self.pos = (mesh[self.n... | <|body_start_0|>
logger.info('Creating ReferenceCell...')
super(ReferenceCell, self).__init__(mesh, pseudo_mesh, time_mesh, boundaries, **kwargs)
logger.info('Finding subdomains and indices...')
self.neg_ind, self.sep_ind, self.pos_ind = subdomains(mesh[0:], [(0, 1), (1, 2), (2, 3)])
... | Reference lithium-ion cell geometry, where the dimensions are normalized. The x dimension is defined such that the negative electrode exists between [0, 1], the separator exists between [1, 2], and the positive electrode exists between [2, 3]. For convenience the subdomains are added onto engine.Mountain. | ReferenceCell | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ReferenceCell:
"""Reference lithium-ion cell geometry, where the dimensions are normalized. The x dimension is defined such that the negative electrode exists between [0, 1], the separator exists between [1, 2], and the positive electrode exists between [2, 3]. For convenience the subdomains are ... | stack_v2_sparse_classes_75kplus_train_065934 | 3,719 | permissive | [
{
"docstring": "Store the solutions to each cell parameter. :param mesh: Solution mesh :param boundaries: internal boundaries in the mesh :param kwargs: arrays for each solution",
"name": "__init__",
"signature": "def __init__(self, mesh: np.ndarray, pseudo_mesh: np.ndarray, time_mesh: np.ndarray, bound... | 2 | stack_v2_sparse_classes_30k_train_045624 | Implement the Python class `ReferenceCell` described below.
Class description:
Reference lithium-ion cell geometry, where the dimensions are normalized. The x dimension is defined such that the negative electrode exists between [0, 1], the separator exists between [1, 2], and the positive electrode exists between [2, ... | Implement the Python class `ReferenceCell` described below.
Class description:
Reference lithium-ion cell geometry, where the dimensions are normalized. The x dimension is defined such that the negative electrode exists between [0, 1], the separator exists between [1, 2], and the positive electrode exists between [2, ... | ba2e93faeed3004d344a8c14f37a409da572271d | <|skeleton|>
class ReferenceCell:
"""Reference lithium-ion cell geometry, where the dimensions are normalized. The x dimension is defined such that the negative electrode exists between [0, 1], the separator exists between [1, 2], and the positive electrode exists between [2, 3]. For convenience the subdomains are ... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class ReferenceCell:
"""Reference lithium-ion cell geometry, where the dimensions are normalized. The x dimension is defined such that the negative electrode exists between [0, 1], the separator exists between [1, 2], and the positive electrode exists between [2, 3]. For convenience the subdomains are added onto en... | the_stack_v2_python_sparse | mtnlion/domain.py | macklenc/mtnlion | train | 0 |
3dff085a46a6fb3665531c22e042021827665b15 | [
"super(VideoEncoder, self).__init__()\nself.n_frames = n_frames\nself.z_dim = z_dim\nself.hand_side_invariance = hand_side_invariance\nself.frameEncoder = RGBEncoder(z_dim, hand_side_invariance)\nfeatures_num = self.n_frames * self.z_dim * 2\nself.layers = nn.Sequential(nn.Linear(features_num, features_num), nn.ReL... | <|body_start_0|>
super(VideoEncoder, self).__init__()
self.n_frames = n_frames
self.z_dim = z_dim
self.hand_side_invariance = hand_side_invariance
self.frameEncoder = RGBEncoder(z_dim, hand_side_invariance)
features_num = self.n_frames * self.z_dim * 2
self.layers... | VideoEncoder | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class VideoEncoder:
def __init__(self, n_frames, z_dim, hand_side_invariance):
"""实现对视频，多帧图片的同时编码。 :param n_frames: :param z_dim: :param hand_side_invariance: TODO: BatchNormal 的正确写法"""
<|body_0|>
def forward(self, x):
"""N 张图片一起输入。 :param x:(batch_size, n, 3, 320, 320) :r... | stack_v2_sparse_classes_75kplus_train_065935 | 1,416 | no_license | [
{
"docstring": "实现对视频，多帧图片的同时编码。 :param n_frames: :param z_dim: :param hand_side_invariance: TODO: BatchNormal 的正确写法",
"name": "__init__",
"signature": "def __init__(self, n_frames, z_dim, hand_side_invariance)"
},
{
"docstring": "N 张图片一起输入。 :param x:(batch_size, n, 3, 320, 320) :return:",
"... | 2 | null | Implement the Python class `VideoEncoder` described below.
Class description:
Implement the VideoEncoder class.
Method signatures and docstrings:
- def __init__(self, n_frames, z_dim, hand_side_invariance): 实现对视频，多帧图片的同时编码。 :param n_frames: :param z_dim: :param hand_side_invariance: TODO: BatchNormal 的正确写法
- def forw... | Implement the Python class `VideoEncoder` described below.
Class description:
Implement the VideoEncoder class.
Method signatures and docstrings:
- def __init__(self, n_frames, z_dim, hand_side_invariance): 实现对视频，多帧图片的同时编码。 :param n_frames: :param z_dim: :param hand_side_invariance: TODO: BatchNormal 的正确写法
- def forw... | a7c3779b3241634bbecaecbc58dbcc42aa44621f | <|skeleton|>
class VideoEncoder:
def __init__(self, n_frames, z_dim, hand_side_invariance):
"""实现对视频，多帧图片的同时编码。 :param n_frames: :param z_dim: :param hand_side_invariance: TODO: BatchNormal 的正确写法"""
<|body_0|>
def forward(self, x):
"""N 张图片一起输入。 :param x:(batch_size, n, 3, 320, 320) :r... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class VideoEncoder:
def __init__(self, n_frames, z_dim, hand_side_invariance):
"""实现对视频，多帧图片的同时编码。 :param n_frames: :param z_dim: :param hand_side_invariance: TODO: BatchNormal 的正确写法"""
super(VideoEncoder, self).__init__()
self.n_frames = n_frames
self.z_dim = z_dim
self.hand... | the_stack_v2_python_sparse | VaePose/Model/Video.py | guoyangzhao/VAE-Pose | train | 0 | |
a5f2fd2ee63600eb2af8aa34113489c36b9ec0b7 | [
"self.model = model\nself.monitors = []\nurwid.WidgetWrap.__init__(self, self.build())",
"self.cpuMonitor = SystemMonitor(self.model, 'cpu', 'header', 'CPU Load')\nself.memoryMonitor = SystemMonitor(self.model, 'memory', 'header', 'Used Memory (MB)')\nself.received = SystemMonitor(self.model, 'packetsreceived', '... | <|body_start_0|>
self.model = model
self.monitors = []
urwid.WidgetWrap.__init__(self, self.build())
<|end_body_0|>
<|body_start_1|>
self.cpuMonitor = SystemMonitor(self.model, 'cpu', 'header', 'CPU Load')
self.memoryMonitor = SystemMonitor(self.model, 'memory', 'header', 'Used ... | SystemMonitors is a view to track various system level information like CPU/Memory usage | SystemMonitors | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SystemMonitors:
"""SystemMonitors is a view to track various system level information like CPU/Memory usage"""
def __init__(self, model):
"""Parameters ---------- model : ConfigurationModel The model that this component is linked to."""
<|body_0|>
def build(self):
... | stack_v2_sparse_classes_75kplus_train_065936 | 1,822 | permissive | [
{
"docstring": "Parameters ---------- model : ConfigurationModel The model that this component is linked to.",
"name": "__init__",
"signature": "def __init__(self, model)"
},
{
"docstring": "Composes child views and components into a single object to be rendered. This view builds a list of Syste... | 3 | null | Implement the Python class `SystemMonitors` described below.
Class description:
SystemMonitors is a view to track various system level information like CPU/Memory usage
Method signatures and docstrings:
- def __init__(self, model): Parameters ---------- model : ConfigurationModel The model that this component is link... | Implement the Python class `SystemMonitors` described below.
Class description:
SystemMonitors is a view to track various system level information like CPU/Memory usage
Method signatures and docstrings:
- def __init__(self, model): Parameters ---------- model : ConfigurationModel The model that this component is link... | e8d919a1a8ebc8605f69d00378d2bfaa533731eb | <|skeleton|>
class SystemMonitors:
"""SystemMonitors is a view to track various system level information like CPU/Memory usage"""
def __init__(self, model):
"""Parameters ---------- model : ConfigurationModel The model that this component is linked to."""
<|body_0|>
def build(self):
... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class SystemMonitors:
"""SystemMonitors is a view to track various system level information like CPU/Memory usage"""
def __init__(self, model):
"""Parameters ---------- model : ConfigurationModel The model that this component is linked to."""
self.model = model
self.monitors = []
... | the_stack_v2_python_sparse | tui/views/systemMonitors.py | lenkatilka/diminish | train | 0 |
060c6cc7fa3636088d7ced3fcd6fc2827829d272 | [
"permitted_objects_filter = self.permitted_objects_filter(Job, JOB_READ)\nfilter_params = {}\nfor key in self.request.arguments.keys():\n if key in JobSchema.get_attribute_names():\n filter_params[key] = self.get_query_argument(key)\nresponse = await self.client(Operation(operation_type='JOB_READ_ALL', kw... | <|body_start_0|>
permitted_objects_filter = self.permitted_objects_filter(Job, JOB_READ)
filter_params = {}
for key in self.request.arguments.keys():
if key in JobSchema.get_attribute_names():
filter_params[key] = self.get_query_argument(key)
response = await ... | JobListAPI | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class JobListAPI:
async def get(self):
"""--- summary: Retrieve all Jobs. responses: 200: description: Successfully retrieved all systems. schema: type: array items: $ref: '#/definitions/Job' 50x: $ref: '#/definitions/50xError' tags: - Jobs"""
<|body_0|>
async def post(self):
... | stack_v2_sparse_classes_75kplus_train_065937 | 12,340 | permissive | [
{
"docstring": "--- summary: Retrieve all Jobs. responses: 200: description: Successfully retrieved all systems. schema: type: array items: $ref: '#/definitions/Job' 50x: $ref: '#/definitions/50xError' tags: - Jobs",
"name": "get",
"signature": "async def get(self)"
},
{
"docstring": "--- summar... | 2 | null | Implement the Python class `JobListAPI` described below.
Class description:
Implement the JobListAPI class.
Method signatures and docstrings:
- async def get(self): --- summary: Retrieve all Jobs. responses: 200: description: Successfully retrieved all systems. schema: type: array items: $ref: '#/definitions/Job' 50x... | Implement the Python class `JobListAPI` described below.
Class description:
Implement the JobListAPI class.
Method signatures and docstrings:
- async def get(self): --- summary: Retrieve all Jobs. responses: 200: description: Successfully retrieved all systems. schema: type: array items: $ref: '#/definitions/Job' 50x... | a5fd2dcc2444409e243d3fdaa43d86695e5cb142 | <|skeleton|>
class JobListAPI:
async def get(self):
"""--- summary: Retrieve all Jobs. responses: 200: description: Successfully retrieved all systems. schema: type: array items: $ref: '#/definitions/Job' 50x: $ref: '#/definitions/50xError' tags: - Jobs"""
<|body_0|>
async def post(self):
... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class JobListAPI:
async def get(self):
"""--- summary: Retrieve all Jobs. responses: 200: description: Successfully retrieved all systems. schema: type: array items: $ref: '#/definitions/Job' 50x: $ref: '#/definitions/50xError' tags: - Jobs"""
permitted_objects_filter = self.permitted_objects_filter... | the_stack_v2_python_sparse | src/app/beer_garden/api/http/handlers/v1/job.py | beer-garden/beer-garden | train | 254 | |
7e576625154c5678a2a913b519a279d77e3324e0 | [
"freq = Counter(tiles).values()\ndp = [[0] * (len(tiles) + 1) for _ in range(len(freq) + 1)]\ndp[0][0] = 1\nfor i, count in enumerate(freq, 1):\n for j in range(len(tiles) + 1):\n for k in range(min(j, count) + 1):\n dp[i][j] += dp[i - 1][j - k] * E.C(j, k)\n dp[i][j] %= MOD\nreturn ... | <|body_start_0|>
freq = Counter(tiles).values()
dp = [[0] * (len(tiles) + 1) for _ in range(len(freq) + 1)]
dp[0][0] = 1
for i, count in enumerate(freq, 1):
for j in range(len(tiles) + 1):
for k in range(min(j, count) + 1):
dp[i][j] += dp[i... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def numTilePossibilities(self, tiles: str) -> int:
"""O(n^2) dp. dp[i][j]表示用前i种字母组成长度为j的序列的个数. 如果选第i种字母k个, 则dp[i][j] = dp[i-1][j-k]*C(j, k)."""
<|body_0|>
def numTilePossibilities2(self, tiles: str) -> int:
"""O(n!*n)."""
<|body_1|>
<|end_skeleton|... | stack_v2_sparse_classes_75kplus_train_065938 | 3,705 | no_license | [
{
"docstring": "O(n^2) dp. dp[i][j]表示用前i种字母组成长度为j的序列的个数. 如果选第i种字母k个, 则dp[i][j] = dp[i-1][j-k]*C(j, k).",
"name": "numTilePossibilities",
"signature": "def numTilePossibilities(self, tiles: str) -> int"
},
{
"docstring": "O(n!*n).",
"name": "numTilePossibilities2",
"signature": "def numTi... | 2 | stack_v2_sparse_classes_30k_val_000309 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def numTilePossibilities(self, tiles: str) -> int: O(n^2) dp. dp[i][j]表示用前i种字母组成长度为j的序列的个数. 如果选第i种字母k个, 则dp[i][j] = dp[i-1][j-k]*C(j, k).
- def numTilePossibilities2(self, tiles:... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def numTilePossibilities(self, tiles: str) -> int: O(n^2) dp. dp[i][j]表示用前i种字母组成长度为j的序列的个数. 如果选第i种字母k个, 则dp[i][j] = dp[i-1][j-k]*C(j, k).
- def numTilePossibilities2(self, tiles:... | 7e79e26bb8f641868561b186e34c1127ed63c9e0 | <|skeleton|>
class Solution:
def numTilePossibilities(self, tiles: str) -> int:
"""O(n^2) dp. dp[i][j]表示用前i种字母组成长度为j的序列的个数. 如果选第i种字母k个, 则dp[i][j] = dp[i-1][j-k]*C(j, k)."""
<|body_0|>
def numTilePossibilities2(self, tiles: str) -> int:
"""O(n!*n)."""
<|body_1|>
<|end_skeleton|... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Solution:
def numTilePossibilities(self, tiles: str) -> int:
"""O(n^2) dp. dp[i][j]表示用前i种字母组成长度为j的序列的个数. 如果选第i种字母k个, 则dp[i][j] = dp[i-1][j-k]*C(j, k)."""
freq = Counter(tiles).values()
dp = [[0] * (len(tiles) + 1) for _ in range(len(freq) + 1)]
dp[0][0] = 1
for i, count... | the_stack_v2_python_sparse | 11_动态规划/字符串计数/1079. 活字印刷.py | 981377660LMT/algorithm-study | train | 225 | |
71e5ec9f0105511cb33dd5bbec9e2789017b92b4 | [
"self.id = id\nself.provider_id = provider_id\nself.server_time = server_time\nself.active_from = active_from\nself.active_to = active_to\nself.rpm_over_value = rpm_over_value\nself.over_speed_value = over_speed_value\nself.excess_speed_value = excess_speed_value\nself.long_idle_value = long_idle_value\nself.hi_thr... | <|body_start_0|>
self.id = id
self.provider_id = provider_id
self.server_time = server_time
self.active_from = active_from
self.active_to = active_to
self.rpm_over_value = rpm_over_value
self.over_speed_value = over_speed_value
self.excess_speed_value = ex... | Implementation of the 'Performance Thresholds' model. TODO: type model description here. Attributes: id (string): The unique identifier for the specific Entity object in the system. provider_id (string): The unique 'Provider ID' of the TSP server_time (string): Date and time when this object was received at the TSP act... | PerformanceThresholds | [
"MIT",
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PerformanceThresholds:
"""Implementation of the 'Performance Thresholds' model. TODO: type model description here. Attributes: id (string): The unique identifier for the specific Entity object in the system. provider_id (string): The unique 'Provider ID' of the TSP server_time (string): Date and ... | stack_v2_sparse_classes_75kplus_train_065939 | 4,395 | permissive | [
{
"docstring": "Constructor for the PerformanceThresholds class",
"name": "__init__",
"signature": "def __init__(self, id=None, provider_id=None, server_time=None, active_from=None, active_to=None, rpm_over_value=None, over_speed_value=None, excess_speed_value=None, long_idle_value=None, hi_throttle_val... | 2 | stack_v2_sparse_classes_30k_train_024596 | Implement the Python class `PerformanceThresholds` described below.
Class description:
Implementation of the 'Performance Thresholds' model. TODO: type model description here. Attributes: id (string): The unique identifier for the specific Entity object in the system. provider_id (string): The unique 'Provider ID' of ... | Implement the Python class `PerformanceThresholds` described below.
Class description:
Implementation of the 'Performance Thresholds' model. TODO: type model description here. Attributes: id (string): The unique identifier for the specific Entity object in the system. provider_id (string): The unique 'Provider ID' of ... | 729e9391879e273545a4818558677b2e47261f08 | <|skeleton|>
class PerformanceThresholds:
"""Implementation of the 'Performance Thresholds' model. TODO: type model description here. Attributes: id (string): The unique identifier for the specific Entity object in the system. provider_id (string): The unique 'Provider ID' of the TSP server_time (string): Date and ... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class PerformanceThresholds:
"""Implementation of the 'Performance Thresholds' model. TODO: type model description here. Attributes: id (string): The unique identifier for the specific Entity object in the system. provider_id (string): The unique 'Provider ID' of the TSP server_time (string): Date and time when thi... | the_stack_v2_python_sparse | sdk/python/v0.1-rc.4/opentelematicsapi/models/performance_thresholds.py | nmfta-repo/nmfta-opentelematics-prototype | train | 2 |
6540e2f840706330c067b01668f4996b2132d1df | [
"self.res = 10 ** n\n\ndef helper(n, set):\n if n <= 0:\n return\n if len(set) == 0:\n helper(n - 1, set)\n for i in range(1, 10):\n helper(n - 1, set + [i])\n else:\n for i in range(10):\n if i in set:\n self.res -= 10 ** (n - 1)\n ... | <|body_start_0|>
self.res = 10 ** n
def helper(n, set):
if n <= 0:
return
if len(set) == 0:
helper(n - 1, set)
for i in range(1, 10):
helper(n - 1, set + [i])
else:
for i in range(10)... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def countNumbersWithUniqueDigits2(self, n):
""":type n: int :rtype: int"""
<|body_0|>
def countNumbersWithUniqueDigits(self, n):
""":type n: int :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
self.res = 10 ** n
def he... | stack_v2_sparse_classes_75kplus_train_065940 | 1,044 | no_license | [
{
"docstring": ":type n: int :rtype: int",
"name": "countNumbersWithUniqueDigits2",
"signature": "def countNumbersWithUniqueDigits2(self, n)"
},
{
"docstring": ":type n: int :rtype: int",
"name": "countNumbersWithUniqueDigits",
"signature": "def countNumbersWithUniqueDigits(self, n)"
}... | 2 | stack_v2_sparse_classes_30k_train_028655 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def countNumbersWithUniqueDigits2(self, n): :type n: int :rtype: int
- def countNumbersWithUniqueDigits(self, n): :type n: int :rtype: int | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def countNumbersWithUniqueDigits2(self, n): :type n: int :rtype: int
- def countNumbersWithUniqueDigits(self, n): :type n: int :rtype: int
<|skeleton|>
class Solution:
def ... | 0fc972e5cd2baf1b5ddf8b192962629f40bc3bf4 | <|skeleton|>
class Solution:
def countNumbersWithUniqueDigits2(self, n):
""":type n: int :rtype: int"""
<|body_0|>
def countNumbersWithUniqueDigits(self, n):
""":type n: int :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Solution:
def countNumbersWithUniqueDigits2(self, n):
""":type n: int :rtype: int"""
self.res = 10 ** n
def helper(n, set):
if n <= 0:
return
if len(set) == 0:
helper(n - 1, set)
for i in range(1, 10):
... | the_stack_v2_python_sparse | Array/src/357. Count Numbers with Unique Digits.py | yukiii-zhong/Leetcode | train | 2 | |
4191b2d94c8897840db9e2aee4605e6c6e653681 | [
"self.x = x\nself.y = y\nself.hx = hx\nself.hy = hy",
"assert len(exclude_refcode) > 0\nrefcodes = self.x.index.tolist()\nhx = self.x.loc[exclude_refcode]\nhy = self.y.loc[exclude_refcode]\nremaining = list(set(refcodes).difference(set(exclude_refcode)))\nx = self.x.loc[remaining]\ny = self.y.loc[remaining]\nself... | <|body_start_0|>
self.x = x
self.y = y
self.hx = hx
self.hy = hy
<|end_body_0|>
<|body_start_1|>
assert len(exclude_refcode) > 0
refcodes = self.x.index.tolist()
hx = self.x.loc[exclude_refcode]
hy = self.y.loc[exclude_refcode]
remaining = list(se... | DimDataset | [
"CC-BY-4.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DimDataset:
def __init__(self, x: pd.DataFrame, y: pd.DataFrame, hx=None, hy=None):
"""dataset used for dimensionality prediction"""
<|body_0|>
def holdout(self, exclude_refcode: [str]):
""":param exclude_refcode: to exclude a list of structures and put them in holdo... | stack_v2_sparse_classes_75kplus_train_065941 | 15,633 | permissive | [
{
"docstring": "dataset used for dimensionality prediction",
"name": "__init__",
"signature": "def __init__(self, x: pd.DataFrame, y: pd.DataFrame, hx=None, hy=None)"
},
{
"docstring": ":param exclude_refcode: to exclude a list of structures and put them in holdout data/target",
"name": "hol... | 3 | stack_v2_sparse_classes_30k_train_004782 | Implement the Python class `DimDataset` described below.
Class description:
Implement the DimDataset class.
Method signatures and docstrings:
- def __init__(self, x: pd.DataFrame, y: pd.DataFrame, hx=None, hy=None): dataset used for dimensionality prediction
- def holdout(self, exclude_refcode: [str]): :param exclude... | Implement the Python class `DimDataset` described below.
Class description:
Implement the DimDataset class.
Method signatures and docstrings:
- def __init__(self, x: pd.DataFrame, y: pd.DataFrame, hx=None, hy=None): dataset used for dimensionality prediction
- def holdout(self, exclude_refcode: [str]): :param exclude... | a9e10419f30fcb1b53cd7c90cc752fd1c4f269d7 | <|skeleton|>
class DimDataset:
def __init__(self, x: pd.DataFrame, y: pd.DataFrame, hx=None, hy=None):
"""dataset used for dimensionality prediction"""
<|body_0|>
def holdout(self, exclude_refcode: [str]):
""":param exclude_refcode: to exclude a list of structures and put them in holdo... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class DimDataset:
def __init__(self, x: pd.DataFrame, y: pd.DataFrame, hx=None, hy=None):
"""dataset used for dimensionality prediction"""
self.x = x
self.y = y
self.hx = hx
self.hy = hy
def holdout(self, exclude_refcode: [str]):
""":param exclude_refcode: to exc... | the_stack_v2_python_sparse | cacgan/data/dataset.py | qai222/CompAugCycleGAN | train | 0 | |
199689dc56fd6fd4410d7620620842f97fb43cf0 | [
"if not num_feat_per_dim > 1:\n raise pyrado.ValueErr(given=num_feat_per_dim, g_constraint='1')\nif not len(bounds) == 2:\n raise pyrado.ShapeErr(given=bounds, expected_match=np.empty(2))\nbounds_to = [None, None]\nfor i, b in enumerate(bounds):\n if isinstance(b, np.ndarray):\n bounds_to[i] = to.fr... | <|body_start_0|>
if not num_feat_per_dim > 1:
raise pyrado.ValueErr(given=num_feat_per_dim, g_constraint='1')
if not len(bounds) == 2:
raise pyrado.ShapeErr(given=bounds, expected_match=np.empty(2))
bounds_to = [None, None]
for i, b in enumerate(bounds):
... | Normalized Gaussian radial basis function features | RBFFeat | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RBFFeat:
"""Normalized Gaussian radial basis function features"""
def __init__(self, num_feat_per_dim: int, bounds: [Sequence[np.ndarray], Sequence[to.Tensor], Sequence[float]], scale: float=None, state_wise_norm: bool=True):
"""Constructor :param num_feat_per_dim: number of radial b... | stack_v2_sparse_classes_75kplus_train_065942 | 13,729 | permissive | [
{
"docstring": "Constructor :param num_feat_per_dim: number of radial basis functions, identical for every dimension of the input :param bounds: lower and upper bound for the Gaussians' centers, the input dimension is inferred from them :param scale: scaling factor for the squared distance, if `None` the factor... | 3 | stack_v2_sparse_classes_30k_train_012772 | Implement the Python class `RBFFeat` described below.
Class description:
Normalized Gaussian radial basis function features
Method signatures and docstrings:
- def __init__(self, num_feat_per_dim: int, bounds: [Sequence[np.ndarray], Sequence[to.Tensor], Sequence[float]], scale: float=None, state_wise_norm: bool=True)... | Implement the Python class `RBFFeat` described below.
Class description:
Normalized Gaussian radial basis function features
Method signatures and docstrings:
- def __init__(self, num_feat_per_dim: int, bounds: [Sequence[np.ndarray], Sequence[to.Tensor], Sequence[float]], scale: float=None, state_wise_norm: bool=True)... | a6c982862e2ab39a9f65d1c09aa59d9a8b7ac6c5 | <|skeleton|>
class RBFFeat:
"""Normalized Gaussian radial basis function features"""
def __init__(self, num_feat_per_dim: int, bounds: [Sequence[np.ndarray], Sequence[to.Tensor], Sequence[float]], scale: float=None, state_wise_norm: bool=True):
"""Constructor :param num_feat_per_dim: number of radial b... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class RBFFeat:
"""Normalized Gaussian radial basis function features"""
def __init__(self, num_feat_per_dim: int, bounds: [Sequence[np.ndarray], Sequence[to.Tensor], Sequence[float]], scale: float=None, state_wise_norm: bool=True):
"""Constructor :param num_feat_per_dim: number of radial basis function... | the_stack_v2_python_sparse | Pyrado/pyrado/policies/features.py | jacarvalho/SimuRLacra | train | 0 |
25e8a9bbf04c03be73433d41d3ca73a792b3ff91 | [
"input_val = [1, 5, 4, 6, 9, 3, 0, 0, 1, 3]\noutput = ArrayMinJumps.ArrayMinJumps(input_val)\nself.assertEqual(output, 3)",
"input_val = [1, 0, 1]\noutput = ArrayMinJumps.ArrayMinJumps(input_val)\nself.assertEqual(output, -1)"
] | <|body_start_0|>
input_val = [1, 5, 4, 6, 9, 3, 0, 0, 1, 3]
output = ArrayMinJumps.ArrayMinJumps(input_val)
self.assertEqual(output, 3)
<|end_body_0|>
<|body_start_1|>
input_val = [1, 0, 1]
output = ArrayMinJumps.ArrayMinJumps(input_val)
self.assertEqual(output, -1)
<|en... | Class with unittests for ArrayMinJumps.py | test_ArrayMinJumps | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class test_ArrayMinJumps:
"""Class with unittests for ArrayMinJumps.py"""
def test_ExpectedOutput(self):
"""Checks if returned output is as expected."""
<|body_0|>
def test_NotPossible(self):
"""Checks if returned output is equal -1 if it is not possible to reach end o... | stack_v2_sparse_classes_75kplus_train_065943 | 924 | no_license | [
{
"docstring": "Checks if returned output is as expected.",
"name": "test_ExpectedOutput",
"signature": "def test_ExpectedOutput(self)"
},
{
"docstring": "Checks if returned output is equal -1 if it is not possible to reach end of array.",
"name": "test_NotPossible",
"signature": "def te... | 2 | stack_v2_sparse_classes_30k_test_001088 | Implement the Python class `test_ArrayMinJumps` described below.
Class description:
Class with unittests for ArrayMinJumps.py
Method signatures and docstrings:
- def test_ExpectedOutput(self): Checks if returned output is as expected.
- def test_NotPossible(self): Checks if returned output is equal -1 if it is not po... | Implement the Python class `test_ArrayMinJumps` described below.
Class description:
Class with unittests for ArrayMinJumps.py
Method signatures and docstrings:
- def test_ExpectedOutput(self): Checks if returned output is as expected.
- def test_NotPossible(self): Checks if returned output is equal -1 if it is not po... | 3aa62ad36c3b06b2a3b05f1f8e2a9e21d68b371f | <|skeleton|>
class test_ArrayMinJumps:
"""Class with unittests for ArrayMinJumps.py"""
def test_ExpectedOutput(self):
"""Checks if returned output is as expected."""
<|body_0|>
def test_NotPossible(self):
"""Checks if returned output is equal -1 if it is not possible to reach end o... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class test_ArrayMinJumps:
"""Class with unittests for ArrayMinJumps.py"""
def test_ExpectedOutput(self):
"""Checks if returned output is as expected."""
input_val = [1, 5, 4, 6, 9, 3, 0, 0, 1, 3]
output = ArrayMinJumps.ArrayMinJumps(input_val)
self.assertEqual(output, 3)
de... | the_stack_v2_python_sparse | Coderbyte_algorithms/Medium/ArrayMinJumps/test_ArrayMinJumps.py | JakubKazimierski/PythonPortfolio | train | 9 |
786981f34fbbbe5895356d53224c4c2665930c17 | [
"if len(args) == 0:\n return ()\nif len(args) == 2:\n substitution_pairs = (args,)\nelif len(args) == 1:\n arg = args[0]\n if isinstance(arg, dict):\n if arg == {}:\n return ()\n substitution_pairs = arg.items()\n else:\n if len(arg) == 0:\n return ()\n ... | <|body_start_0|>
if len(args) == 0:
return ()
if len(args) == 2:
substitution_pairs = (args,)
elif len(args) == 1:
arg = args[0]
if isinstance(arg, dict):
if arg == {}:
return ()
substitution_pair... | SubstitutionPairsParsingUtility | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SubstitutionPairsParsingUtility:
def parse(*args, required_object_type: Optional[Type]=object):
"""Extracts substitution pairs from general argument and check them. While extracting, also checks some invariants regarding the shape and content of the substitution pairs. The object is iter... | stack_v2_sparse_classes_75kplus_train_065944 | 16,598 | no_license | [
{
"docstring": "Extracts substitution pairs from general argument and check them. While extracting, also checks some invariants regarding the shape and content of the substitution pairs. The object is iterable of pairs (sequence of length 2). The first element is a pair occur only once among first elements (i.e... | 2 | stack_v2_sparse_classes_30k_train_016478 | Implement the Python class `SubstitutionPairsParsingUtility` described below.
Class description:
Implement the SubstitutionPairsParsingUtility class.
Method signatures and docstrings:
- def parse(*args, required_object_type: Optional[Type]=object): Extracts substitution pairs from general argument and check them. Whi... | Implement the Python class `SubstitutionPairsParsingUtility` described below.
Class description:
Implement the SubstitutionPairsParsingUtility class.
Method signatures and docstrings:
- def parse(*args, required_object_type: Optional[Type]=object): Extracts substitution pairs from general argument and check them. Whi... | acaf4d340f8ab0807ac655186a154b064d49f12c | <|skeleton|>
class SubstitutionPairsParsingUtility:
def parse(*args, required_object_type: Optional[Type]=object):
"""Extracts substitution pairs from general argument and check them. While extracting, also checks some invariants regarding the shape and content of the substitution pairs. The object is iter... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class SubstitutionPairsParsingUtility:
def parse(*args, required_object_type: Optional[Type]=object):
"""Extracts substitution pairs from general argument and check them. While extracting, also checks some invariants regarding the shape and content of the substitution pairs. The object is iterable of pairs ... | the_stack_v2_python_sparse | neads/activation_model/symbolic_objects/symbolic_object.py | Thrayld/neads | train | 0 | |
d0ea87c9c30a5f1d183d4fb95e618a2e684300c7 | [
"reasoner = GdlChainingReasoner.create(model)\nsentencesByForm = reasoner.getConstantSentences()\naddSentencesTrueByRulesDifferentially(sentencesByForm, model, reasoner)\nreturn ImmutableConstantChecker.create(model, Multimaps.filterKeys(sentencesByForm.getSentences(), Predicates.in_(model.getConstantSentenceForms(... | <|body_start_0|>
reasoner = GdlChainingReasoner.create(model)
sentencesByForm = reasoner.getConstantSentences()
addSentencesTrueByRulesDifferentially(sentencesByForm, model, reasoner)
return ImmutableConstantChecker.create(model, Multimaps.filterKeys(sentencesByForm.getSentences(), Predi... | generated source for class ConstantCheckerFactory | ConstantCheckerFactory | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ConstantCheckerFactory:
"""generated source for class ConstantCheckerFactory"""
def createWithForwardChaining(cls, model):
"""generated source for method createWithForwardChaining"""
<|body_0|>
def addSentencesTrueByRulesDifferentially(cls, sentencesByForm, domainModel, ... | stack_v2_sparse_classes_75kplus_train_065945 | 5,719 | permissive | [
{
"docstring": "generated source for method createWithForwardChaining",
"name": "createWithForwardChaining",
"signature": "def createWithForwardChaining(cls, model)"
},
{
"docstring": "generated source for method addSentencesTrueByRulesDifferentially",
"name": "addSentencesTrueByRulesDiffere... | 5 | stack_v2_sparse_classes_30k_test_001249 | Implement the Python class `ConstantCheckerFactory` described below.
Class description:
generated source for class ConstantCheckerFactory
Method signatures and docstrings:
- def createWithForwardChaining(cls, model): generated source for method createWithForwardChaining
- def addSentencesTrueByRulesDifferentially(cls... | Implement the Python class `ConstantCheckerFactory` described below.
Class description:
generated source for class ConstantCheckerFactory
Method signatures and docstrings:
- def createWithForwardChaining(cls, model): generated source for method createWithForwardChaining
- def addSentencesTrueByRulesDifferentially(cls... | 4e6e6e876c3a4294cd711647051da2d9c1836b60 | <|skeleton|>
class ConstantCheckerFactory:
"""generated source for class ConstantCheckerFactory"""
def createWithForwardChaining(cls, model):
"""generated source for method createWithForwardChaining"""
<|body_0|>
def addSentencesTrueByRulesDifferentially(cls, sentencesByForm, domainModel, ... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class ConstantCheckerFactory:
"""generated source for class ConstantCheckerFactory"""
def createWithForwardChaining(cls, model):
"""generated source for method createWithForwardChaining"""
reasoner = GdlChainingReasoner.create(model)
sentencesByForm = reasoner.getConstantSentences()
... | the_stack_v2_python_sparse | ggpy/cruft/autocode/ConstantCheckerFactory.py | hobson/ggpy | train | 1 |
9a271f9b08b3c1b6fd0d99f87872cbeb78d93115 | [
"if db_field.name == 'user':\n kwargs['queryset'] = User.objects.filter(id=request.user.id)\n kwargs['initial'] = request.user.id\nelif db_field.name == 'topic' and (not request.user.is_superuser):\n kwargs['queryset'] = Topic.objects.filter(id__in=request.user.profile.topics.all())\nreturn super(TaskAdmin... | <|body_start_0|>
if db_field.name == 'user':
kwargs['queryset'] = User.objects.filter(id=request.user.id)
kwargs['initial'] = request.user.id
elif db_field.name == 'topic' and (not request.user.is_superuser):
kwargs['queryset'] = Topic.objects.filter(id__in=request.us... | TaskAdmin | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TaskAdmin:
def formfield_for_foreignkey(self, db_field, request, **kwargs):
"""Assigns default value for User field. limits Topics field to user's topics."""
<|body_0|>
def formfield_for_manytomany(self, db_field, request, **kwargs):
"""Limits the choices of professo... | stack_v2_sparse_classes_75kplus_train_065946 | 9,167 | permissive | [
{
"docstring": "Assigns default value for User field. limits Topics field to user's topics.",
"name": "formfield_for_foreignkey",
"signature": "def formfield_for_foreignkey(self, db_field, request, **kwargs)"
},
{
"docstring": "Limits the choices of professors for the limit of user.",
"name"... | 3 | stack_v2_sparse_classes_30k_train_000481 | Implement the Python class `TaskAdmin` described below.
Class description:
Implement the TaskAdmin class.
Method signatures and docstrings:
- def formfield_for_foreignkey(self, db_field, request, **kwargs): Assigns default value for User field. limits Topics field to user's topics.
- def formfield_for_manytomany(self... | Implement the Python class `TaskAdmin` described below.
Class description:
Implement the TaskAdmin class.
Method signatures and docstrings:
- def formfield_for_foreignkey(self, db_field, request, **kwargs): Assigns default value for User field. limits Topics field to user's topics.
- def formfield_for_manytomany(self... | 70638c121ea85ff0e6a650c5f2641b0b3b04d6d0 | <|skeleton|>
class TaskAdmin:
def formfield_for_foreignkey(self, db_field, request, **kwargs):
"""Assigns default value for User field. limits Topics field to user's topics."""
<|body_0|>
def formfield_for_manytomany(self, db_field, request, **kwargs):
"""Limits the choices of professo... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class TaskAdmin:
def formfield_for_foreignkey(self, db_field, request, **kwargs):
"""Assigns default value for User field. limits Topics field to user's topics."""
if db_field.name == 'user':
kwargs['queryset'] = User.objects.filter(id=request.user.id)
kwargs['initial'] = req... | the_stack_v2_python_sparse | cms/admin.py | Ibrahem3amer/bala7 | train | 0 | |
4e59dd42814fa7575e5c3577c1a29890521ac950 | [
"train_loss_legend, test_loss_legend = ([], [])\nmin_test_loss, running_loss, i = (np.inf, 0, 0)\nloss_pattern = standardize_loss_pattern(loss_pattern)\nif evaluate_every is None:\n evaluate_every = log_every if log_every is not None else 1\ndataset, test_dataset = self.split_dataset(dataset, test_frac)\nmodel.t... | <|body_start_0|>
train_loss_legend, test_loss_legend = ([], [])
min_test_loss, running_loss, i = (np.inf, 0, 0)
loss_pattern = standardize_loss_pattern(loss_pattern)
if evaluate_every is None:
evaluate_every = log_every if log_every is not None else 1
dataset, test_da... | Class for neural-networks training. | NNTrainer | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class NNTrainer:
"""Class for neural-networks training."""
def train_model(self, model, dataset, n_epoch=1, lr=0.001, parameters_to_optimize=None, loss_pattern=None, log_every=None, evaluate_every=None, test_frac=0, dump_best_parameters=None, optimizer=torch.optim.Adam, scheduler=partial(torch.opt... | stack_v2_sparse_classes_75kplus_train_065947 | 6,362 | permissive | [
{
"docstring": "Train model on the given dataset. Parameters ---------- model: BaseModel Model to train. dataset: FieldDataset Dataset to use. n_epoch: int Number of iterations through the dataset. lr: float Learning rate. parameters_to_optimize: None or tuple of model's parameters Will optimize loss over this ... | 6 | stack_v2_sparse_classes_30k_train_034446 | Implement the Python class `NNTrainer` described below.
Class description:
Class for neural-networks training.
Method signatures and docstrings:
- def train_model(self, model, dataset, n_epoch=1, lr=0.001, parameters_to_optimize=None, loss_pattern=None, log_every=None, evaluate_every=None, test_frac=0, dump_best_para... | Implement the Python class `NNTrainer` described below.
Class description:
Class for neural-networks training.
Method signatures and docstrings:
- def train_model(self, model, dataset, n_epoch=1, lr=0.001, parameters_to_optimize=None, loss_pattern=None, log_every=None, evaluate_every=None, test_frac=0, dump_best_para... | 3b336ed110ff806316f1f6a99b212f99256a6b56 | <|skeleton|>
class NNTrainer:
"""Class for neural-networks training."""
def train_model(self, model, dataset, n_epoch=1, lr=0.001, parameters_to_optimize=None, loss_pattern=None, log_every=None, evaluate_every=None, test_frac=0, dump_best_parameters=None, optimizer=torch.optim.Adam, scheduler=partial(torch.opt... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class NNTrainer:
"""Class for neural-networks training."""
def train_model(self, model, dataset, n_epoch=1, lr=0.001, parameters_to_optimize=None, loss_pattern=None, log_every=None, evaluate_every=None, test_frac=0, dump_best_parameters=None, optimizer=torch.optim.Adam, scheduler=partial(torch.optim.lr_schedul... | the_stack_v2_python_sparse | deepfield/metamodelling/training.py | scuervo91/DeepField | train | 0 |
589857bd6234392e36f0991be93fe854536c96f9 | [
"self.sys = platform.system()\nself.name = netName or settings.ADSL_NAME\nself.url = url or settings.IP138_URL\nself.token = token or settings.IP138_TOKEN\nself.user = user or settings.ADSL_USER\nself.password = password or settings.ADSL_PASSWORD\nself.ip = self.refreshIP()\nself.status = False\nif self.sys == 'Win... | <|body_start_0|>
self.sys = platform.system()
self.name = netName or settings.ADSL_NAME
self.url = url or settings.IP138_URL
self.token = token or settings.IP138_TOKEN
self.user = user or settings.ADSL_USER
self.password = password or settings.ADSL_PASSWORD
self.i... | ADSL_Tool | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ADSL_Tool:
def __init__(self, netName='adslproxy', user=None, password=None, url=None, token=None):
"""user: windows使用者的adsl用户账号 password: windows使用者的adsl密码 url: ip138的ip查询临时url token: ip138提供的ip查询接口 当同时使用了url与token参数，默认会调用token返回ip地址"""
<|body_0|>
def cmd(self, commands):
... | stack_v2_sparse_classes_75kplus_train_065948 | 3,858 | no_license | [
{
"docstring": "user: windows使用者的adsl用户账号 password: windows使用者的adsl密码 url: ip138的ip查询临时url token: ip138提供的ip查询接口 当同时使用了url与token参数，默认会调用token返回ip地址",
"name": "__init__",
"signature": "def __init__(self, netName='adslproxy', user=None, password=None, url=None, token=None)"
},
{
"docstring": "comm... | 6 | stack_v2_sparse_classes_30k_train_053265 | Implement the Python class `ADSL_Tool` described below.
Class description:
Implement the ADSL_Tool class.
Method signatures and docstrings:
- def __init__(self, netName='adslproxy', user=None, password=None, url=None, token=None): user: windows使用者的adsl用户账号 password: windows使用者的adsl密码 url: ip138的ip查询临时url token: ip138... | Implement the Python class `ADSL_Tool` described below.
Class description:
Implement the ADSL_Tool class.
Method signatures and docstrings:
- def __init__(self, netName='adslproxy', user=None, password=None, url=None, token=None): user: windows使用者的adsl用户账号 password: windows使用者的adsl密码 url: ip138的ip查询临时url token: ip138... | 54bf8cc8fba72a1177ce3279a3e0f7a7a8fc754e | <|skeleton|>
class ADSL_Tool:
def __init__(self, netName='adslproxy', user=None, password=None, url=None, token=None):
"""user: windows使用者的adsl用户账号 password: windows使用者的adsl密码 url: ip138的ip查询临时url token: ip138提供的ip查询接口 当同时使用了url与token参数，默认会调用token返回ip地址"""
<|body_0|>
def cmd(self, commands):
... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class ADSL_Tool:
def __init__(self, netName='adslproxy', user=None, password=None, url=None, token=None):
"""user: windows使用者的adsl用户账号 password: windows使用者的adsl密码 url: ip138的ip查询临时url token: ip138提供的ip查询接口 当同时使用了url与token参数，默认会调用token返回ip地址"""
self.sys = platform.system()
self.name = netName... | the_stack_v2_python_sparse | adsl_py/adsl.py | crystalxiao/myspider | train | 0 | |
faa581470bb770c5b7e886fd25ebb7c27f47aecc | [
"tel_id = h.get_teldata_list()\ntel_num = h.get_num_telescope()\ntel_posX = [-1] * u.m\ntel_posY = [-1] * u.m\ntel_posZ = [-1] * u.m\nreturn (tel_id, tel_num, tel_posX, tel_posY, tel_posZ)",
"hdulist = item\nteles = hdulist[1].data\ntel_id = teles['TelID']\ntel_num = len(tel_id)\ntel_posX = teles['TelX'] * u.m\nt... | <|body_start_0|>
tel_id = h.get_teldata_list()
tel_num = h.get_num_telescope()
tel_posX = [-1] * u.m
tel_posY = [-1] * u.m
tel_posZ = [-1] * u.m
return (tel_id, tel_num, tel_posX, tel_posY, tel_posZ)
<|end_body_0|>
<|body_start_1|>
hdulist = item
teles = ... | `Initialize` is a class containing the initialize functions for the different file extensions | Initialize | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Initialize:
"""`Initialize` is a class containing the initialize functions for the different file extensions"""
def _initialize_hessio(filename, item):
"""reads the Telescope data out of the open hessio file Parameters ---------- filename: string name of the hessio file (must be a he... | stack_v2_sparse_classes_75kplus_train_065949 | 3,190 | no_license | [
{
"docstring": "reads the Telescope data out of the open hessio file Parameters ---------- filename: string name of the hessio file (must be a hessio file!)",
"name": "_initialize_hessio",
"signature": "def _initialize_hessio(filename, item)"
},
{
"docstring": "reads the Telescope data out of th... | 3 | stack_v2_sparse_classes_30k_train_014890 | Implement the Python class `Initialize` described below.
Class description:
`Initialize` is a class containing the initialize functions for the different file extensions
Method signatures and docstrings:
- def _initialize_hessio(filename, item): reads the Telescope data out of the open hessio file Parameters --------... | Implement the Python class `Initialize` described below.
Class description:
`Initialize` is a class containing the initialize functions for the different file extensions
Method signatures and docstrings:
- def _initialize_hessio(filename, item): reads the Telescope data out of the open hessio file Parameters --------... | 3308978bcd0f02abc9ca34f028a24d9689fb6364 | <|skeleton|>
class Initialize:
"""`Initialize` is a class containing the initialize functions for the different file extensions"""
def _initialize_hessio(filename, item):
"""reads the Telescope data out of the open hessio file Parameters ---------- filename: string name of the hessio file (must be a he... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Initialize:
"""`Initialize` is a class containing the initialize functions for the different file extensions"""
def _initialize_hessio(filename, item):
"""reads the Telescope data out of the open hessio file Parameters ---------- filename: string name of the hessio file (must be a hessio file!)""... | the_stack_v2_python_sparse | instrument/telescope/TelescopeDescription_20151203.py | justuszorn/CTA_InstrumentModule_Backup | train | 0 |
96a8e020a72d791a55bf963cc8fc9d8fe4217538 | [
"max_len = 0\nA_set = set(A)\n\ndef backtrack(cur_seq):\n nonlocal max_len\n next = cur_seq[-1] + cur_seq[-2]\n if next in A_set:\n cur_seq.append(next)\n backtrack(cur_seq)\n else:\n max_len = max(max_len, len(cur_seq))\nfor i in range(len(A) - 1):\n for j in range(i + 1, len(A)... | <|body_start_0|>
max_len = 0
A_set = set(A)
def backtrack(cur_seq):
nonlocal max_len
next = cur_seq[-1] + cur_seq[-2]
if next in A_set:
cur_seq.append(next)
backtrack(cur_seq)
else:
max_len = max(max... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def lenLongestFibSubseq(self, A):
""":type A: List[int] :rtype: int"""
<|body_0|>
def lenLongestFibSubseq2(self, A):
""":type A: List[int] :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
max_len = 0
A_set = set(A)
... | stack_v2_sparse_classes_75kplus_train_065950 | 1,219 | no_license | [
{
"docstring": ":type A: List[int] :rtype: int",
"name": "lenLongestFibSubseq",
"signature": "def lenLongestFibSubseq(self, A)"
},
{
"docstring": ":type A: List[int] :rtype: int",
"name": "lenLongestFibSubseq2",
"signature": "def lenLongestFibSubseq2(self, A)"
}
] | 2 | stack_v2_sparse_classes_30k_train_041472 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def lenLongestFibSubseq(self, A): :type A: List[int] :rtype: int
- def lenLongestFibSubseq2(self, A): :type A: List[int] :rtype: int | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def lenLongestFibSubseq(self, A): :type A: List[int] :rtype: int
- def lenLongestFibSubseq2(self, A): :type A: List[int] :rtype: int
<|skeleton|>
class Solution:
def lenLon... | 4a1747b6497305f3821612d9c358a6795b1690da | <|skeleton|>
class Solution:
def lenLongestFibSubseq(self, A):
""":type A: List[int] :rtype: int"""
<|body_0|>
def lenLongestFibSubseq2(self, A):
""":type A: List[int] :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Solution:
def lenLongestFibSubseq(self, A):
""":type A: List[int] :rtype: int"""
max_len = 0
A_set = set(A)
def backtrack(cur_seq):
nonlocal max_len
next = cur_seq[-1] + cur_seq[-2]
if next in A_set:
cur_seq.append(next)
... | the_stack_v2_python_sparse | DynamicProgramming/q873_length_of_longest_fibonacci_subsequence.py | sevenhe716/LeetCode | train | 0 | |
382db0d228e8136c6eef0b0e670b1762ce0acb71 | [
"sql = \"\\n SELECT\\n *\\n FROM\\n (SELECT\\n CONCAT(t.name, '-', e.model) e_name, COUNT(*) type_count\\n FROM\\n cmdb.cmdb_equipment e, cmdb.cmdb_baseequipmenttype t, cmdb.cmdb_basefactory f\\n ... | <|body_start_0|>
sql = "\n SELECT\n *\n FROM\n (SELECT\n CONCAT(t.name, '-', e.model) e_name, COUNT(*) type_count\n FROM\n cmdb.cmdb_equipment e, cmdb.cmdb_baseequipmenttype t, cmdb.cmdb_basefactory f\n ... | EquipmentManage | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class EquipmentManage:
def equipment_type_group_count(self, custid=None):
"""统计网络设备数量,按设备类型分类统计 :return:"""
<|body_0|>
def month_group_count(self, month_value_dict, custid=None):
"""获取当前月份以及之前12月内所有设备数量统计 :param custid: 客户ID :return:"""
<|body_1|>
<|end_skeleton|>... | stack_v2_sparse_classes_75kplus_train_065951 | 9,914 | permissive | [
{
"docstring": "统计网络设备数量,按设备类型分类统计 :return:",
"name": "equipment_type_group_count",
"signature": "def equipment_type_group_count(self, custid=None)"
},
{
"docstring": "获取当前月份以及之前12月内所有设备数量统计 :param custid: 客户ID :return:",
"name": "month_group_count",
"signature": "def month_group_count(s... | 2 | null | Implement the Python class `EquipmentManage` described below.
Class description:
Implement the EquipmentManage class.
Method signatures and docstrings:
- def equipment_type_group_count(self, custid=None): 统计网络设备数量,按设备类型分类统计 :return:
- def month_group_count(self, month_value_dict, custid=None): 获取当前月份以及之前12月内所有设备数量统计 ... | Implement the Python class `EquipmentManage` described below.
Class description:
Implement the EquipmentManage class.
Method signatures and docstrings:
- def equipment_type_group_count(self, custid=None): 统计网络设备数量,按设备类型分类统计 :return:
- def month_group_count(self, month_value_dict, custid=None): 获取当前月份以及之前12月内所有设备数量统计 ... | 002f80dcc07e3502610b0a0be1e91fe61bcfc42c | <|skeleton|>
class EquipmentManage:
def equipment_type_group_count(self, custid=None):
"""统计网络设备数量,按设备类型分类统计 :return:"""
<|body_0|>
def month_group_count(self, month_value_dict, custid=None):
"""获取当前月份以及之前12月内所有设备数量统计 :param custid: 客户ID :return:"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class EquipmentManage:
def equipment_type_group_count(self, custid=None):
"""统计网络设备数量,按设备类型分类统计 :return:"""
sql = "\n SELECT\n *\n FROM\n (SELECT\n CONCAT(t.name, '-', e.model) e_name, COUNT(*) type_count\n FROM\n ... | the_stack_v2_python_sparse | cmdb/afcat/cmdb/packages/cmdbmanger.py | tonglinge/MyProjects | train | 4 | |
2cecf00407ae0f3a914afd50bad69d7edb990522 | [
"self.name = name\nself.sgm = {}\nself.psarj = []\nself.ssarj = []\nself.read_sgm()\nself.eqv_15deg()",
"with open(self.name) as f:\n for i, line in enumerate(f):\n line_split = line.split(sep=',')\n if i > 2:\n line_split[0] = int(line_split[0])\n label = line_split[0]\n ... | <|body_start_0|>
self.name = name
self.sgm = {}
self.psarj = []
self.ssarj = []
self.read_sgm()
self.eqv_15deg()
<|end_body_0|>
<|body_start_1|>
with open(self.name) as f:
for i, line in enumerate(f):
line_split = line.split(sep=',')
... | Class of object to represent the sgm file, csv of inc. | SGM | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SGM:
"""Class of object to represent the sgm file, csv of inc."""
def __init__(self, name):
"""Method to initialize."""
<|body_0|>
def read_sgm(self):
"""Method to read in the sgm file and parse."""
<|body_1|>
def eqv_15deg(self):
"""Method t... | stack_v2_sparse_classes_75kplus_train_065952 | 11,241 | no_license | [
{
"docstring": "Method to initialize.",
"name": "__init__",
"signature": "def __init__(self, name)"
},
{
"docstring": "Method to read in the sgm file and parse.",
"name": "read_sgm",
"signature": "def read_sgm(self)"
},
{
"docstring": "Method to round to nearest 15deg increment f... | 3 | stack_v2_sparse_classes_30k_train_019331 | Implement the Python class `SGM` described below.
Class description:
Class of object to represent the sgm file, csv of inc.
Method signatures and docstrings:
- def __init__(self, name): Method to initialize.
- def read_sgm(self): Method to read in the sgm file and parse.
- def eqv_15deg(self): Method to round to near... | Implement the Python class `SGM` described below.
Class description:
Class of object to represent the sgm file, csv of inc.
Method signatures and docstrings:
- def __init__(self, name): Method to initialize.
- def read_sgm(self): Method to read in the sgm file and parse.
- def eqv_15deg(self): Method to round to near... | 6b37842203ff4318a48dbf0258cbe2b253436e7d | <|skeleton|>
class SGM:
"""Class of object to represent the sgm file, csv of inc."""
def __init__(self, name):
"""Method to initialize."""
<|body_0|>
def read_sgm(self):
"""Method to read in the sgm file and parse."""
<|body_1|>
def eqv_15deg(self):
"""Method t... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class SGM:
"""Class of object to represent the sgm file, csv of inc."""
def __init__(self, name):
"""Method to initialize."""
self.name = name
self.sgm = {}
self.psarj = []
self.ssarj = []
self.read_sgm()
self.eqv_15deg()
def read_sgm(self):
... | the_stack_v2_python_sparse | thermal/inc.py | tslowery78/PyLnD | train | 0 |
f80f2dfe0c9fe305c4cbe9075e027b947422f23a | [
"if not root:\n return []\nqueue = deque([root])\nresult = []\nwhile queue:\n curr = queue.popleft()\n result.append(curr.val) if curr else result.append(curr)\n if not curr:\n continue\n queue.append(curr.left)\n queue.append(curr.right)\nreturn result",
"if not data:\n return None\ni... | <|body_start_0|>
if not root:
return []
queue = deque([root])
result = []
while queue:
curr = queue.popleft()
result.append(curr.val) if curr else result.append(curr)
if not curr:
continue
queue.append(curr.left)... | 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_75kplus_train_065953 | 1,591 | 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_test_002311 | 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:... | 8d120162657a1e29c3e821b51ac4121300fc7a12 | <|skeleton|>
class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
<|body_0|>
def deserialize(self, data):
"""Decodes your encoded data to tree. :type data: str :rtype: TreeNode"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
if not root:
return []
queue = deque([root])
result = []
while queue:
curr = queue.popleft()
result.append(curr.val) if curr e... | the_stack_v2_python_sparse | LeetCode/Tree/297. Serialize and Deserialize Binary Tree.py | thehanemperor/LeetCode | train | 0 | |
d897c9ca13ebcfaa38c8b439461ccd66128ae5dc | [
"self.serializer = instance_serializer\nkwargs['source'] = '*'\nsuper().__init__(**kwargs)",
"if data == self.parent.context['request'].user.id:\n raise serializers.ValidationError('Cannot be friend with self.')\nreturn {'friend': User.objects.get(id=data)}",
"if self.parent.context['request'].user == value.... | <|body_start_0|>
self.serializer = instance_serializer
kwargs['source'] = '*'
super().__init__(**kwargs)
<|end_body_0|>
<|body_start_1|>
if data == self.parent.context['request'].user.id:
raise serializers.ValidationError('Cannot be friend with self.')
return {'frien... | Field for a friend. This will check the `from_account` and `to_account` values and will return the one that is not the current user. :param instance_serializer: serializer used to serialize the friend :param kwargs: additional arguments to pass to the `Field` constructor | FriendField | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FriendField:
"""Field for a friend. This will check the `from_account` and `to_account` values and will return the one that is not the current user. :param instance_serializer: serializer used to serialize the friend :param kwargs: additional arguments to pass to the `Field` constructor"""
d... | stack_v2_sparse_classes_75kplus_train_065954 | 14,729 | no_license | [
{
"docstring": "Override the default constructor of field to force the `source` to be the whole object. This also sets the serializer used internally :param kwargs: arguments to pass to the parent constructor",
"name": "__init__",
"signature": "def __init__(self, instance_serializer, **kwargs)"
},
{... | 3 | stack_v2_sparse_classes_30k_train_019727 | Implement the Python class `FriendField` described below.
Class description:
Field for a friend. This will check the `from_account` and `to_account` values and will return the one that is not the current user. :param instance_serializer: serializer used to serialize the friend :param kwargs: additional arguments to pa... | Implement the Python class `FriendField` described below.
Class description:
Field for a friend. This will check the `from_account` and `to_account` values and will return the one that is not the current user. :param instance_serializer: serializer used to serialize the friend :param kwargs: additional arguments to pa... | 38f0b29e6fc737756ae21a8c193a110876bc221c | <|skeleton|>
class FriendField:
"""Field for a friend. This will check the `from_account` and `to_account` values and will return the one that is not the current user. :param instance_serializer: serializer used to serialize the friend :param kwargs: additional arguments to pass to the `Field` constructor"""
d... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class FriendField:
"""Field for a friend. This will check the `from_account` and `to_account` values and will return the one that is not the current user. :param instance_serializer: serializer used to serialize the friend :param kwargs: additional arguments to pass to the `Field` constructor"""
def __init__(s... | the_stack_v2_python_sparse | backend/user/serializers.py | BenjaminSchubert/HEIG_VD_2016_PDG | train | 0 |
d44217a9444780e5f7f8b258bc1a9497b80f4d89 | [
"self.dir = experiment_dir\nself.name = self.dir.split('/')[-1]\nself.sessions = {}",
"cwd = os.getcwd()\nos.chdir(probe_file_dir)\nprobe_module = import_module(probe_name)\nprobe_class = getattr(probe_module, probe_name)\nos.chdir(cwd)\nself.probe = probe_class()\nself.probe.get_channel_mapping(self.amplifier)\n... | <|body_start_0|>
self.dir = experiment_dir
self.name = self.dir.split('/')[-1]
self.sessions = {}
<|end_body_0|>
<|body_start_1|>
cwd = os.getcwd()
os.chdir(probe_file_dir)
probe_module = import_module(probe_name)
probe_class = getattr(probe_module, probe_name)
... | Experiment | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Experiment:
def __init__(self, experiment_dir):
"""Initialization function of the Experiment class. This class contains the parameters and classes for the overall acute or chronic experiment. Inputs: -experiment_dir: The directory to the folder that contains the recording session folders... | stack_v2_sparse_classes_75kplus_train_065955 | 8,147 | no_license | [
{
"docstring": "Initialization function of the Experiment class. This class contains the parameters and classes for the overall acute or chronic experiment. Inputs: -experiment_dir: The directory to the folder that contains the recording session folders for the experiment",
"name": "__init__",
"signatur... | 4 | stack_v2_sparse_classes_30k_train_001859 | Implement the Python class `Experiment` described below.
Class description:
Implement the Experiment class.
Method signatures and docstrings:
- def __init__(self, experiment_dir): Initialization function of the Experiment class. This class contains the parameters and classes for the overall acute or chronic experimen... | Implement the Python class `Experiment` described below.
Class description:
Implement the Experiment class.
Method signatures and docstrings:
- def __init__(self, experiment_dir): Initialization function of the Experiment class. This class contains the parameters and classes for the overall acute or chronic experimen... | 9639815b9de793c867a358f73df32e15b5b01544 | <|skeleton|>
class Experiment:
def __init__(self, experiment_dir):
"""Initialization function of the Experiment class. This class contains the parameters and classes for the overall acute or chronic experiment. Inputs: -experiment_dir: The directory to the folder that contains the recording session folders... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Experiment:
def __init__(self, experiment_dir):
"""Initialization function of the Experiment class. This class contains the parameters and classes for the overall acute or chronic experiment. Inputs: -experiment_dir: The directory to the folder that contains the recording session folders for the exper... | the_stack_v2_python_sparse | utils/experiment_classes.py | tanselbaran/depth_recordings | train | 1 | |
1a29ae6ed997a7ad9ca85526086c6dd3c0431919 | [
"formula = self.get_object()\npublic = request.DATA.get('public', None)\nif public is None or len(request.DATA) > 1:\n raise BadRequest('Only \"public\" field of a formula may be modified.')\nif not isinstance(public, bool):\n raise BadRequest(\"'public' field must be a boolean value.\")\nformula.public = pub... | <|body_start_0|>
formula = self.get_object()
public = request.DATA.get('public', None)
if public is None or len(request.DATA) > 1:
raise BadRequest('Only "public" field of a formula may be modified.')
if not isinstance(public, bool):
raise BadRequest("'public' fie... | FormulaDetailAPIView | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FormulaDetailAPIView:
def update(self, request, *args, **kwargs):
"""Override PUT requests to only allow the public field to be changed."""
<|body_0|>
def delete(self, request, *args, **kwargs):
"""Override the delete method to check for ownership and prevent delete ... | stack_v2_sparse_classes_75kplus_train_065956 | 10,644 | permissive | [
{
"docstring": "Override PUT requests to only allow the public field to be changed.",
"name": "update",
"signature": "def update(self, request, *args, **kwargs)"
},
{
"docstring": "Override the delete method to check for ownership and prevent delete if other resources depend on this formula or o... | 2 | null | Implement the Python class `FormulaDetailAPIView` described below.
Class description:
Implement the FormulaDetailAPIView class.
Method signatures and docstrings:
- def update(self, request, *args, **kwargs): Override PUT requests to only allow the public field to be changed.
- def delete(self, request, *args, **kwarg... | Implement the Python class `FormulaDetailAPIView` described below.
Class description:
Implement the FormulaDetailAPIView class.
Method signatures and docstrings:
- def update(self, request, *args, **kwargs): Override PUT requests to only allow the public field to be changed.
- def delete(self, request, *args, **kwarg... | 8dcd9967a9213bdeec666b14e55583c50bdaf57e | <|skeleton|>
class FormulaDetailAPIView:
def update(self, request, *args, **kwargs):
"""Override PUT requests to only allow the public field to be changed."""
<|body_0|>
def delete(self, request, *args, **kwargs):
"""Override the delete method to check for ownership and prevent delete ... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class FormulaDetailAPIView:
def update(self, request, *args, **kwargs):
"""Override PUT requests to only allow the public field to be changed."""
formula = self.get_object()
public = request.DATA.get('public', None)
if public is None or len(request.DATA) > 1:
raise BadReq... | the_stack_v2_python_sparse | stackdio/server/formulas/api.py | WLPhoenix/stackdio | train | 0 | |
be1a32c608ef50c27edb0e9fb9f4c644b50a1837 | [
"self._duration = 0\nself._frequency = 65\nself._waves = []\nself._amplitude = 1\nself._next_notes = []",
"d = 0\nfor wave in self._waves:\n d += wave.get_duration()\nself._duration = d\nreturn d",
"a = 0\nfor wave in self._waves:\n w_a = wave._get_amplitude()\n if w_a > a:\n a = w_a\nself._ampl... | <|body_start_0|>
self._duration = 0
self._frequency = 65
self._waves = []
self._amplitude = 1
self._next_notes = []
<|end_body_0|>
<|body_start_1|>
d = 0
for wave in self._waves:
d += wave.get_duration()
self._duration = d
return d
<|e... | A Holophonor is an instrument === Attributes === _frequency: fundamental frequency of the instrument in Hz. _duration: duration of the wave in seconds. _waves: A list of StutterNote which make up the sound of this instrument _amplitude: Amplitude of the instrument _next_notes: Stores the next notes for the instrument. ... | Holophonor | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Holophonor:
"""A Holophonor is an instrument === Attributes === _frequency: fundamental frequency of the instrument in Hz. _duration: duration of the wave in seconds. _waves: A list of StutterNote which make up the sound of this instrument _amplitude: Amplitude of the instrument _next_notes: Stor... | stack_v2_sparse_classes_75kplus_train_065957 | 30,960 | no_license | [
{
"docstring": "Initializes an instance of class Holophonor",
"name": "__init__",
"signature": "def __init__(self) -> None"
},
{
"docstring": "Duration of this Baliset",
"name": "get_duration",
"signature": "def get_duration(self) -> float"
},
{
"docstring": "Max amplitude of thi... | 6 | stack_v2_sparse_classes_30k_train_042432 | Implement the Python class `Holophonor` described below.
Class description:
A Holophonor is an instrument === Attributes === _frequency: fundamental frequency of the instrument in Hz. _duration: duration of the wave in seconds. _waves: A list of StutterNote which make up the sound of this instrument _amplitude: Amplit... | Implement the Python class `Holophonor` described below.
Class description:
A Holophonor is an instrument === Attributes === _frequency: fundamental frequency of the instrument in Hz. _duration: duration of the wave in seconds. _waves: A list of StutterNote which make up the sound of this instrument _amplitude: Amplit... | faa08c32faae732dd04f59a73d0f71579afbe1f5 | <|skeleton|>
class Holophonor:
"""A Holophonor is an instrument === Attributes === _frequency: fundamental frequency of the instrument in Hz. _duration: duration of the wave in seconds. _waves: A list of StutterNote which make up the sound of this instrument _amplitude: Amplitude of the instrument _next_notes: Stor... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Holophonor:
"""A Holophonor is an instrument === Attributes === _frequency: fundamental frequency of the instrument in Hz. _duration: duration of the wave in seconds. _waves: A list of StutterNote which make up the sound of this instrument _amplitude: Amplitude of the instrument _next_notes: Stores the next n... | the_stack_v2_python_sparse | make_some_noise.py | a2khera/music_simulator | train | 0 |
f6400a1157a563dc0a1162d9bed14478f096afaa | [
"super(ReluNet, self).__init__()\nself.input_dim = input_dim\nself.output_dim = output_dim\nself.hidden_dim = hidden_dim\nself.num_layers = num_layers\nself.num_epochs = num_epochs\nself.threshold = threshold\nself.learning_rate = learning_rate\nself.layers = nn.ModuleList()\nself.layers.append(nn.Linear(input_dim,... | <|body_start_0|>
super(ReluNet, self).__init__()
self.input_dim = input_dim
self.output_dim = output_dim
self.hidden_dim = hidden_dim
self.num_layers = num_layers
self.num_epochs = num_epochs
self.threshold = threshold
self.learning_rate = learning_rate
... | Fully connected neural network with relu activation | ReluNet | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ReluNet:
"""Fully connected neural network with relu activation"""
def __init__(self, input_dim, output_dim, hidden_dim=100, num_layers=2, num_epochs=100, learning_rate=0.001, threshold=0.1):
"""Initilize a Neural Network with Relu activation Args: input_dim: int -- dimension of the ... | stack_v2_sparse_classes_75kplus_train_065958 | 4,882 | no_license | [
{
"docstring": "Initilize a Neural Network with Relu activation Args: input_dim: int -- dimension of the input feature output_dim: int -- dimension of the output feature hidden_dim: int -- number of hidden units at each layer num_layers: int -- number of hidden layers num_epochs: int -- number of epochs to trai... | 5 | stack_v2_sparse_classes_30k_train_018955 | Implement the Python class `ReluNet` described below.
Class description:
Fully connected neural network with relu activation
Method signatures and docstrings:
- def __init__(self, input_dim, output_dim, hidden_dim=100, num_layers=2, num_epochs=100, learning_rate=0.001, threshold=0.1): Initilize a Neural Network with ... | Implement the Python class `ReluNet` described below.
Class description:
Fully connected neural network with relu activation
Method signatures and docstrings:
- def __init__(self, input_dim, output_dim, hidden_dim=100, num_layers=2, num_epochs=100, learning_rate=0.001, threshold=0.1): Initilize a Neural Network with ... | d7e651024b07587b46497183d90934561a4839e2 | <|skeleton|>
class ReluNet:
"""Fully connected neural network with relu activation"""
def __init__(self, input_dim, output_dim, hidden_dim=100, num_layers=2, num_epochs=100, learning_rate=0.001, threshold=0.1):
"""Initilize a Neural Network with Relu activation Args: input_dim: int -- dimension of the ... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class ReluNet:
"""Fully connected neural network with relu activation"""
def __init__(self, input_dim, output_dim, hidden_dim=100, num_layers=2, num_epochs=100, learning_rate=0.001, threshold=0.1):
"""Initilize a Neural Network with Relu activation Args: input_dim: int -- dimension of the input feature... | the_stack_v2_python_sparse | model/relunet.py | SSF-climate/SSF | train | 7 |
281c9f4ef4996b25288527e8102f4afdc821cdfe | [
"parameters = []\nfor group in groups:\n parameters += [param for param in group]\nsuper().__init__(parameters, lrate=lrate, std_optim=std_optim)\nself._groups = groups\nself._update_count = 0",
"if self._std_optim is not None:\n self._std_optim.step()\nif self._update_count >= len(self._groups):\n self.... | <|body_start_0|>
parameters = []
for group in groups:
parameters += [param for param in group]
super().__init__(parameters, lrate=lrate, std_optim=std_optim)
self._groups = groups
self._update_count = 0
<|end_body_0|>
<|body_start_1|>
if self._std_optim is no... | Optimizer that update iteratively groups of parameters. This optimizer is suited for model like PPCA which cannot estimate the gradient of all its paramaters at once. Example: >>> # Assume "model" is a BayesianModel to be trained and X is >>> # the dataset. >>> elbo_fn = beer.EvidenceLowerBound(len(X)) >>> optim = beer... | BayesianModelCoordinateAscentOptimizer | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BayesianModelCoordinateAscentOptimizer:
"""Optimizer that update iteratively groups of parameters. This optimizer is suited for model like PPCA which cannot estimate the gradient of all its paramaters at once. Example: >>> # Assume "model" is a BayesianModel to be trained and X is >>> # the datas... | stack_v2_sparse_classes_75kplus_train_065959 | 13,212 | permissive | [
{
"docstring": "Args: ... (list): N List of ``BayesianParameter``. to be updated separately. lrate (float): learning rate. std_optim (``torch.optim.Optimizer``): Optimizer for non-Bayesian parameters (i.e. standard ``pytorch`` parameters)",
"name": "__init__",
"signature": "def __init__(self, groups, lr... | 2 | null | Implement the Python class `BayesianModelCoordinateAscentOptimizer` described below.
Class description:
Optimizer that update iteratively groups of parameters. This optimizer is suited for model like PPCA which cannot estimate the gradient of all its paramaters at once. Example: >>> # Assume "model" is a BayesianModel... | Implement the Python class `BayesianModelCoordinateAscentOptimizer` described below.
Class description:
Optimizer that update iteratively groups of parameters. This optimizer is suited for model like PPCA which cannot estimate the gradient of all its paramaters at once. Example: >>> # Assume "model" is a BayesianModel... | 6fe968c7ca4864437890aa6bd705755c2580696e | <|skeleton|>
class BayesianModelCoordinateAscentOptimizer:
"""Optimizer that update iteratively groups of parameters. This optimizer is suited for model like PPCA which cannot estimate the gradient of all its paramaters at once. Example: >>> # Assume "model" is a BayesianModel to be trained and X is >>> # the datas... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class BayesianModelCoordinateAscentOptimizer:
"""Optimizer that update iteratively groups of parameters. This optimizer is suited for model like PPCA which cannot estimate the gradient of all its paramaters at once. Example: >>> # Assume "model" is a BayesianModel to be trained and X is >>> # the dataset. >>> elbo_... | the_stack_v2_python_sparse | beer/vbi.py | bolajiy/beer | train | 0 |
5a0aa3c571a2b2c460401d7837dc71a3d28d2ad7 | [
"self.transformed_collection: list[TiTransform] = []\nfor ti_dict in self.ti_dicts:\n self.transformed_collection.append(TiTransform(ti_dict, self.transforms))",
"self.process()\nbatch = {'group': [], 'indicator': []}\nself.log.trace(f'feature=ti-transform-batch, ti-count={len(self.transformed_collection)}')\n... | <|body_start_0|>
self.transformed_collection: list[TiTransform] = []
for ti_dict in self.ti_dicts:
self.transformed_collection.append(TiTransform(ti_dict, self.transforms))
<|end_body_0|>
<|body_start_1|>
self.process()
batch = {'group': [], 'indicator': []}
self.log... | Mappings | TiTransforms | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TiTransforms:
"""Mappings"""
def process(self):
"""Process the mapping."""
<|body_0|>
def batch(self) -> dict:
"""Return the data in batch format."""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
self.transformed_collection: list[TiTransform] = [... | stack_v2_sparse_classes_75kplus_train_065960 | 6,246 | permissive | [
{
"docstring": "Process the mapping.",
"name": "process",
"signature": "def process(self)"
},
{
"docstring": "Return the data in batch format.",
"name": "batch",
"signature": "def batch(self) -> dict"
}
] | 2 | stack_v2_sparse_classes_30k_train_026025 | Implement the Python class `TiTransforms` described below.
Class description:
Mappings
Method signatures and docstrings:
- def process(self): Process the mapping.
- def batch(self) -> dict: Return the data in batch format. | Implement the Python class `TiTransforms` described below.
Class description:
Mappings
Method signatures and docstrings:
- def process(self): Process the mapping.
- def batch(self) -> dict: Return the data in batch format.
<|skeleton|>
class TiTransforms:
"""Mappings"""
def process(self):
"""Process... | 30dc147e40d63d1082ec2a5e6c62005b60c29c37 | <|skeleton|>
class TiTransforms:
"""Mappings"""
def process(self):
"""Process the mapping."""
<|body_0|>
def batch(self) -> dict:
"""Return the data in batch format."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class TiTransforms:
"""Mappings"""
def process(self):
"""Process the mapping."""
self.transformed_collection: list[TiTransform] = []
for ti_dict in self.ti_dicts:
self.transformed_collection.append(TiTransform(ti_dict, self.transforms))
def batch(self) -> dict:
... | the_stack_v2_python_sparse | tcex/api/tc/ti_transform/ti_transform.py | ThreatConnect-Inc/tcex | train | 24 |
60e3f8764853e5156982855ae193d1cdba1ed48f | [
"super(Instance, self).__init__(resource_id=instance_id, resource_type=resource.ResourceType.INSTANCE, name=kwargs.get('name'), display_name=kwargs.get('name'), parent=parent, locations=kwargs.get('locations'))\nif parent and parent.type != 'project':\n raise TypeError('Unexpected parent type: got {}, want proje... | <|body_start_0|>
super(Instance, self).__init__(resource_id=instance_id, resource_type=resource.ResourceType.INSTANCE, name=kwargs.get('name'), display_name=kwargs.get('name'), parent=parent, locations=kwargs.get('locations'))
if parent and parent.type != 'project':
raise TypeError('Unexpect... | Represents Instance resource. | Instance | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Instance:
"""Represents Instance resource."""
def __init__(self, instance_id, parent=None, **kwargs):
"""Instance resource. Args: instance_id (str): id of the instance. parent (Resource): Parent resource of this instance. Should be a project. **kwargs (dict): The object's attributes.... | stack_v2_sparse_classes_75kplus_train_065961 | 10,814 | permissive | [
{
"docstring": "Instance resource. Args: instance_id (str): id of the instance. parent (Resource): Parent resource of this instance. Should be a project. **kwargs (dict): The object's attributes. Raises: TypeError: If unexpected parent type.",
"name": "__init__",
"signature": "def __init__(self, instanc... | 6 | stack_v2_sparse_classes_30k_train_001301 | Implement the Python class `Instance` described below.
Class description:
Represents Instance resource.
Method signatures and docstrings:
- def __init__(self, instance_id, parent=None, **kwargs): Instance resource. Args: instance_id (str): id of the instance. parent (Resource): Parent resource of this instance. Shoul... | Implement the Python class `Instance` described below.
Class description:
Represents Instance resource.
Method signatures and docstrings:
- def __init__(self, instance_id, parent=None, **kwargs): Instance resource. Args: instance_id (str): id of the instance. parent (Resource): Parent resource of this instance. Shoul... | d4421afa50a17ed47cbebe942044ebab3720e0f5 | <|skeleton|>
class Instance:
"""Represents Instance resource."""
def __init__(self, instance_id, parent=None, **kwargs):
"""Instance resource. Args: instance_id (str): id of the instance. parent (Resource): Parent resource of this instance. Should be a project. **kwargs (dict): The object's attributes.... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Instance:
"""Represents Instance resource."""
def __init__(self, instance_id, parent=None, **kwargs):
"""Instance resource. Args: instance_id (str): id of the instance. parent (Resource): Parent resource of this instance. Should be a project. **kwargs (dict): The object's attributes. Raises: Type... | the_stack_v2_python_sparse | google/cloud/forseti/common/gcp_type/instance.py | kevensen/forseti-security | train | 1 |
61777ce76fa0aa407cfce3f0c6fc81db02d04523 | [
"super(WeightQuantizerLS2, self).__init__()\nself.register_buffer('v1', torch.tensor([0.0] * size))\nself.register_buffer('v2', torch.tensor([0.0] * size))",
"if self.training:\n v1, v2, w_q = quantization.quantizer_ls_2(w, skip=skip)\n self.v1.copy_(v1)\n self.v2.copy_(v2)\nelse:\n _, _, w_q = quanti... | <|body_start_0|>
super(WeightQuantizerLS2, self).__init__()
self.register_buffer('v1', torch.tensor([0.0] * size))
self.register_buffer('v2', torch.tensor([0.0] * size))
<|end_body_0|>
<|body_start_1|>
if self.training:
v1, v2, w_q = quantization.quantizer_ls_2(w, skip=skip)... | Weight quantizer using least squares, 2 bits. In training mode, the optimal scalars are computed and cached. In eval mode, the cached scalars are used to compute the quantization. | WeightQuantizerLS2 | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class WeightQuantizerLS2:
"""Weight quantizer using least squares, 2 bits. In training mode, the optimal scalars are computed and cached. In eval mode, the cached scalars are used to compute the quantization."""
def __init__(self, size: int) -> None:
"""Construct a weight quantizer using l... | stack_v2_sparse_classes_75kplus_train_065962 | 4,037 | no_license | [
{
"docstring": "Construct a weight quantizer using least squares with 2 bits.",
"name": "__init__",
"signature": "def __init__(self, size: int) -> None"
},
{
"docstring": "Forward pass of quantizing weight using least squares 2 bits.",
"name": "forward",
"signature": "def forward(self, w... | 2 | stack_v2_sparse_classes_30k_train_051005 | Implement the Python class `WeightQuantizerLS2` described below.
Class description:
Weight quantizer using least squares, 2 bits. In training mode, the optimal scalars are computed and cached. In eval mode, the cached scalars are used to compute the quantization.
Method signatures and docstrings:
- def __init__(self,... | Implement the Python class `WeightQuantizerLS2` described below.
Class description:
Weight quantizer using least squares, 2 bits. In training mode, the optimal scalars are computed and cached. In eval mode, the cached scalars are used to compute the quantization.
Method signatures and docstrings:
- def __init__(self,... | 39197b5f54cd84ff35022c851dd2dcb753ca6b89 | <|skeleton|>
class WeightQuantizerLS2:
"""Weight quantizer using least squares, 2 bits. In training mode, the optimal scalars are computed and cached. In eval mode, the cached scalars are used to compute the quantization."""
def __init__(self, size: int) -> None:
"""Construct a weight quantizer using l... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class WeightQuantizerLS2:
"""Weight quantizer using least squares, 2 bits. In training mode, the optimal scalars are computed and cached. In eval mode, the cached scalars are used to compute the quantization."""
def __init__(self, size: int) -> None:
"""Construct a weight quantizer using least squares ... | the_stack_v2_python_sparse | quant/binary/weight_quantization.py | mikechen66/ml-quant | train | 0 |
2b88076fae52ade5de17e2de5d7a3fd09a1773a7 | [
"if rowIndex < 0:\n return []\nbuild = [0 for i in range(rowIndex + 1)]\nbuild[0] = 1\nfor row in range(1, rowIndex + 1):\n pre = build[0]\n for i in range(1, row + 1):\n temp = build[i]\n build[i] += pre\n pre = temp\n build[row] = 1\nreturn build",
"res = [1] * (rowIndex + 1)\nf... | <|body_start_0|>
if rowIndex < 0:
return []
build = [0 for i in range(rowIndex + 1)]
build[0] = 1
for row in range(1, rowIndex + 1):
pre = build[0]
for i in range(1, row + 1):
temp = build[i]
build[i] += pre
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def getRow(self, rowIndex):
""":type rowIndex: int :rtype: List[int]"""
<|body_0|>
def getRow2(self, rowIndex):
""":type rowIndex: int :rtype: List[int]"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
if rowIndex < 0:
return []... | stack_v2_sparse_classes_75kplus_train_065963 | 1,538 | no_license | [
{
"docstring": ":type rowIndex: int :rtype: List[int]",
"name": "getRow",
"signature": "def getRow(self, rowIndex)"
},
{
"docstring": ":type rowIndex: int :rtype: List[int]",
"name": "getRow2",
"signature": "def getRow2(self, rowIndex)"
}
] | 2 | stack_v2_sparse_classes_30k_train_046490 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def getRow(self, rowIndex): :type rowIndex: int :rtype: List[int]
- def getRow2(self, rowIndex): :type rowIndex: int :rtype: List[int] | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def getRow(self, rowIndex): :type rowIndex: int :rtype: List[int]
- def getRow2(self, rowIndex): :type rowIndex: int :rtype: List[int]
<|skeleton|>
class Solution:
def getR... | 635af6e22aa8eef8e7920a585d43a45a891a8157 | <|skeleton|>
class Solution:
def getRow(self, rowIndex):
""":type rowIndex: int :rtype: List[int]"""
<|body_0|>
def getRow2(self, rowIndex):
""":type rowIndex: int :rtype: List[int]"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Solution:
def getRow(self, rowIndex):
""":type rowIndex: int :rtype: List[int]"""
if rowIndex < 0:
return []
build = [0 for i in range(rowIndex + 1)]
build[0] = 1
for row in range(1, rowIndex + 1):
pre = build[0]
for i in range(1, row... | the_stack_v2_python_sparse | code119PascalsTriangleII.py | cybelewang/leetcode-python | train | 0 | |
6bf8fbfd054fa302153423bdcd86d6a2603572ea | [
"try:\n if mode == 'r':\n if is_py2():\n return open(str(name), 'rb', buffering=1)\n else:\n kwargs = dict(buffering=1, newline='', encoding=encoding)\n return open(str(name), 'r', **kwargs)\n elif mode == 'w':\n if is_py2():\n return open(str(n... | <|body_start_0|>
try:
if mode == 'r':
if is_py2():
return open(str(name), 'rb', buffering=1)
else:
kwargs = dict(buffering=1, newline='', encoding=encoding)
return open(str(name), 'r', **kwargs)
e... | Read and write CSV files to and from lists of dictionaries | CSVAdapter | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CSVAdapter:
"""Read and write CSV files to and from lists of dictionaries"""
def open_csv_file(name, mode, encoding=None):
""":type name: str :type mode: str :type encoding: str, but ignored in py2 :rtype file"""
<|body_0|>
def guess_delimiter_from_filename(filename):
... | stack_v2_sparse_classes_75kplus_train_065964 | 7,527 | permissive | [
{
"docstring": ":type name: str :type mode: str :type encoding: str, but ignored in py2 :rtype file",
"name": "open_csv_file",
"signature": "def open_csv_file(name, mode, encoding=None)"
},
{
"docstring": ":type filename :rtype str",
"name": "guess_delimiter_from_filename",
"signature": ... | 4 | stack_v2_sparse_classes_30k_val_002486 | Implement the Python class `CSVAdapter` described below.
Class description:
Read and write CSV files to and from lists of dictionaries
Method signatures and docstrings:
- def open_csv_file(name, mode, encoding=None): :type name: str :type mode: str :type encoding: str, but ignored in py2 :rtype file
- def guess_delim... | Implement the Python class `CSVAdapter` described below.
Class description:
Read and write CSV files to and from lists of dictionaries
Method signatures and docstrings:
- def open_csv_file(name, mode, encoding=None): :type name: str :type mode: str :type encoding: str, but ignored in py2 :rtype file
- def guess_delim... | 15a228b6f3d4b12efe213eec1bf599c8a56840a3 | <|skeleton|>
class CSVAdapter:
"""Read and write CSV files to and from lists of dictionaries"""
def open_csv_file(name, mode, encoding=None):
""":type name: str :type mode: str :type encoding: str, but ignored in py2 :rtype file"""
<|body_0|>
def guess_delimiter_from_filename(filename):
... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class CSVAdapter:
"""Read and write CSV files to and from lists of dictionaries"""
def open_csv_file(name, mode, encoding=None):
""":type name: str :type mode: str :type encoding: str, but ignored in py2 :rtype file"""
try:
if mode == 'r':
if is_py2():
... | the_stack_v2_python_sparse | user_sync/helper.py | adobe-apiplatform/user-sync.py | train | 96 |
c76ce85112c52bafde6e529ee6819b36ee420489 | [
"self.query = query or pywikibot.input('Please enter the search query:')\nif site is None:\n site = pywikibot.Site()\nself.site = site\nself._google_query = None",
"try:\n import google\nexcept ImportError:\n pywikibot.error(\"generator GoogleSearchPageGenerator depends on package 'google'.\\nTo install,... | <|body_start_0|>
self.query = query or pywikibot.input('Please enter the search query:')
if site is None:
site = pywikibot.Site()
self.site = site
self._google_query = None
<|end_body_0|>
<|body_start_1|>
try:
import google
except ImportError:
... | Page generator using Google search results. To use this generator, you need to install the package 'google': :py:obj:`https://pypi.org/project/google` This package has been available since 2010, hosted on GitHub since 2012, and provided by PyPI since 2013. As there are concerns about Google's Terms of Service, this gen... | GoogleSearchPageGenerator | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GoogleSearchPageGenerator:
"""Page generator using Google search results. To use this generator, you need to install the package 'google': :py:obj:`https://pypi.org/project/google` This package has been available since 2010, hosted on GitHub since 2012, and provided by PyPI since 2013. As there a... | stack_v2_sparse_classes_75kplus_train_065965 | 43,909 | permissive | [
{
"docstring": "Initializer. :param site: Site for generator results.",
"name": "__init__",
"signature": "def __init__(self, query: Optional[str]=None, site: OPT_SITE_TYPE=None) -> None"
},
{
"docstring": "Perform a query using python package 'google'. The terms of service as at June 2014 give t... | 3 | stack_v2_sparse_classes_30k_train_021061 | Implement the Python class `GoogleSearchPageGenerator` described below.
Class description:
Page generator using Google search results. To use this generator, you need to install the package 'google': :py:obj:`https://pypi.org/project/google` This package has been available since 2010, hosted on GitHub since 2012, and ... | Implement the Python class `GoogleSearchPageGenerator` described below.
Class description:
Page generator using Google search results. To use this generator, you need to install the package 'google': :py:obj:`https://pypi.org/project/google` This package has been available since 2010, hosted on GitHub since 2012, and ... | 5c01e6bfcd328bc6eae643e661f1a0ae57612808 | <|skeleton|>
class GoogleSearchPageGenerator:
"""Page generator using Google search results. To use this generator, you need to install the package 'google': :py:obj:`https://pypi.org/project/google` This package has been available since 2010, hosted on GitHub since 2012, and provided by PyPI since 2013. As there a... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class GoogleSearchPageGenerator:
"""Page generator using Google search results. To use this generator, you need to install the package 'google': :py:obj:`https://pypi.org/project/google` This package has been available since 2010, hosted on GitHub since 2012, and provided by PyPI since 2013. As there are concerns a... | the_stack_v2_python_sparse | pywikibot/pagegenerators/_generators.py | wikimedia/pywikibot | train | 432 |
cb4af0bd557b6baba0f6425869bd52d119c005a1 | [
"pagination = context_utils.get_pagination()\nquery = LoginLogs.query.order_by(LoginLogs.id.desc())\npagination = query.paginate(pagination.page_no, pagination.page_size)\npagination = pagination_utils.get_pagination_sqlalchemy(pagination)\nreturn pagination",
"context = db.get_app().app_context()\n\ndef do_save(... | <|body_start_0|>
pagination = context_utils.get_pagination()
query = LoginLogs.query.order_by(LoginLogs.id.desc())
pagination = query.paginate(pagination.page_no, pagination.page_size)
pagination = pagination_utils.get_pagination_sqlalchemy(pagination)
return pagination
<|end_bod... | 用户登录业务接口 | LoginLogsService | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LoginLogsService:
"""用户登录业务接口"""
def get_page_loginlogs():
"""返回分页对象"""
<|body_0|>
def add_loginlogs(loginlogs):
"""添加登录日志"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
pagination = context_utils.get_pagination()
query = LoginLogs.quer... | stack_v2_sparse_classes_75kplus_train_065966 | 1,444 | no_license | [
{
"docstring": "返回分页对象",
"name": "get_page_loginlogs",
"signature": "def get_page_loginlogs()"
},
{
"docstring": "添加登录日志",
"name": "add_loginlogs",
"signature": "def add_loginlogs(loginlogs)"
}
] | 2 | null | Implement the Python class `LoginLogsService` described below.
Class description:
用户登录业务接口
Method signatures and docstrings:
- def get_page_loginlogs(): 返回分页对象
- def add_loginlogs(loginlogs): 添加登录日志 | Implement the Python class `LoginLogsService` described below.
Class description:
用户登录业务接口
Method signatures and docstrings:
- def get_page_loginlogs(): 返回分页对象
- def add_loginlogs(loginlogs): 添加登录日志
<|skeleton|>
class LoginLogsService:
"""用户登录业务接口"""
def get_page_loginlogs():
"""返回分页对象"""
<|... | a3027259b2f5275f81ab530a3fdf53c97c23f863 | <|skeleton|>
class LoginLogsService:
"""用户登录业务接口"""
def get_page_loginlogs():
"""返回分页对象"""
<|body_0|>
def add_loginlogs(loginlogs):
"""添加登录日志"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class LoginLogsService:
"""用户登录业务接口"""
def get_page_loginlogs():
"""返回分页对象"""
pagination = context_utils.get_pagination()
query = LoginLogs.query.order_by(LoginLogs.id.desc())
pagination = query.paginate(pagination.page_no, pagination.page_size)
pagination = pagination_u... | the_stack_v2_python_sparse | blog/services/logs_service.py | foxandyhu/projects | train | 0 |
f2b678dc1aecc282ec2f10e6f05cd72091c573a8 | [
"source_path = PathConvertor.build_path_without_trailing_slash(local_path)\nbasename = os.path.basename(source_path)\nfolder_name = os.path.expanduser(os.path.dirname(source_path))\ntarget_dir = PathConvertor.build_path_with_trailing_slash(remote_dir)\nmkdir_script = f\"ssh -o StrictHostKeyChecking=no -p {node_ssh_... | <|body_start_0|>
source_path = PathConvertor.build_path_without_trailing_slash(local_path)
basename = os.path.basename(source_path)
folder_name = os.path.expanduser(os.path.dirname(source_path))
target_dir = PathConvertor.build_path_with_trailing_slash(remote_dir)
mkdir_script = ... | Synchronizer class for files. | FileSynchronizer | [
"LicenseRef-scancode-generic-cla",
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FileSynchronizer:
"""Synchronizer class for files."""
def copy_files_to_node(local_path: str, remote_dir: str, node_username: str, node_hostname: str, node_ssh_port: int) -> None:
"""Copy local files to node, automatically create folder if not exist. Args: local_path (str): path of t... | stack_v2_sparse_classes_75kplus_train_065967 | 6,685 | permissive | [
{
"docstring": "Copy local files to node, automatically create folder if not exist. Args: local_path (str): path of the local file. remote_dir (str): dir for remote files. node_username (str): username of the vm. node_hostname (str): hostname of the vm. node_ssh_port (int): port of the ssh connection.",
"na... | 3 | null | Implement the Python class `FileSynchronizer` described below.
Class description:
Synchronizer class for files.
Method signatures and docstrings:
- def copy_files_to_node(local_path: str, remote_dir: str, node_username: str, node_hostname: str, node_ssh_port: int) -> None: Copy local files to node, automatically crea... | Implement the Python class `FileSynchronizer` described below.
Class description:
Synchronizer class for files.
Method signatures and docstrings:
- def copy_files_to_node(local_path: str, remote_dir: str, node_username: str, node_hostname: str, node_ssh_port: int) -> None: Copy local files to node, automatically crea... | b3c6a589ad9036b03221e776a6929b2bc1eb4680 | <|skeleton|>
class FileSynchronizer:
"""Synchronizer class for files."""
def copy_files_to_node(local_path: str, remote_dir: str, node_username: str, node_hostname: str, node_ssh_port: int) -> None:
"""Copy local files to node, automatically create folder if not exist. Args: local_path (str): path of t... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class FileSynchronizer:
"""Synchronizer class for files."""
def copy_files_to_node(local_path: str, remote_dir: str, node_username: str, node_hostname: str, node_ssh_port: int) -> None:
"""Copy local files to node, automatically create folder if not exist. Args: local_path (str): path of the local file... | the_stack_v2_python_sparse | maro/cli/grass/utils/file_synchronizer.py | microsoft/maro | train | 764 |
b7430d53b55a61c1e7153ec971d49b72b8a0b563 | [
"self.n_classes = number_class\nself.is_training = is_training\nself.is_simplified = is_simplified\nself.dropout = dropout",
"if not self.is_simplified:\n net, _ = unet.unet(input_batch, self.n_classes, is_training=is_training, dropout=dropout, weight_decay=0.0005)\nelse:\n net, _ = simplified_unet.unet(inp... | <|body_start_0|>
self.n_classes = number_class
self.is_training = is_training
self.is_simplified = is_simplified
self.dropout = dropout
<|end_body_0|>
<|body_start_1|>
if not self.is_simplified:
net, _ = unet.unet(input_batch, self.n_classes, is_training=is_training,... | UnetModel | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class UnetModel:
def __init__(self, number_class=3, is_training=True, is_simplified=False, dropout=True):
"""Create the model"""
<|body_0|>
def _create_network(self, input_batch, dropout=False, is_training=True):
"""Args: input_batch: batch of pre-processed images. keep_pr... | stack_v2_sparse_classes_75kplus_train_065968 | 4,646 | permissive | [
{
"docstring": "Create the model",
"name": "__init__",
"signature": "def __init__(self, number_class=3, is_training=True, is_simplified=False, dropout=True)"
},
{
"docstring": "Args: input_batch: batch of pre-processed images. keep_prob: probability of keeping neurons intact. Returns: A downsamp... | 5 | stack_v2_sparse_classes_30k_val_000168 | Implement the Python class `UnetModel` described below.
Class description:
Implement the UnetModel class.
Method signatures and docstrings:
- def __init__(self, number_class=3, is_training=True, is_simplified=False, dropout=True): Create the model
- def _create_network(self, input_batch, dropout=False, is_training=Tr... | Implement the Python class `UnetModel` described below.
Class description:
Implement the UnetModel class.
Method signatures and docstrings:
- def __init__(self, number_class=3, is_training=True, is_simplified=False, dropout=True): Create the model
- def _create_network(self, input_batch, dropout=False, is_training=Tr... | 57904544c6d6b43dcd5937afeb474c0a47456d98 | <|skeleton|>
class UnetModel:
def __init__(self, number_class=3, is_training=True, is_simplified=False, dropout=True):
"""Create the model"""
<|body_0|>
def _create_network(self, input_batch, dropout=False, is_training=True):
"""Args: input_batch: batch of pre-processed images. keep_pr... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class UnetModel:
def __init__(self, number_class=3, is_training=True, is_simplified=False, dropout=True):
"""Create the model"""
self.n_classes = number_class
self.is_training = is_training
self.is_simplified = is_simplified
self.dropout = dropout
def _create_network(sel... | the_stack_v2_python_sparse | models/model_unet.py | cliang1453/IHC-based-labels-generation-and-semantic-segmentation-for-lung-cancer | train | 0 | |
7c05fb6bdc88876cce6ca8e3aac44c7a98c1003a | [
"if MUTED:\n return MutedSound()\nelse:\n return pygame.mixer.Sound(os.path.join('data', 'sounds', name))",
"if not MUTED and False:\n pygame.mixer.music.load(os.path.join('data', 'music', name))\n pygame.mixer.music.play(-1)"
] | <|body_start_0|>
if MUTED:
return MutedSound()
else:
return pygame.mixer.Sound(os.path.join('data', 'sounds', name))
<|end_body_0|>
<|body_start_1|>
if not MUTED and False:
pygame.mixer.music.load(os.path.join('data', 'music', name))
pygame.mixer.... | Handle sounds | _Sounds | [
"BSD-3-Clause",
"LicenseRef-scancode-unknown-license-reference"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class _Sounds:
"""Handle sounds"""
def getSound(self, name):
"""Return a new sound"""
<|body_0|>
def playMusic(self, name):
"""Return a new sound"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
if MUTED:
return MutedSound()
else:
... | stack_v2_sparse_classes_75kplus_train_065969 | 638 | permissive | [
{
"docstring": "Return a new sound",
"name": "getSound",
"signature": "def getSound(self, name)"
},
{
"docstring": "Return a new sound",
"name": "playMusic",
"signature": "def playMusic(self, name)"
}
] | 2 | stack_v2_sparse_classes_30k_train_045780 | Implement the Python class `_Sounds` described below.
Class description:
Handle sounds
Method signatures and docstrings:
- def getSound(self, name): Return a new sound
- def playMusic(self, name): Return a new sound | Implement the Python class `_Sounds` described below.
Class description:
Handle sounds
Method signatures and docstrings:
- def getSound(self, name): Return a new sound
- def playMusic(self, name): Return a new sound
<|skeleton|>
class _Sounds:
"""Handle sounds"""
def getSound(self, name):
"""Return ... | cee7e4b5dc28c57a6c912852827652b5f51005ae | <|skeleton|>
class _Sounds:
"""Handle sounds"""
def getSound(self, name):
"""Return a new sound"""
<|body_0|>
def playMusic(self, name):
"""Return a new sound"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class _Sounds:
"""Handle sounds"""
def getSound(self, name):
"""Return a new sound"""
if MUTED:
return MutedSound()
else:
return pygame.mixer.Sound(os.path.join('data', 'sounds', name))
def playMusic(self, name):
"""Return a new sound"""
if n... | the_stack_v2_python_sparse | IE_games_8/unvisible-0.4.0/unvisible/sound.py | IndexErrorCoders/PygamesCompilation | train | 2 |
e95f4924ef775cac6b7c3da500adcddf85641b37 | [
"super().__init__()\nself.hidden_layers = nn.ModuleList([nn.Linear(n_input, hiddens[0])])\nhidden_sizes = zip(hiddens[:-1], hiddens[1:])\nself.hidden_layers.extend([nn.Linear(h1, h2) for h1, h2 in hidden_sizes])\nself.output = nn.Linear(hiddens[-1], n_output)\nself.dropout = nn.Dropout(p=p_dropout)",
"for each_hi... | <|body_start_0|>
super().__init__()
self.hidden_layers = nn.ModuleList([nn.Linear(n_input, hiddens[0])])
hidden_sizes = zip(hiddens[:-1], hiddens[1:])
self.hidden_layers.extend([nn.Linear(h1, h2) for h1, h2 in hidden_sizes])
self.output = nn.Linear(hiddens[-1], n_output)
... | Network | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Network:
def __init__(self, n_input, n_output, hiddens, p_dropout=0.2):
"""Initiate a full connected neural network with hidden layers. Arguments: n_input: int, size of input layer n_output: int, size of output layer hiddens: list of integers, sizes of hidden layers p_dropout: float, dro... | stack_v2_sparse_classes_75kplus_train_065970 | 5,551 | no_license | [
{
"docstring": "Initiate a full connected neural network with hidden layers. Arguments: n_input: int, size of input layer n_output: int, size of output layer hiddens: list of integers, sizes of hidden layers p_dropout: float, drop out probablity",
"name": "__init__",
"signature": "def __init__(self, n_i... | 2 | stack_v2_sparse_classes_30k_train_026031 | Implement the Python class `Network` described below.
Class description:
Implement the Network class.
Method signatures and docstrings:
- def __init__(self, n_input, n_output, hiddens, p_dropout=0.2): Initiate a full connected neural network with hidden layers. Arguments: n_input: int, size of input layer n_output: i... | Implement the Python class `Network` described below.
Class description:
Implement the Network class.
Method signatures and docstrings:
- def __init__(self, n_input, n_output, hiddens, p_dropout=0.2): Initiate a full connected neural network with hidden layers. Arguments: n_input: int, size of input layer n_output: i... | 707cbc8150d123e8b49a450e20fb36e07088dfc5 | <|skeleton|>
class Network:
def __init__(self, n_input, n_output, hiddens, p_dropout=0.2):
"""Initiate a full connected neural network with hidden layers. Arguments: n_input: int, size of input layer n_output: int, size of output layer hiddens: list of integers, sizes of hidden layers p_dropout: float, dro... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Network:
def __init__(self, n_input, n_output, hiddens, p_dropout=0.2):
"""Initiate a full connected neural network with hidden layers. Arguments: n_input: int, size of input layer n_output: int, size of output layer hiddens: list of integers, sizes of hidden layers p_dropout: float, drop out probabli... | the_stack_v2_python_sparse | fc_model.py | str1ngth3ory/Flower-Image-Classifier | train | 0 | |
76e5e12e74481c6a6be66a0d8c481505c0203332 | [
"if relevant_docs is None or docs is None:\n raise Exception('Invalid Input: Inputs cannot be None!')\nself.docs = docs\nself.relevant_docs = relevant_docs\nself.retrieved_docs = retrieved_docs\nself.query = query\nself.para_section = pickle.load(open('process_data/paragraph_article_section.pkl', 'rb'))",
"tit... | <|body_start_0|>
if relevant_docs is None or docs is None:
raise Exception('Invalid Input: Inputs cannot be None!')
self.docs = docs
self.relevant_docs = relevant_docs
self.retrieved_docs = retrieved_docs
self.query = query
self.para_section = pickle.load(open... | HTMLCreator | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class HTMLCreator:
def __init__(self, docs, retrieved_docs, relevant_docs, query):
""":param docs: a dict containing docid: str, where str contains the paragraph :param relevant_docs: a list of docid sorted in order to generate the site"""
<|body_0|>
def generate_wiki(self, title=... | stack_v2_sparse_classes_75kplus_train_065971 | 2,290 | no_license | [
{
"docstring": ":param docs: a dict containing docid: str, where str contains the paragraph :param relevant_docs: a list of docid sorted in order to generate the site",
"name": "__init__",
"signature": "def __init__(self, docs, retrieved_docs, relevant_docs, query)"
},
{
"docstring": "Generates ... | 2 | stack_v2_sparse_classes_30k_train_021565 | Implement the Python class `HTMLCreator` described below.
Class description:
Implement the HTMLCreator class.
Method signatures and docstrings:
- def __init__(self, docs, retrieved_docs, relevant_docs, query): :param docs: a dict containing docid: str, where str contains the paragraph :param relevant_docs: a list of ... | Implement the Python class `HTMLCreator` described below.
Class description:
Implement the HTMLCreator class.
Method signatures and docstrings:
- def __init__(self, docs, retrieved_docs, relevant_docs, query): :param docs: a dict containing docid: str, where str contains the paragraph :param relevant_docs: a list of ... | 5c5d77b2f341b4579d4d096b7531e25296850c3a | <|skeleton|>
class HTMLCreator:
def __init__(self, docs, retrieved_docs, relevant_docs, query):
""":param docs: a dict containing docid: str, where str contains the paragraph :param relevant_docs: a list of docid sorted in order to generate the site"""
<|body_0|>
def generate_wiki(self, title=... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class HTMLCreator:
def __init__(self, docs, retrieved_docs, relevant_docs, query):
""":param docs: a dict containing docid: str, where str contains the paragraph :param relevant_docs: a list of docid sorted in order to generate the site"""
if relevant_docs is None or docs is None:
raise ... | the_stack_v2_python_sparse | html_creator/site_creator.py | heatherwan/IR_Complex_Question_Retrieval | train | 0 | |
669d3301c1296ec8dc15a77d44accb8540fc9ab6 | [
"arg_fields = {'token': String(required=True)}\nargs = parser.parse(arg_fields)\nvalidated_user = UserDAO().verify_token(args['token'])\nlogging.info('Successfully validated token {0} for user {1}.'.format(args['token'], validated_user))\nreturn jsonify(UserMarshal().dump(validated_user).data)",
"arg_fields = {'e... | <|body_start_0|>
arg_fields = {'token': String(required=True)}
args = parser.parse(arg_fields)
validated_user = UserDAO().verify_token(args['token'])
logging.info('Successfully validated token {0} for user {1}.'.format(args['token'], validated_user))
return jsonify(UserMarshal().... | Handles anything related to the verification token. | VerificationToken | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class VerificationToken:
"""Handles anything related to the verification token."""
def post(self):
"""Validate a user by providing the correct token."""
<|body_0|>
def put(self):
"""Regenerate the verification token."""
<|body_1|>
<|end_skeleton|>
<|body_star... | stack_v2_sparse_classes_75kplus_train_065972 | 1,487 | no_license | [
{
"docstring": "Validate a user by providing the correct token.",
"name": "post",
"signature": "def post(self)"
},
{
"docstring": "Regenerate the verification token.",
"name": "put",
"signature": "def put(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_038915 | Implement the Python class `VerificationToken` described below.
Class description:
Handles anything related to the verification token.
Method signatures and docstrings:
- def post(self): Validate a user by providing the correct token.
- def put(self): Regenerate the verification token. | Implement the Python class `VerificationToken` described below.
Class description:
Handles anything related to the verification token.
Method signatures and docstrings:
- def post(self): Validate a user by providing the correct token.
- def put(self): Regenerate the verification token.
<|skeleton|>
class Verificatio... | dcd65ade96b7239b799fe436e61dea29cb568f2e | <|skeleton|>
class VerificationToken:
"""Handles anything related to the verification token."""
def post(self):
"""Validate a user by providing the correct token."""
<|body_0|>
def put(self):
"""Regenerate the verification token."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class VerificationToken:
"""Handles anything related to the verification token."""
def post(self):
"""Validate a user by providing the correct token."""
arg_fields = {'token': String(required=True)}
args = parser.parse(arg_fields)
validated_user = UserDAO().verify_token(args['to... | the_stack_v2_python_sparse | backend/skael/skael/api/user_verify_token.py | webcat12345/skael-public | train | 2 |
5a64a2de474acdaced518ca6d74cf6f595eb0abc | [
"tile = self.a2.Tile('#')\nresult = tile.get_name()\nself.assertEqual(result, '#')",
"tile = self.a2.Tile('#')\nresult = tile.get_id()\nself.assertEqual(result, 'tile')",
"tile = self.a2.Tile('#')\nresult = tile.can_select()\nself.assertIs(result, True)",
"tile = self.a2.Tile('#', False)\nresult = tile.can_se... | <|body_start_0|>
tile = self.a2.Tile('#')
result = tile.get_name()
self.assertEqual(result, '#')
<|end_body_0|>
<|body_start_1|>
tile = self.a2.Tile('#')
result = tile.get_id()
self.assertEqual(result, 'tile')
<|end_body_1|>
<|body_start_2|>
tile = self.a2.Tile(... | TestTile | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestTile:
def test_get_name(self):
"""test Tile.get_name"""
<|body_0|>
def test_get_id(self):
"""test Tile.get_id"""
<|body_1|>
def test_can_select_default(self):
"""test Tile.can_select defaults True"""
<|body_2|>
def test_can_selec... | stack_v2_sparse_classes_75kplus_train_065973 | 23,226 | no_license | [
{
"docstring": "test Tile.get_name",
"name": "test_get_name",
"signature": "def test_get_name(self)"
},
{
"docstring": "test Tile.get_id",
"name": "test_get_id",
"signature": "def test_get_id(self)"
},
{
"docstring": "test Tile.can_select defaults True",
"name": "test_can_sel... | 6 | stack_v2_sparse_classes_30k_train_044829 | Implement the Python class `TestTile` described below.
Class description:
Implement the TestTile class.
Method signatures and docstrings:
- def test_get_name(self): test Tile.get_name
- def test_get_id(self): test Tile.get_id
- def test_can_select_default(self): test Tile.can_select defaults True
- def test_can_selec... | Implement the Python class `TestTile` described below.
Class description:
Implement the TestTile class.
Method signatures and docstrings:
- def test_get_name(self): test Tile.get_name
- def test_get_id(self): test Tile.get_id
- def test_can_select_default(self): test Tile.can_select defaults True
- def test_can_selec... | 47c1dbbca81f83e2a7f9601d367d4d563742c3f5 | <|skeleton|>
class TestTile:
def test_get_name(self):
"""test Tile.get_name"""
<|body_0|>
def test_get_id(self):
"""test Tile.get_id"""
<|body_1|>
def test_can_select_default(self):
"""test Tile.can_select defaults True"""
<|body_2|>
def test_can_selec... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class TestTile:
def test_get_name(self):
"""test Tile.get_name"""
tile = self.a2.Tile('#')
result = tile.get_name()
self.assertEqual(result, '#')
def test_get_id(self):
"""test Tile.get_id"""
tile = self.a2.Tile('#')
result = tile.get_id()
self.as... | the_stack_v2_python_sparse | CSSE7030/Assignment - 2/test_files/test_a2_sample.py | admiralmafei/UQ | train | 3 | |
f299c4ecf87e8710df75aa041b18fecfe4404d7f | [
"for key, value in validated_data.items():\n setattr(instance, key, value)\ninstance.save()\nreturn instance",
"is_following = False\ntry:\n current_user = User.objects.get(username=self.context['request'])\n username = User.objects.get(username=self.context['username'])\n is_following = UserFollow.ob... | <|body_start_0|>
for key, value in validated_data.items():
setattr(instance, key, value)
instance.save()
return instance
<|end_body_0|>
<|body_start_1|>
is_following = False
try:
current_user = User.objects.get(username=self.context['request'])
... | Handles serialization and deserialization of User Profile objects. | ProfileSerializer | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ProfileSerializer:
"""Handles serialization and deserialization of User Profile objects."""
def update(self, instance, validated_data):
"""Performs an update on a User Profile."""
<|body_0|>
def get_following(self, user):
"""This method 'get_following' defines wh... | stack_v2_sparse_classes_75kplus_train_065974 | 1,864 | permissive | [
{
"docstring": "Performs an update on a User Profile.",
"name": "update",
"signature": "def update(self, instance, validated_data)"
},
{
"docstring": "This method 'get_following' defines whether the authenticated user follows the user associated with the provided username",
"name": "get_foll... | 2 | stack_v2_sparse_classes_30k_val_001404 | Implement the Python class `ProfileSerializer` described below.
Class description:
Handles serialization and deserialization of User Profile objects.
Method signatures and docstrings:
- def update(self, instance, validated_data): Performs an update on a User Profile.
- def get_following(self, user): This method 'get_... | Implement the Python class `ProfileSerializer` described below.
Class description:
Handles serialization and deserialization of User Profile objects.
Method signatures and docstrings:
- def update(self, instance, validated_data): Performs an update on a User Profile.
- def get_following(self, user): This method 'get_... | ba429dfcec577bd6d52052673c1c413835f65988 | <|skeleton|>
class ProfileSerializer:
"""Handles serialization and deserialization of User Profile objects."""
def update(self, instance, validated_data):
"""Performs an update on a User Profile."""
<|body_0|>
def get_following(self, user):
"""This method 'get_following' defines wh... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class ProfileSerializer:
"""Handles serialization and deserialization of User Profile objects."""
def update(self, instance, validated_data):
"""Performs an update on a User Profile."""
for key, value in validated_data.items():
setattr(instance, key, value)
instance.save()
... | the_stack_v2_python_sparse | authors/apps/profiles/serializers.py | andela/ah-the-jedi-backend | train | 1 |
8da9623c561a4454bfd5921a12c1192c935e2a25 | [
"g = GoogleV3()\nplace, (lat, lng) = g.geocode(address, exactly_one=True)\nreturn (lat, lng)",
"g = geocoders.Google()\nplace, point = g.reverse(lat_lng)\nreturn place",
"config = h.parse_config_file()\nrequest_url = config['api_timezone_url']\nrequest_user = config['geonames_user']\nparams = {'lat': lat_lng[0]... | <|body_start_0|>
g = GoogleV3()
place, (lat, lng) = g.geocode(address, exactly_one=True)
return (lat, lng)
<|end_body_0|>
<|body_start_1|>
g = geocoders.Google()
place, point = g.reverse(lat_lng)
return place
<|end_body_1|>
<|body_start_2|>
config = h.parse_conf... | BarLocation | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BarLocation:
def geocode_address(cls, address):
"""a method to convert an address in string representation to its lat, long form"""
<|body_0|>
def reverse_geocode(cls, lat_lng):
"""a method to create an address out of its latitude and longitude"""
<|body_1|>
... | stack_v2_sparse_classes_75kplus_train_065975 | 1,463 | no_license | [
{
"docstring": "a method to convert an address in string representation to its lat, long form",
"name": "geocode_address",
"signature": "def geocode_address(cls, address)"
},
{
"docstring": "a method to create an address out of its latitude and longitude",
"name": "reverse_geocode",
"sig... | 3 | stack_v2_sparse_classes_30k_train_012116 | Implement the Python class `BarLocation` described below.
Class description:
Implement the BarLocation class.
Method signatures and docstrings:
- def geocode_address(cls, address): a method to convert an address in string representation to its lat, long form
- def reverse_geocode(cls, lat_lng): a method to create an ... | Implement the Python class `BarLocation` described below.
Class description:
Implement the BarLocation class.
Method signatures and docstrings:
- def geocode_address(cls, address): a method to convert an address in string representation to its lat, long form
- def reverse_geocode(cls, lat_lng): a method to create an ... | 89196dcce3fe5f967fe53da710b9886d80bab5f8 | <|skeleton|>
class BarLocation:
def geocode_address(cls, address):
"""a method to convert an address in string representation to its lat, long form"""
<|body_0|>
def reverse_geocode(cls, lat_lng):
"""a method to create an address out of its latitude and longitude"""
<|body_1|>
... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class BarLocation:
def geocode_address(cls, address):
"""a method to convert an address in string representation to its lat, long form"""
g = GoogleV3()
place, (lat, lng) = g.geocode(address, exactly_one=True)
return (lat, lng)
def reverse_geocode(cls, lat_lng):
"""a met... | the_stack_v2_python_sparse | opentapp/opentapp/barportal/location.py | steinbachr/OpenTapp | train | 0 | |
4854b02d5ef6582486b3c8e7eb8a1042a98f49c1 | [
"self._engine = create_engine('sqlite:///a.db', echo=False)\nBase.metadata.drop_all(self._engine)\nBase.metadata.create_all(self._engine)\nself.__session = None",
"if self.__session is None:\n DBSession = sessionmaker(bind=self._engine)\n self.__session = DBSession()\nreturn self.__session",
"user = User(... | <|body_start_0|>
self._engine = create_engine('sqlite:///a.db', echo=False)
Base.metadata.drop_all(self._engine)
Base.metadata.create_all(self._engine)
self.__session = None
<|end_body_0|>
<|body_start_1|>
if self.__session is None:
DBSession = sessionmaker(bind=self... | Database class | DB | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DB:
"""Database class"""
def __init__(self):
"""Initializes class attributes"""
<|body_0|>
def _session(self):
"""Private method that returns a session"""
<|body_1|>
def add_user(self, email: str, hashed_password: str) -> User:
"""Save new th... | stack_v2_sparse_classes_75kplus_train_065976 | 2,320 | no_license | [
{
"docstring": "Initializes class attributes",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Private method that returns a session",
"name": "_session",
"signature": "def _session(self)"
},
{
"docstring": "Save new the user to the database",
"name":... | 5 | stack_v2_sparse_classes_30k_train_007044 | Implement the Python class `DB` described below.
Class description:
Database class
Method signatures and docstrings:
- def __init__(self): Initializes class attributes
- def _session(self): Private method that returns a session
- def add_user(self, email: str, hashed_password: str) -> User: Save new the user to the d... | Implement the Python class `DB` described below.
Class description:
Database class
Method signatures and docstrings:
- def __init__(self): Initializes class attributes
- def _session(self): Private method that returns a session
- def add_user(self, email: str, hashed_password: str) -> User: Save new the user to the d... | 151c5c063b15c8474c1fa4ab5ce27f94f36c42b5 | <|skeleton|>
class DB:
"""Database class"""
def __init__(self):
"""Initializes class attributes"""
<|body_0|>
def _session(self):
"""Private method that returns a session"""
<|body_1|>
def add_user(self, email: str, hashed_password: str) -> User:
"""Save new th... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class DB:
"""Database class"""
def __init__(self):
"""Initializes class attributes"""
self._engine = create_engine('sqlite:///a.db', echo=False)
Base.metadata.drop_all(self._engine)
Base.metadata.create_all(self._engine)
self.__session = None
def _session(self):
... | the_stack_v2_python_sparse | 0x08-user_authentication_service/db.py | Gzoref/holbertonschool-web_back_end | train | 0 |
8d9a7f86bee9b0309bfd7cd6c30c87277f9f4843 | [
"self._cc_squares, self._ch_squares = ({2, 17, 33}, {7, 22, 36})\ncc_cards = [lambda x, tar=tar: tar for tar in ['STAY'] * 14 + [0, 10]]\nrandom.shuffle(cc_cards)\nself._cc_cards = itertools.cycle(cc_cards)\nch_cards = [lambda x, tar=tar: tar for tar in ['STAY'] * 6 + [0, 10, 11, 24, 39, 5]]\nnext_rr = lambda x: {7... | <|body_start_0|>
self._cc_squares, self._ch_squares = ({2, 17, 33}, {7, 22, 36})
cc_cards = [lambda x, tar=tar: tar for tar in ['STAY'] * 14 + [0, 10]]
random.shuffle(cc_cards)
self._cc_cards = itertools.cycle(cc_cards)
ch_cards = [lambda x, tar=tar: tar for tar in ['STAY'] * 6 +... | class finding the probabilities of a Monopoly turn ending on every square for given dice (through simulation) problem 84 | Monopoly | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Monopoly:
"""class finding the probabilities of a Monopoly turn ending on every square for given dice (through simulation) problem 84"""
def __init__(self, dice):
"""requires dice passed (see helper function get_n_sided_dice), designates spots w/ special actions, creates lambdas expe... | stack_v2_sparse_classes_75kplus_train_065977 | 3,658 | no_license | [
{
"docstring": "requires dice passed (see helper function get_n_sided_dice), designates spots w/ special actions, creates lambdas expecting current position for every significant card",
"name": "__init__",
"signature": "def __init__(self, dice)"
},
{
"docstring": "only real interface with logic ... | 5 | stack_v2_sparse_classes_30k_train_024127 | Implement the Python class `Monopoly` described below.
Class description:
class finding the probabilities of a Monopoly turn ending on every square for given dice (through simulation) problem 84
Method signatures and docstrings:
- def __init__(self, dice): requires dice passed (see helper function get_n_sided_dice), ... | Implement the Python class `Monopoly` described below.
Class description:
class finding the probabilities of a Monopoly turn ending on every square for given dice (through simulation) problem 84
Method signatures and docstrings:
- def __init__(self, dice): requires dice passed (see helper function get_n_sided_dice), ... | 5c0333fb240d0e8e75411c6301157c12d27758f4 | <|skeleton|>
class Monopoly:
"""class finding the probabilities of a Monopoly turn ending on every square for given dice (through simulation) problem 84"""
def __init__(self, dice):
"""requires dice passed (see helper function get_n_sided_dice), designates spots w/ special actions, creates lambdas expe... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Monopoly:
"""class finding the probabilities of a Monopoly turn ending on every square for given dice (through simulation) problem 84"""
def __init__(self, dice):
"""requires dice passed (see helper function get_n_sided_dice), designates spots w/ special actions, creates lambdas expecting current... | the_stack_v2_python_sparse | pe_80_89/problem_84.py | jwilner/project_euler | train | 0 |
82381db6c92600a6f97e118455a8dcd1f7018200 | [
"parser.add_argument('LINK_ID', help='ID of the linked dataset to create.')\nparser.add_argument('--description', help='A textual description for the linked dataset.')\nutil.AddParentArgs(parser, 'linked dataset to create')\nutil.AddBucketLocationArg(parser, True, 'Location of the bucket that will hold the linked d... | <|body_start_0|>
parser.add_argument('LINK_ID', help='ID of the linked dataset to create.')
parser.add_argument('--description', help='A textual description for the linked dataset.')
util.AddParentArgs(parser, 'linked dataset to create')
util.AddBucketLocationArg(parser, True, 'Location ... | Create a linked dataset on an analytics log bucket. | Create | [
"Apache-2.0",
"LicenseRef-scancode-unknown-license-reference"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Create:
"""Create a linked dataset on an analytics log 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 namespace. All the argu... | stack_v2_sparse_classes_75kplus_train_065978 | 3,633 | 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: Linked data... | 2 | stack_v2_sparse_classes_30k_train_034006 | Implement the Python class `Create` described below.
Class description:
Create a linked dataset on an analytics log 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: args: an argparse na... | Implement the Python class `Create` described below.
Class description:
Create a linked dataset on an analytics log 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: args: an argparse na... | 392abf004b16203030e6efd2f0af24db7c8d669e | <|skeleton|>
class Create:
"""Create a linked dataset on an analytics log 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 namespace. All the argu... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Create:
"""Create a linked dataset on an analytics log bucket."""
def Args(parser):
"""Register flags for this command."""
parser.add_argument('LINK_ID', help='ID of the linked dataset to create.')
parser.add_argument('--description', help='A textual description for the linked dat... | the_stack_v2_python_sparse | lib/surface/logging/links/create.py | google-cloud-sdk-unofficial/google-cloud-sdk | train | 9 |
27bdce3d62cb4f29b605c14764da3c2470778be0 | [
"time_units = AnalysisHelpers.convert_time_units_str_to_enum(time_units)\ntravel_direction = AnalysisHelpers.convert_travel_direction_str_to_enum(travel_direction)\ngeometry_at_cutoff = AnalysisHelpers.convert_geometry_at_cutoff_str_to_enum(geometry_at_cutoff)\ngeometry_at_overlap = AnalysisHelpers.convert_geometry... | <|body_start_0|>
time_units = AnalysisHelpers.convert_time_units_str_to_enum(time_units)
travel_direction = AnalysisHelpers.convert_travel_direction_str_to_enum(travel_direction)
geometry_at_cutoff = AnalysisHelpers.convert_geometry_at_cutoff_str_to_enum(geometry_at_cutoff)
geometry_at_o... | Solves a Service Area incrementally over a time window solving in parallel and combining results. | ParallelSACalculator | [
"Apache-2.0",
"LicenseRef-scancode-unknown-license-reference"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ParallelSACalculator:
"""Solves a Service Area incrementally over a time window solving in parallel and combining results."""
def __init__(self, facilities, output_polygons, network_data_source, travel_mode, cutoffs, time_units, time_window_start_day, time_window_start_time, time_window_end_... | stack_v2_sparse_classes_75kplus_train_065979 | 30,157 | permissive | [
{
"docstring": "Compute Service Areas in parallel over the time window and save the output polygons to a feature class. This class assumes that the inputs have already been pre-processed and validated. Args: facilities (str): Catalog path to facilities output_polygons (str): Catalog path to output polygons netw... | 3 | null | Implement the Python class `ParallelSACalculator` described below.
Class description:
Solves a Service Area incrementally over a time window solving in parallel and combining results.
Method signatures and docstrings:
- def __init__(self, facilities, output_polygons, network_data_source, travel_mode, cutoffs, time_un... | Implement the Python class `ParallelSACalculator` described below.
Class description:
Solves a Service Area incrementally over a time window solving in parallel and combining results.
Method signatures and docstrings:
- def __init__(self, facilities, output_polygons, network_data_source, travel_mode, cutoffs, time_un... | 47cbc3de67a7b1bf9255e07e88cba7b051db0505 | <|skeleton|>
class ParallelSACalculator:
"""Solves a Service Area incrementally over a time window solving in parallel and combining results."""
def __init__(self, facilities, output_polygons, network_data_source, travel_mode, cutoffs, time_units, time_window_start_day, time_window_start_time, time_window_end_... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class ParallelSACalculator:
"""Solves a Service Area incrementally over a time window solving in parallel and combining results."""
def __init__(self, facilities, output_polygons, network_data_source, travel_mode, cutoffs, time_units, time_window_start_day, time_window_start_time, time_window_end_day, time_win... | the_stack_v2_python_sparse | transit-network-analysis-tools/parallel_sa.py | Esri/public-transit-tools | train | 155 |
769f986c82f77dbf7d20c4b179ecdffb46db68e4 | [
"num_smooth = left_gauss_linear(num)\nden_smooth = left_gauss_linear(den)\nden_smooth = np.clip(den_smooth, 0, None)\nnum_smooth = np.clip(num_smooth, 0, den_smooth)\nreturn (num_smooth, den_smooth)",
"if isinstance(den, (pd.DataFrame, pd.Series)):\n den = den.values\nif isinstance(num, (pd.DataFrame, pd.Serie... | <|body_start_0|>
num_smooth = left_gauss_linear(num)
den_smooth = left_gauss_linear(den)
den_smooth = np.clip(den_smooth, 0, None)
num_smooth = np.clip(num_smooth, 0, den_smooth)
return (num_smooth, den_smooth)
<|end_body_0|>
<|body_start_1|>
if isinstance(den, (pd.DataF... | Class to fit a hospitalizations indicator using CLI counts from claims-based data. | ClaimsHospIndicator | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ClaimsHospIndicator:
"""Class to fit a hospitalizations indicator using CLI counts from claims-based data."""
def gauss_smooth(num, den):
"""Smooth using the left_gauss_linear. Args: num: array of numerator counts den: array of denominator counts Returns: tuple: (array of smoothed nu... | stack_v2_sparse_classes_75kplus_train_065980 | 5,159 | permissive | [
{
"docstring": "Smooth using the left_gauss_linear. Args: num: array of numerator counts den: array of denominator counts Returns: tuple: (array of smoothed num, array of smoothed den)",
"name": "gauss_smooth",
"signature": "def gauss_smooth(num, den)"
},
{
"docstring": "Adjust for small denomin... | 3 | stack_v2_sparse_classes_30k_val_000249 | Implement the Python class `ClaimsHospIndicator` described below.
Class description:
Class to fit a hospitalizations indicator using CLI counts from claims-based data.
Method signatures and docstrings:
- def gauss_smooth(num, den): Smooth using the left_gauss_linear. Args: num: array of numerator counts den: array of... | Implement the Python class `ClaimsHospIndicator` described below.
Class description:
Class to fit a hospitalizations indicator using CLI counts from claims-based data.
Method signatures and docstrings:
- def gauss_smooth(num, den): Smooth using the left_gauss_linear. Args: num: array of numerator counts den: array of... | 0c0ca18f38892c850565edf8bed9d2acaf234354 | <|skeleton|>
class ClaimsHospIndicator:
"""Class to fit a hospitalizations indicator using CLI counts from claims-based data."""
def gauss_smooth(num, den):
"""Smooth using the left_gauss_linear. Args: num: array of numerator counts den: array of denominator counts Returns: tuple: (array of smoothed nu... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class ClaimsHospIndicator:
"""Class to fit a hospitalizations indicator using CLI counts from claims-based data."""
def gauss_smooth(num, den):
"""Smooth using the left_gauss_linear. Args: num: array of numerator counts den: array of denominator counts Returns: tuple: (array of smoothed num, array of s... | the_stack_v2_python_sparse | claims_hosp/delphi_claims_hosp/indicator.py | alexcoda/covidcast-indicators | train | 0 |
a59a492015185b6e1a9af5b1fdf98acebc0c109e | [
"sc.logger.info('小影圈关注页面初始状态检查开始')\nfun_name = 'test_planet_page'\nsc.logger.info('点击小影圈主按钮')\np_btn = 'com.quvideo.xiaoying:id/img_find'\nWebDriverWait(sc.driver, 10, 1).until(lambda el: el.find_element_by_id(p_btn)).click()\ntime.sleep(1)\nsc.logger.info('开始查找小影圈关注tab')\nel_tab_list = sc.driver.find_elements_by_i... | <|body_start_0|>
sc.logger.info('小影圈关注页面初始状态检查开始')
fun_name = 'test_planet_page'
sc.logger.info('点击小影圈主按钮')
p_btn = 'com.quvideo.xiaoying:id/img_find'
WebDriverWait(sc.driver, 10, 1).until(lambda el: el.find_element_by_id(p_btn)).click()
time.sleep(1)
sc.logger.in... | 小影圈关注页UI的测试类，分步截图. | TestPlanetExploreUI | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestPlanetExploreUI:
"""小影圈关注页UI的测试类，分步截图."""
def test_planet_page(self):
"""小影圈关注页面初始状态测试."""
<|body_0|>
def test_refresh(self):
"""测试下拉刷新."""
<|body_1|>
def test_swipe_vertical(self):
"""测试上下方向的滑动."""
<|body_2|>
def test_origin... | stack_v2_sparse_classes_75kplus_train_065981 | 3,656 | no_license | [
{
"docstring": "小影圈关注页面初始状态测试.",
"name": "test_planet_page",
"signature": "def test_planet_page(self)"
},
{
"docstring": "测试下拉刷新.",
"name": "test_refresh",
"signature": "def test_refresh(self)"
},
{
"docstring": "测试上下方向的滑动.",
"name": "test_swipe_vertical",
"signature": "d... | 4 | stack_v2_sparse_classes_30k_test_001362 | Implement the Python class `TestPlanetExploreUI` described below.
Class description:
小影圈关注页UI的测试类，分步截图.
Method signatures and docstrings:
- def test_planet_page(self): 小影圈关注页面初始状态测试.
- def test_refresh(self): 测试下拉刷新.
- def test_swipe_vertical(self): 测试上下方向的滑动.
- def test_origin_home(self): 关注页tab的功能. | Implement the Python class `TestPlanetExploreUI` described below.
Class description:
小影圈关注页UI的测试类，分步截图.
Method signatures and docstrings:
- def test_planet_page(self): 小影圈关注页面初始状态测试.
- def test_refresh(self): 测试下拉刷新.
- def test_swipe_vertical(self): 测试上下方向的滑动.
- def test_origin_home(self): 关注页tab的功能.
<|skeleton|>
cl... | 0003b68fc8e26a96ee1661c1eb1f26f96810e909 | <|skeleton|>
class TestPlanetExploreUI:
"""小影圈关注页UI的测试类，分步截图."""
def test_planet_page(self):
"""小影圈关注页面初始状态测试."""
<|body_0|>
def test_refresh(self):
"""测试下拉刷新."""
<|body_1|>
def test_swipe_vertical(self):
"""测试上下方向的滑动."""
<|body_2|>
def test_origin... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class TestPlanetExploreUI:
"""小影圈关注页UI的测试类，分步截图."""
def test_planet_page(self):
"""小影圈关注页面初始状态测试."""
sc.logger.info('小影圈关注页面初始状态检查开始')
fun_name = 'test_planet_page'
sc.logger.info('点击小影圈主按钮')
p_btn = 'com.quvideo.xiaoying:id/img_find'
WebDriverWait(sc.driver, 10,... | the_stack_v2_python_sparse | iOS/VivaVideo/test_community/test_personal/test_follow.py | Lemonzhulixin/UItest | train | 5 |
1c84273a119cb53f83c6811b624b69c15f2c244f | [
"category_Q = Q()\nif category:\n category_Q = Q(category=category)\nname_Q = Q()\nif name:\n name_Q = Q(name__contains=name)\nstart = page * self.page_size\nend = start + self.page_size\nreturn Clothing.objects.filter(category_Q, name_Q).filter(is_active=True)[start:end]",
"category = request.REQUEST.get('... | <|body_start_0|>
category_Q = Q()
if category:
category_Q = Q(category=category)
name_Q = Q()
if name:
name_Q = Q(name__contains=name)
start = page * self.page_size
end = start + self.page_size
return Clothing.objects.filter(category_Q, nam... | Search clothing | ClothingSearchView | [
"Unlicense"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ClothingSearchView:
"""Search clothing"""
def search(self, category, name, page):
"""Search clothings"""
<|body_0|>
def get_ajax(self, request, *args, **kwargs):
"""Do ajax search"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
category_Q = Q()
... | stack_v2_sparse_classes_75kplus_train_065982 | 4,207 | permissive | [
{
"docstring": "Search clothings",
"name": "search",
"signature": "def search(self, category, name, page)"
},
{
"docstring": "Do ajax search",
"name": "get_ajax",
"signature": "def get_ajax(self, request, *args, **kwargs)"
}
] | 2 | stack_v2_sparse_classes_30k_train_039103 | Implement the Python class `ClothingSearchView` described below.
Class description:
Search clothing
Method signatures and docstrings:
- def search(self, category, name, page): Search clothings
- def get_ajax(self, request, *args, **kwargs): Do ajax search | Implement the Python class `ClothingSearchView` described below.
Class description:
Search clothing
Method signatures and docstrings:
- def search(self, category, name, page): Search clothings
- def get_ajax(self, request, *args, **kwargs): Do ajax search
<|skeleton|>
class ClothingSearchView:
"""Search clothing... | 0ea016745d92054bd4df8d934c1b67fd61b6f845 | <|skeleton|>
class ClothingSearchView:
"""Search clothing"""
def search(self, category, name, page):
"""Search clothings"""
<|body_0|>
def get_ajax(self, request, *args, **kwargs):
"""Do ajax search"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class ClothingSearchView:
"""Search clothing"""
def search(self, category, name, page):
"""Search clothings"""
category_Q = Q()
if category:
category_Q = Q(category=category)
name_Q = Q()
if name:
name_Q = Q(name__contains=name)
start = pa... | the_stack_v2_python_sparse | clothings/views.py | ygrass/handsome | train | 0 |
b66e4b55e67bb7998cd7bc882caaf58a9e15e8dd | [
"envelopes.sort(key=lambda x: (x[0], -x[1]))\ndoll = [0] * len(envelopes)\nmaxLen = 0\nfor envelope in envelopes:\n i = bisect_left(doll, envelope[1], 0, maxLen)\n doll[i] = envelope[1]\n if i == maxLen:\n maxLen += 1\nreturn maxLen",
"n = len(envelopes)\nif n < 1:\n return 0\nenvelopes.sort()\... | <|body_start_0|>
envelopes.sort(key=lambda x: (x[0], -x[1]))
doll = [0] * len(envelopes)
maxLen = 0
for envelope in envelopes:
i = bisect_left(doll, envelope[1], 0, maxLen)
doll[i] = envelope[1]
if i == maxLen:
maxLen += 1
retur... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def maxEnvelopes(self, envelopes):
""":type envelopes: List[List[int]] :rtype: int"""
<|body_0|>
def maxEnvelopes2(self, envelopes):
""":type envelopes: List[List[int]] :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
envelopes.... | stack_v2_sparse_classes_75kplus_train_065983 | 2,260 | no_license | [
{
"docstring": ":type envelopes: List[List[int]] :rtype: int",
"name": "maxEnvelopes",
"signature": "def maxEnvelopes(self, envelopes)"
},
{
"docstring": ":type envelopes: List[List[int]] :rtype: int",
"name": "maxEnvelopes2",
"signature": "def maxEnvelopes2(self, envelopes)"
}
] | 2 | stack_v2_sparse_classes_30k_train_010709 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def maxEnvelopes(self, envelopes): :type envelopes: List[List[int]] :rtype: int
- def maxEnvelopes2(self, envelopes): :type envelopes: List[List[int]] :rtype: int | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def maxEnvelopes(self, envelopes): :type envelopes: List[List[int]] :rtype: int
- def maxEnvelopes2(self, envelopes): :type envelopes: List[List[int]] :rtype: int
<|skeleton|>
c... | 635af6e22aa8eef8e7920a585d43a45a891a8157 | <|skeleton|>
class Solution:
def maxEnvelopes(self, envelopes):
""":type envelopes: List[List[int]] :rtype: int"""
<|body_0|>
def maxEnvelopes2(self, envelopes):
""":type envelopes: List[List[int]] :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Solution:
def maxEnvelopes(self, envelopes):
""":type envelopes: List[List[int]] :rtype: int"""
envelopes.sort(key=lambda x: (x[0], -x[1]))
doll = [0] * len(envelopes)
maxLen = 0
for envelope in envelopes:
i = bisect_left(doll, envelope[1], 0, maxLen)
... | the_stack_v2_python_sparse | code354RussianDollEnvelopes.py | cybelewang/leetcode-python | train | 0 | |
5790083497e46ef6365b2f805fcbdcfad01e7824 | [
"self.name = name\nself.given_name = given_name\nself.middle_name = middle_name\nself.family_name = family_name\nself.address = address\nself.additional_properties = additional_properties",
"if dictionary is None:\n return None\nname = dictionary.get('name')\ngiven_name = dictionary.get('givenName')\nmiddle_na... | <|body_start_0|>
self.name = name
self.given_name = given_name
self.middle_name = middle_name
self.family_name = family_name
self.address = address
self.additional_properties = additional_properties
<|end_body_0|>
<|body_start_1|>
if dictionary is None:
... | Implementation of the 'Payroll Employee Record' model. TODO: type model description here. Attributes: name (string): Full name of the employee: first, middle (if stated), and last name. given_name (string): First name of employee middle_name (string): Middle name of employee, if stated family_name (string): Last name o... | PayrollEmployeeRecord | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PayrollEmployeeRecord:
"""Implementation of the 'Payroll Employee Record' model. TODO: type model description here. Attributes: name (string): Full name of the employee: first, middle (if stated), and last name. given_name (string): First name of employee middle_name (string): Middle name of empl... | stack_v2_sparse_classes_75kplus_train_065984 | 2,958 | permissive | [
{
"docstring": "Constructor for the PayrollEmployeeRecord class",
"name": "__init__",
"signature": "def __init__(self, name=None, given_name=None, middle_name=None, family_name=None, address=None, additional_properties={})"
},
{
"docstring": "Creates an instance of this model from a dictionary A... | 2 | stack_v2_sparse_classes_30k_train_053093 | Implement the Python class `PayrollEmployeeRecord` described below.
Class description:
Implementation of the 'Payroll Employee Record' model. TODO: type model description here. Attributes: name (string): Full name of the employee: first, middle (if stated), and last name. given_name (string): First name of employee mi... | Implement the Python class `PayrollEmployeeRecord` described below.
Class description:
Implementation of the 'Payroll Employee Record' model. TODO: type model description here. Attributes: name (string): Full name of the employee: first, middle (if stated), and last name. given_name (string): First name of employee mi... | b2ab1ded435db75c78d42261f5e4acd2a3061487 | <|skeleton|>
class PayrollEmployeeRecord:
"""Implementation of the 'Payroll Employee Record' model. TODO: type model description here. Attributes: name (string): Full name of the employee: first, middle (if stated), and last name. given_name (string): First name of employee middle_name (string): Middle name of empl... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class PayrollEmployeeRecord:
"""Implementation of the 'Payroll Employee Record' model. TODO: type model description here. Attributes: name (string): Full name of the employee: first, middle (if stated), and last name. given_name (string): First name of employee middle_name (string): Middle name of employee, if stat... | the_stack_v2_python_sparse | finicityapi/models/payroll_employee_record.py | monarchmoney/finicity-python | train | 0 |
02161fb03380a04e8027c87eab197a384e62a0aa | [
"max_len = 0\nfor size in range(1, 27):\n m = {}\n begin = 0\n end = 0\n unique_letter = 0\n counter = 0\n while end < len(s):\n if unique_letter <= size:\n if s[end] not in m or m[s[end]] == 0:\n unique_letter += 1\n m[s[end]] = m[s[end]] + 1 if s[end] ... | <|body_start_0|>
max_len = 0
for size in range(1, 27):
m = {}
begin = 0
end = 0
unique_letter = 0
counter = 0
while end < len(s):
if unique_letter <= size:
if s[end] not in m or m[s[end]] == 0:
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def longestSubstring(self, s, k):
""":type s: str :type k: int :rtype: int"""
<|body_0|>
def longestSubstring2(self, s, k):
""":type s: str :type k: int :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
max_len = 0
for si... | stack_v2_sparse_classes_75kplus_train_065985 | 4,028 | no_license | [
{
"docstring": ":type s: str :type k: int :rtype: int",
"name": "longestSubstring",
"signature": "def longestSubstring(self, s, k)"
},
{
"docstring": ":type s: str :type k: int :rtype: int",
"name": "longestSubstring2",
"signature": "def longestSubstring2(self, s, k)"
}
] | 2 | stack_v2_sparse_classes_30k_train_035901 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def longestSubstring(self, s, k): :type s: str :type k: int :rtype: int
- def longestSubstring2(self, s, k): :type s: str :type k: int :rtype: int | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def longestSubstring(self, s, k): :type s: str :type k: int :rtype: int
- def longestSubstring2(self, s, k): :type s: str :type k: int :rtype: int
<|skeleton|>
class Solution:
... | 516b28a3df505b942098d91a4891e414f1c75c08 | <|skeleton|>
class Solution:
def longestSubstring(self, s, k):
""":type s: str :type k: int :rtype: int"""
<|body_0|>
def longestSubstring2(self, s, k):
""":type s: str :type k: int :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Solution:
def longestSubstring(self, s, k):
""":type s: str :type k: int :rtype: int"""
max_len = 0
for size in range(1, 27):
m = {}
begin = 0
end = 0
unique_letter = 0
counter = 0
while end < len(s):
... | the_stack_v2_python_sparse | TwoPointer/395_maxSubstrKRepeat.py | terylll/LeetCode | train | 0 | |
504652878111639cc349d6f684e799d7213d815b | [
"self.__image = Image(imagePath)\nself.__axis_from_edges = None\nself.__right_direction = None",
"if self.__right_direction == None:\n angle = None\n counts = 0\n ordinate = None\n for i in range(-20, 21):\n rotated_i = ndimage.rotate(self.__image.edges_detection(), i, reshape=False)\n l... | <|body_start_0|>
self.__image = Image(imagePath)
self.__axis_from_edges = None
self.__right_direction = None
<|end_body_0|>
<|body_start_1|>
if self.__right_direction == None:
angle = None
counts = 0
ordinate = None
for i in range(-20, 21)... | class defining the protection object on a photograph | Protection | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Protection:
"""class defining the protection object on a photograph"""
def __init__(self, imagePath: str) -> None:
"""constructor of class Protection"""
<|body_0|>
def get_right_direction(self) -> dict:
"""@parameters: object itself @returns: an object indicating... | stack_v2_sparse_classes_75kplus_train_065986 | 4,133 | no_license | [
{
"docstring": "constructor of class Protection",
"name": "__init__",
"signature": "def __init__(self, imagePath: str) -> None"
},
{
"docstring": "@parameters: object itself @returns: an object indicating information on the protection axis approximation",
"name": "get_right_direction",
"... | 4 | stack_v2_sparse_classes_30k_train_009077 | Implement the Python class `Protection` described below.
Class description:
class defining the protection object on a photograph
Method signatures and docstrings:
- def __init__(self, imagePath: str) -> None: constructor of class Protection
- def get_right_direction(self) -> dict: @parameters: object itself @returns:... | Implement the Python class `Protection` described below.
Class description:
class defining the protection object on a photograph
Method signatures and docstrings:
- def __init__(self, imagePath: str) -> None: constructor of class Protection
- def get_right_direction(self) -> dict: @parameters: object itself @returns:... | dc29188eb7cb3a7302ac474f04d5307c6e584d52 | <|skeleton|>
class Protection:
"""class defining the protection object on a photograph"""
def __init__(self, imagePath: str) -> None:
"""constructor of class Protection"""
<|body_0|>
def get_right_direction(self) -> dict:
"""@parameters: object itself @returns: an object indicating... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Protection:
"""class defining the protection object on a photograph"""
def __init__(self, imagePath: str) -> None:
"""constructor of class Protection"""
self.__image = Image(imagePath)
self.__axis_from_edges = None
self.__right_direction = None
def get_right_direction... | the_stack_v2_python_sparse | src/conformity/Protection.py | fredericsonergia/confomity-presence | train | 0 |
bb584cc5b7c53705b83d1c9bd57872f326f83a05 | [
"command_list = []\nfor i in range(len(command)):\n if i % 2 == 0:\n command_list.append(command[i:i + 2].replace('ff', 'fe01').replace('fe', 'fe00'))\nresult = ''.join(command_list)\nreturn result",
"rsctl = CommonUtil.get_rsctl()\ncmd_type = 'c0'\ntime_stamp = int(time.time())\nunix_time = hex(time_st... | <|body_start_0|>
command_list = []
for i in range(len(command)):
if i % 2 == 0:
command_list.append(command[i:i + 2].replace('ff', 'fe01').replace('fe', 'fe00'))
result = ''.join(command_list)
return result
<|end_body_0|>
<|body_start_1|>
rsctl = Comm... | 指令发送集合 | CommandSendSet | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CommandSendSet:
"""指令发送集合"""
def transfer_command(command):
"""转义指令"""
<|body_0|>
def combine_c0(lane_mode='03', wait_time='02', tx_power='0a', pll_channel_id='00', trans_mode='02'):
"""组合c0指令， 该指令为初始化指令 :param lane_mode: 车道工作模式 :param wait_time: 最小重读时间 :param tx... | stack_v2_sparse_classes_75kplus_train_065987 | 5,258 | no_license | [
{
"docstring": "转义指令",
"name": "transfer_command",
"signature": "def transfer_command(command)"
},
{
"docstring": "组合c0指令， 该指令为初始化指令 :param lane_mode: 车道工作模式 :param wait_time: 最小重读时间 :param tx_power: etc天线功率级数 :param pll_channel_id: 信道号 :param trans_mode: 记账模式 :return:",
"name": "combine_c0"... | 6 | stack_v2_sparse_classes_30k_train_018577 | Implement the Python class `CommandSendSet` described below.
Class description:
指令发送集合
Method signatures and docstrings:
- def transfer_command(command): 转义指令
- def combine_c0(lane_mode='03', wait_time='02', tx_power='0a', pll_channel_id='00', trans_mode='02'): 组合c0指令， 该指令为初始化指令 :param lane_mode: 车道工作模式 :param wait_t... | Implement the Python class `CommandSendSet` described below.
Class description:
指令发送集合
Method signatures and docstrings:
- def transfer_command(command): 转义指令
- def combine_c0(lane_mode='03', wait_time='02', tx_power='0a', pll_channel_id='00', trans_mode='02'): 组合c0指令， 该指令为初始化指令 :param lane_mode: 车道工作模式 :param wait_t... | 3e854b6cb479497e44fb36a77e4f4ccef803d911 | <|skeleton|>
class CommandSendSet:
"""指令发送集合"""
def transfer_command(command):
"""转义指令"""
<|body_0|>
def combine_c0(lane_mode='03', wait_time='02', tx_power='0a', pll_channel_id='00', trans_mode='02'):
"""组合c0指令， 该指令为初始化指令 :param lane_mode: 车道工作模式 :param wait_time: 最小重读时间 :param tx... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class CommandSendSet:
"""指令发送集合"""
def transfer_command(command):
"""转义指令"""
command_list = []
for i in range(len(command)):
if i % 2 == 0:
command_list.append(command[i:i + 2].replace('ff', 'fe01').replace('fe', 'fe00'))
result = ''.join(command_list... | the_stack_v2_python_sparse | rsu_soulin/service/command_send_set.py | crazyfox07/rsu-langneng | train | 0 |
0645eaee821c22e20c7ff7b92d68907c28af7cf2 | [
"IV = FeaDisSelectClass(self.ivThreshold, self.xgbSelectNum).iv_xy(self.train['c1'], self.train['label'])\nprint(IV)\nreturn IV",
"ivList = FeaDisSelectClass(self.ivThreshold, self.xgbSelectNum).iv_claculate(self.train, 'label', X=['c1', 'c2', 'c3', 'c4', 'c5', 'c6', 'c7', 'c8'], order=True)\nprint(ivList)\nretur... | <|body_start_0|>
IV = FeaDisSelectClass(self.ivThreshold, self.xgbSelectNum).iv_xy(self.train['c1'], self.train['label'])
print(IV)
return IV
<|end_body_0|>
<|body_start_1|>
ivList = FeaDisSelectClass(self.ivThreshold, self.xgbSelectNum).iv_claculate(self.train, 'label', X=['c1', 'c2', ... | 功能描述：缺失值处理测试类 | TestFeaDisSelectHandle | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestFeaDisSelectHandle:
"""功能描述：缺失值处理测试类"""
def test_iv_xy(self):
"""功能描述： 测试iV值计算函数 管理记录： 1. edited by 王文丹 2021/07/24"""
<|body_0|>
def test_iv_claculate(self):
"""功能描述： 测试iv_claculate函数 管理记录： 1. edited by 王文丹 2021/07/24"""
<|body_1|>
def test_iv_se... | stack_v2_sparse_classes_75kplus_train_065988 | 4,772 | no_license | [
{
"docstring": "功能描述： 测试iV值计算函数 管理记录： 1. edited by 王文丹 2021/07/24",
"name": "test_iv_xy",
"signature": "def test_iv_xy(self)"
},
{
"docstring": "功能描述： 测试iv_claculate函数 管理记录： 1. edited by 王文丹 2021/07/24",
"name": "test_iv_claculate",
"signature": "def test_iv_claculate(self)"
},
{
... | 3 | null | Implement the Python class `TestFeaDisSelectHandle` described below.
Class description:
功能描述：缺失值处理测试类
Method signatures and docstrings:
- def test_iv_xy(self): 功能描述： 测试iV值计算函数 管理记录： 1. edited by 王文丹 2021/07/24
- def test_iv_claculate(self): 功能描述： 测试iv_claculate函数 管理记录： 1. edited by 王文丹 2021/07/24
- def test_iv_select... | Implement the Python class `TestFeaDisSelectHandle` described below.
Class description:
功能描述：缺失值处理测试类
Method signatures and docstrings:
- def test_iv_xy(self): 功能描述： 测试iV值计算函数 管理记录： 1. edited by 王文丹 2021/07/24
- def test_iv_claculate(self): 功能描述： 测试iv_claculate函数 管理记录： 1. edited by 王文丹 2021/07/24
- def test_iv_select... | e1f7d1229ee82fb5cf7e5f969f24f8c61568c2c9 | <|skeleton|>
class TestFeaDisSelectHandle:
"""功能描述：缺失值处理测试类"""
def test_iv_xy(self):
"""功能描述： 测试iV值计算函数 管理记录： 1. edited by 王文丹 2021/07/24"""
<|body_0|>
def test_iv_claculate(self):
"""功能描述： 测试iv_claculate函数 管理记录： 1. edited by 王文丹 2021/07/24"""
<|body_1|>
def test_iv_se... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class TestFeaDisSelectHandle:
"""功能描述：缺失值处理测试类"""
def test_iv_xy(self):
"""功能描述： 测试iV值计算函数 管理记录： 1. edited by 王文丹 2021/07/24"""
IV = FeaDisSelectClass(self.ivThreshold, self.xgbSelectNum).iv_xy(self.train['c1'], self.train['label'])
print(IV)
return IV
def test_iv_claculate... | the_stack_v2_python_sparse | J_unintest/F_SelectChar_test/test_FeaSelectProcessModule/test_FeaDisSelectHandle.py | wenhui0331/Basic_Model_V1 | train | 0 |
255d4f94087b86f574f047e44430680b0291aa2d | [
"super(ReignitionCallback, self).__init__()\nself.priority = 100\nself.desc_copy = None",
"logging.info('Start SPNas Reigniting.')\nself.desc_copy = copy.deepcopy(self.trainer.model_desc)\nbackbone = self.desc_copy.get('backbone')\ncode = backbone.get('code')\nself.trainer.model_desc = dict(type='SerialClassifica... | <|body_start_0|>
super(ReignitionCallback, self).__init__()
self.priority = 100
self.desc_copy = None
<|end_body_0|>
<|body_start_1|>
logging.info('Start SPNas Reigniting.')
self.desc_copy = copy.deepcopy(self.trainer.model_desc)
backbone = self.desc_copy.get('backbone')... | Reignition callback. | ReignitionCallback | [
"LicenseRef-scancode-unknown-license-reference",
"Apache-2.0",
"BSD-3-Clause",
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ReignitionCallback:
"""Reignition callback."""
def __init__(self):
"""Initialize callback."""
<|body_0|>
def init_trainer(self, logs=None):
"""Be called before train."""
<|body_1|>
def after_epoch(self, epoch, logs=None):
"""Save desc into Fa... | stack_v2_sparse_classes_75kplus_train_065989 | 1,801 | permissive | [
{
"docstring": "Initialize callback.",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Be called before train.",
"name": "init_trainer",
"signature": "def init_trainer(self, logs=None)"
},
{
"docstring": "Save desc into FasterRCNN.",
"name": "after_ep... | 3 | stack_v2_sparse_classes_30k_train_023082 | Implement the Python class `ReignitionCallback` described below.
Class description:
Reignition callback.
Method signatures and docstrings:
- def __init__(self): Initialize callback.
- def init_trainer(self, logs=None): Be called before train.
- def after_epoch(self, epoch, logs=None): Save desc into FasterRCNN. | Implement the Python class `ReignitionCallback` described below.
Class description:
Reignition callback.
Method signatures and docstrings:
- def __init__(self): Initialize callback.
- def init_trainer(self, logs=None): Be called before train.
- def after_epoch(self, epoch, logs=None): Save desc into FasterRCNN.
<|sk... | 12e37a1991eb6771a2999fe0a46ddda920c47948 | <|skeleton|>
class ReignitionCallback:
"""Reignition callback."""
def __init__(self):
"""Initialize callback."""
<|body_0|>
def init_trainer(self, logs=None):
"""Be called before train."""
<|body_1|>
def after_epoch(self, epoch, logs=None):
"""Save desc into Fa... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class ReignitionCallback:
"""Reignition callback."""
def __init__(self):
"""Initialize callback."""
super(ReignitionCallback, self).__init__()
self.priority = 100
self.desc_copy = None
def init_trainer(self, logs=None):
"""Be called before train."""
logging.... | the_stack_v2_python_sparse | vega/algorithms/nas/sp_nas/reignition.py | huawei-noah/vega | train | 850 |
72d2ee8a515be65bb5becaa84726c86102dc928c | [
"super(ResizeDetailsMessageBox, self).__init__(*args, **kwargs)\nself.detailsBoxWidth = detailsBoxWidth\nself.detailBoxHeight = detailBoxHeight",
"result = super(ResizeDetailsMessageBox, self).resizeEvent(event)\ndetails_box = self.findChild(QtWidgets.QTextEdit)\nif details_box is not None:\n details_box.setFi... | <|body_start_0|>
super(ResizeDetailsMessageBox, self).__init__(*args, **kwargs)
self.detailsBoxWidth = detailsBoxWidth
self.detailBoxHeight = detailBoxHeight
<|end_body_0|>
<|body_start_1|>
result = super(ResizeDetailsMessageBox, self).resizeEvent(event)
details_box = self.findC... | Message box that enlarges when the 'Show Details' button is clicked. Can be used to better view stack traces. I could't find how to make a resizeable message box but this it the next best thing. Taken from: http://stackoverflow.com/questions/2655354/how-to-allow-resizing-of-qmessagebox-in-pyqt4 | ResizeDetailsMessageBox | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ResizeDetailsMessageBox:
"""Message box that enlarges when the 'Show Details' button is clicked. Can be used to better view stack traces. I could't find how to make a resizeable message box but this it the next best thing. Taken from: http://stackoverflow.com/questions/2655354/how-to-allow-resizi... | stack_v2_sparse_classes_75kplus_train_065990 | 7,800 | permissive | [
{
"docstring": "Constructor :param detailsBoxWidht: The width of the details text box (default=700) :param detailBoxHeight: The heights of the details text box (default=700)",
"name": "__init__",
"signature": "def __init__(self, detailsBoxWidth=700, detailBoxHeight=300, *args, **kwargs)"
},
{
"d... | 2 | stack_v2_sparse_classes_30k_train_033039 | Implement the Python class `ResizeDetailsMessageBox` described below.
Class description:
Message box that enlarges when the 'Show Details' button is clicked. Can be used to better view stack traces. I could't find how to make a resizeable message box but this it the next best thing. Taken from: http://stackoverflow.co... | Implement the Python class `ResizeDetailsMessageBox` described below.
Class description:
Message box that enlarges when the 'Show Details' button is clicked. Can be used to better view stack traces. I could't find how to make a resizeable message box but this it the next best thing. Taken from: http://stackoverflow.co... | 57a66e2d7030c265f0b5e1d577326cb9e986216f | <|skeleton|>
class ResizeDetailsMessageBox:
"""Message box that enlarges when the 'Show Details' button is clicked. Can be used to better view stack traces. I could't find how to make a resizeable message box but this it the next best thing. Taken from: http://stackoverflow.com/questions/2655354/how-to-allow-resizi... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class ResizeDetailsMessageBox:
"""Message box that enlarges when the 'Show Details' button is clicked. Can be used to better view stack traces. I could't find how to make a resizeable message box but this it the next best thing. Taken from: http://stackoverflow.com/questions/2655354/how-to-allow-resizing-of-qmessag... | the_stack_v2_python_sparse | objbrowser/app.py | titusjan/objbrowser | train | 94 |
3c5441a9df4a66d80d30bb975cc02649e04e57f3 | [
"if server_ip == '' and server_port != 0 or (server_ip != '' and server_port == 0):\n raise Exception('server_ip和server_port必须同时指定')\nself._server_ip = server_ip\nself._server_port = server_port\nself._service_name = service_name\nself._host = host",
"headers = {'org': org, 'user': user}\nroute_name = ''\nserv... | <|body_start_0|>
if server_ip == '' and server_port != 0 or (server_ip != '' and server_port == 0):
raise Exception('server_ip和server_port必须同时指定')
self._server_ip = server_ip
self._server_port = server_port
self._service_name = service_name
self._host = host
<|end_bod... | AlertRuleClient | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AlertRuleClient:
def __init__(self, server_ip='', server_port=0, service_name='', host=''):
"""初始化client :param server_ip: 指定sdk请求的server_ip，为空时走名字服务路由 :param server_port: 指定sdk请求的server_port，与server_ip一起使用, 为空时走名字服务路由 :param service_name: 指定sdk请求的service_name, 为空时按契约名称路由。如果server_ip和ser... | stack_v2_sparse_classes_75kplus_train_065991 | 4,691 | permissive | [
{
"docstring": "初始化client :param server_ip: 指定sdk请求的server_ip，为空时走名字服务路由 :param server_port: 指定sdk请求的server_port，与server_ip一起使用, 为空时走名字服务路由 :param service_name: 指定sdk请求的service_name, 为空时按契约名称路由。如果server_ip和service_name同时设置，server_ip优先级更高 :param host: 指定sdk请求服务的host名称, 如cmdb.easyops-only.com",
"name": "__ini... | 3 | stack_v2_sparse_classes_30k_train_035369 | Implement the Python class `AlertRuleClient` described below.
Class description:
Implement the AlertRuleClient class.
Method signatures and docstrings:
- def __init__(self, server_ip='', server_port=0, service_name='', host=''): 初始化client :param server_ip: 指定sdk请求的server_ip，为空时走名字服务路由 :param server_port: 指定sdk请求的serv... | Implement the Python class `AlertRuleClient` described below.
Class description:
Implement the AlertRuleClient class.
Method signatures and docstrings:
- def __init__(self, server_ip='', server_port=0, service_name='', host=''): 初始化client :param server_ip: 指定sdk请求的server_ip，为空时走名字服务路由 :param server_port: 指定sdk请求的serv... | adf6e3bad33fa6266b5fa0a449dd4ac42f8447d0 | <|skeleton|>
class AlertRuleClient:
def __init__(self, server_ip='', server_port=0, service_name='', host=''):
"""初始化client :param server_ip: 指定sdk请求的server_ip，为空时走名字服务路由 :param server_port: 指定sdk请求的server_port，与server_ip一起使用, 为空时走名字服务路由 :param service_name: 指定sdk请求的service_name, 为空时按契约名称路由。如果server_ip和ser... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class AlertRuleClient:
def __init__(self, server_ip='', server_port=0, service_name='', host=''):
"""初始化client :param server_ip: 指定sdk请求的server_ip，为空时走名字服务路由 :param server_port: 指定sdk请求的server_port，与server_ip一起使用, 为空时走名字服务路由 :param service_name: 指定sdk请求的service_name, 为空时按契约名称路由。如果server_ip和service_name同时设置，... | the_stack_v2_python_sparse | alert_service_sdk/api/alert_rule/alert_rule_client.py | easyopsapis/easyops-api-python | train | 5 | |
048e6500651bb7b7bcd37a991421be697a63a2ca | [
"args = parser.parse_args()\nsentence = args.get('sentence')\nreturn jsonify(rst)",
"form = ChargeForm().validate_for_api()\nsentence = form.sentence.data\nreturn jsonify(rst)"
] | <|body_start_0|>
args = parser.parse_args()
sentence = args.get('sentence')
return jsonify(rst)
<|end_body_0|>
<|body_start_1|>
form = ChargeForm().validate_for_api()
sentence = form.sentence.data
return jsonify(rst)
<|end_body_1|>
| （刑事）罪名预测引擎 | Charge | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Charge:
"""（刑事）罪名预测引擎"""
def get(self):
"""罪名预测 根据用户的输入预测罪名"""
<|body_0|>
def post(self):
"""罪名预测 根据用户的输入预测罪名"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
args = parser.parse_args()
sentence = args.get('sentence')
return jsoni... | stack_v2_sparse_classes_75kplus_train_065992 | 1,388 | no_license | [
{
"docstring": "罪名预测 根据用户的输入预测罪名",
"name": "get",
"signature": "def get(self)"
},
{
"docstring": "罪名预测 根据用户的输入预测罪名",
"name": "post",
"signature": "def post(self)"
}
] | 2 | stack_v2_sparse_classes_30k_val_000669 | Implement the Python class `Charge` described below.
Class description:
（刑事）罪名预测引擎
Method signatures and docstrings:
- def get(self): 罪名预测 根据用户的输入预测罪名
- def post(self): 罪名预测 根据用户的输入预测罪名 | Implement the Python class `Charge` described below.
Class description:
（刑事）罪名预测引擎
Method signatures and docstrings:
- def get(self): 罪名预测 根据用户的输入预测罪名
- def post(self): 罪名预测 根据用户的输入预测罪名
<|skeleton|>
class Charge:
"""（刑事）罪名预测引擎"""
def get(self):
"""罪名预测 根据用户的输入预测罪名"""
<|body_0|>
def post... | d373f6bcd461a55d6a6662ccf3a9edd66f0b91ab | <|skeleton|>
class Charge:
"""（刑事）罪名预测引擎"""
def get(self):
"""罪名预测 根据用户的输入预测罪名"""
<|body_0|>
def post(self):
"""罪名预测 根据用户的输入预测罪名"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Charge:
"""（刑事）罪名预测引擎"""
def get(self):
"""罪名预测 根据用户的输入预测罪名"""
args = parser.parse_args()
sentence = args.get('sentence')
return jsonify(rst)
def post(self):
"""罪名预测 根据用户的输入预测罪名"""
form = ChargeForm().validate_for_api()
sentence = form.sentence... | the_stack_v2_python_sparse | charges_kg/charges_kg/src/web/apps/v1/charge.py | yaolinxia/practice | train | 0 |
c979607d76c252a7029c18bf8f76286d65a6b907 | [
"self.validate_json(data, process_fn=process_fn)\nself.semantic_validate_json(data, process_fn=process_fn)\nreturn data",
"self.validate_json(data, process_fn=process_fn)\nself.semantic_validate_json(data, process_fn=process_fn)\nreturn data",
"validate = self.context.get(self.VALIDATE, False)\nif not validate:... | <|body_start_0|>
self.validate_json(data, process_fn=process_fn)
self.semantic_validate_json(data, process_fn=process_fn)
return data
<|end_body_0|>
<|body_start_1|>
self.validate_json(data, process_fn=process_fn)
self.semantic_validate_json(data, process_fn=process_fn)
... | ValidatingSchema is a marshmallow schema that calls the appropriate Telescope Model schema validation functions when serialising or deserialising JSON. | ValidatingSchema | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ValidatingSchema:
"""ValidatingSchema is a marshmallow schema that calls the appropriate Telescope Model schema validation functions when serialising or deserialising JSON."""
def validate_on_load(self, data, process_fn=lambda x: x, **_):
"""Validate the JSON string to deserialise. :... | stack_v2_sparse_classes_75kplus_train_065993 | 4,890 | permissive | [
{
"docstring": "Validate the JSON string to deserialise. :param data: Marshmallow-provided dict containing parsed object values :param process_fn: function to process data before validation :param _: unused kwargs passed by Marshmallow :return: dict suitable for object constructor.",
"name": "validate_on_lo... | 4 | stack_v2_sparse_classes_30k_train_003007 | Implement the Python class `ValidatingSchema` described below.
Class description:
ValidatingSchema is a marshmallow schema that calls the appropriate Telescope Model schema validation functions when serialising or deserialising JSON.
Method signatures and docstrings:
- def validate_on_load(self, data, process_fn=lamb... | Implement the Python class `ValidatingSchema` described below.
Class description:
ValidatingSchema is a marshmallow schema that calls the appropriate Telescope Model schema validation functions when serialising or deserialising JSON.
Method signatures and docstrings:
- def validate_on_load(self, data, process_fn=lamb... | 87083655aca8f8f53a26dba253a0189d8519714b | <|skeleton|>
class ValidatingSchema:
"""ValidatingSchema is a marshmallow schema that calls the appropriate Telescope Model schema validation functions when serialising or deserialising JSON."""
def validate_on_load(self, data, process_fn=lambda x: x, **_):
"""Validate the JSON string to deserialise. :... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class ValidatingSchema:
"""ValidatingSchema is a marshmallow schema that calls the appropriate Telescope Model schema validation functions when serialising or deserialising JSON."""
def validate_on_load(self, data, process_fn=lambda x: x, **_):
"""Validate the JSON string to deserialise. :param data: M... | the_stack_v2_python_sparse | src/ska_tmc_cdm/schemas/shared.py | ska-telescope/cdm-shared-library | train | 0 |
105366e7da84f09ca4651ffd86a53e1a710299ac | [
"if len(signals) == 0:\n return 0.0\nif len(signals) == 1:\n res = np.copy(signals[0])\nelse:\n rng = slice(*phase)\n wei = np.clip(np.array([nanhfsigma(i[rng]) for i in signals], dtype='f4'), 0.0005, 0.01)\n wei[np.isnan(wei)] = 0.01\n wei = 0.01 - wei\n wei[np.isnan(wei)] = 0.0\n res = np.... | <|body_start_0|>
if len(signals) == 0:
return 0.0
if len(signals) == 1:
res = np.copy(signals[0])
else:
rng = slice(*phase)
wei = np.clip(np.array([nanhfsigma(i[rng]) for i in signals], dtype='f4'), 0.0005, 0.01)
wei[np.isnan(wei)] = 0.... | Subtracts the average signal | SubtractWeightedAverageSignal | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SubtractWeightedAverageSignal:
"""Subtracts the average signal"""
def apply(signals, phase):
"""Aggregates signals"""
<|body_0|>
def process(self, beads, frame):
"""Aggregates signals from a frame"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
... | stack_v2_sparse_classes_75kplus_train_065994 | 17,420 | no_license | [
{
"docstring": "Aggregates signals",
"name": "apply",
"signature": "def apply(signals, phase)"
},
{
"docstring": "Aggregates signals from a frame",
"name": "process",
"signature": "def process(self, beads, frame)"
}
] | 2 | stack_v2_sparse_classes_30k_train_027720 | Implement the Python class `SubtractWeightedAverageSignal` described below.
Class description:
Subtracts the average signal
Method signatures and docstrings:
- def apply(signals, phase): Aggregates signals
- def process(self, beads, frame): Aggregates signals from a frame | Implement the Python class `SubtractWeightedAverageSignal` described below.
Class description:
Subtracts the average signal
Method signatures and docstrings:
- def apply(signals, phase): Aggregates signals
- def process(self, beads, frame): Aggregates signals from a frame
<|skeleton|>
class SubtractWeightedAverageSi... | f9534e4fff9775ff45d08d401de61015d4a69e76 | <|skeleton|>
class SubtractWeightedAverageSignal:
"""Subtracts the average signal"""
def apply(signals, phase):
"""Aggregates signals"""
<|body_0|>
def process(self, beads, frame):
"""Aggregates signals from a frame"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class SubtractWeightedAverageSignal:
"""Subtracts the average signal"""
def apply(signals, phase):
"""Aggregates signals"""
if len(signals) == 0:
return 0.0
if len(signals) == 1:
res = np.copy(signals[0])
else:
rng = slice(*phase)
... | the_stack_v2_python_sparse | src/cleaning/beadsubtraction.py | depixusgenome/trackanalysis | train | 0 |
c98c5fc182539b9fdbedaf30acb24607f80dd8b2 | [
"self.original_user = kwargs.pop('original_user', None)\nsuper(EmailShareForm, self).__init__(*args, **kwargs)\nif self.original_user is not None:\n self.fields['user_group'].queryset = CustomUser.objects.filter(tenant=get_current_user().tenant).exclude(pk=self.original_user.pk)\nelse:\n self.fields['user_gro... | <|body_start_0|>
self.original_user = kwargs.pop('original_user', None)
super(EmailShareForm, self).__init__(*args, **kwargs)
if self.original_user is not None:
self.fields['user_group'].queryset = CustomUser.objects.filter(tenant=get_current_user().tenant).exclude(pk=self.original_u... | Form to share an e-mail account. | EmailShareForm | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class EmailShareForm:
"""Form to share an e-mail account."""
def __init__(self, *args, **kwargs):
"""Overload super().__init__ to change the appearance of the form."""
<|body_0|>
def clean(self):
"""Please specify which users to share this email address with."""
... | stack_v2_sparse_classes_75kplus_train_065995 | 19,124 | no_license | [
{
"docstring": "Overload super().__init__ to change the appearance of the form.",
"name": "__init__",
"signature": "def __init__(self, *args, **kwargs)"
},
{
"docstring": "Please specify which users to share this email address with.",
"name": "clean",
"signature": "def clean(self)"
},
... | 3 | null | Implement the Python class `EmailShareForm` described below.
Class description:
Form to share an e-mail account.
Method signatures and docstrings:
- def __init__(self, *args, **kwargs): Overload super().__init__ to change the appearance of the form.
- def clean(self): Please specify which users to share this email ad... | Implement the Python class `EmailShareForm` described below.
Class description:
Form to share an e-mail account.
Method signatures and docstrings:
- def __init__(self, *args, **kwargs): Overload super().__init__ to change the appearance of the form.
- def clean(self): Please specify which users to share this email ad... | 0a284e2aae3ca08955215418a76bb70ad9af1f81 | <|skeleton|>
class EmailShareForm:
"""Form to share an e-mail account."""
def __init__(self, *args, **kwargs):
"""Overload super().__init__ to change the appearance of the form."""
<|body_0|>
def clean(self):
"""Please specify which users to share this email address with."""
... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class EmailShareForm:
"""Form to share an e-mail account."""
def __init__(self, *args, **kwargs):
"""Overload super().__init__ to change the appearance of the form."""
self.original_user = kwargs.pop('original_user', None)
super(EmailShareForm, self).__init__(*args, **kwargs)
if... | the_stack_v2_python_sparse | lily/messaging/email/forms/forms.py | rmoorman/hellolily | train | 0 |
67ac9a0282cf54e04071ece60930f6fe934ecc14 | [
"if sort is None:\n self.sort = lambda numbers: numbers\nelif callable(sort):\n self.sort = sort\nelse:\n raise TypeError('sort must be a callable method or None')",
"if numbers[0] <= target <= numbers[-1]:\n return True\nreturn False"
] | <|body_start_0|>
if sort is None:
self.sort = lambda numbers: numbers
elif callable(sort):
self.sort = sort
else:
raise TypeError('sort must be a callable method or None')
<|end_body_0|>
<|body_start_1|>
if numbers[0] <= target <= numbers[-1]:
... | An abstract base class for sorted sequence-based search algorithms. | SortedSequenceSearchAlgorithm | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SortedSequenceSearchAlgorithm:
"""An abstract base class for sorted sequence-based search algorithms."""
def __init__(self, sort: Callable=sorted):
"""Initialize a new sortable search algorithm. Args: sort: the sorting method to used (default sorted) Note: passing None to sort will d... | stack_v2_sparse_classes_75kplus_train_065996 | 2,104 | no_license | [
{
"docstring": "Initialize a new sortable search algorithm. Args: sort: the sorting method to used (default sorted) Note: passing None to sort will disable sorting before searching",
"name": "__init__",
"signature": "def __init__(self, sort: Callable=sorted)"
},
{
"docstring": "Return a boolean ... | 2 | null | Implement the Python class `SortedSequenceSearchAlgorithm` described below.
Class description:
An abstract base class for sorted sequence-based search algorithms.
Method signatures and docstrings:
- def __init__(self, sort: Callable=sorted): Initialize a new sortable search algorithm. Args: sort: the sorting method t... | Implement the Python class `SortedSequenceSearchAlgorithm` described below.
Class description:
An abstract base class for sorted sequence-based search algorithms.
Method signatures and docstrings:
- def __init__(self, sort: Callable=sorted): Initialize a new sortable search algorithm. Args: sort: the sorting method t... | 06bff81c5b8942c3b713263b0d417c4d19f1d7b3 | <|skeleton|>
class SortedSequenceSearchAlgorithm:
"""An abstract base class for sorted sequence-based search algorithms."""
def __init__(self, sort: Callable=sorted):
"""Initialize a new sortable search algorithm. Args: sort: the sorting method to used (default sorted) Note: passing None to sort will d... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class SortedSequenceSearchAlgorithm:
"""An abstract base class for sorted sequence-based search algorithms."""
def __init__(self, sort: Callable=sorted):
"""Initialize a new sortable search algorithm. Args: sort: the sorting method to used (default sorted) Note: passing None to sort will disable sortin... | the_stack_v2_python_sparse | venv1/lib/python3.6/site-packages/searching/sequence/sequence_search_algorithm.py | Shawnmhy/BEProject | train | 0 |
9818a7466268e82c9e5cff02176ca64384394c9d | [
"res = 1\np = x\nt = abs(n)\nwhile t:\n print(t, p, res)\n if t & 1:\n res *= p\n p *= p\n t >>= 1\nreturn res if n > 0 else 1 / res",
"res = 1\np = x\nt = abs(n)\ncur = 1\nwhile cur <= t:\n if cur * 2 < t:\n p *= p\n cur *= 2\n else:\n t -= cur\n res *= p\n ... | <|body_start_0|>
res = 1
p = x
t = abs(n)
while t:
print(t, p, res)
if t & 1:
res *= p
p *= p
t >>= 1
return res if n > 0 else 1 / res
<|end_body_0|>
<|body_start_1|>
res = 1
p = x
t = abs(n)... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def myPow(self, x: float, n: int) -> float:
"""二进制拆分 2^15 = 2^8*2^7 = 1111=1000+111 -> 8=1000=((2*2)**2)**2(三次p=p*p) 7=111 -> :param x: :param n: :return:"""
<|body_0|>
def myPow2(self, x: float, n: int) -> float:
"""正向乘方逼近 :param x: :param n: :return:"""
... | stack_v2_sparse_classes_75kplus_train_065997 | 1,990 | no_license | [
{
"docstring": "二进制拆分 2^15 = 2^8*2^7 = 1111=1000+111 -> 8=1000=((2*2)**2)**2(三次p=p*p) 7=111 -> :param x: :param n: :return:",
"name": "myPow",
"signature": "def myPow(self, x: float, n: int) -> float"
},
{
"docstring": "正向乘方逼近 :param x: :param n: :return:",
"name": "myPow2",
"signature":... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def myPow(self, x: float, n: int) -> float: 二进制拆分 2^15 = 2^8*2^7 = 1111=1000+111 -> 8=1000=((2*2)**2)**2(三次p=p*p) 7=111 -> :param x: :param n: :return:
- def myPow2(self, x: floa... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def myPow(self, x: float, n: int) -> float: 二进制拆分 2^15 = 2^8*2^7 = 1111=1000+111 -> 8=1000=((2*2)**2)**2(三次p=p*p) 7=111 -> :param x: :param n: :return:
- def myPow2(self, x: floa... | 5d3574ccd282d0146c83c286ae28d8baaabd4910 | <|skeleton|>
class Solution:
def myPow(self, x: float, n: int) -> float:
"""二进制拆分 2^15 = 2^8*2^7 = 1111=1000+111 -> 8=1000=((2*2)**2)**2(三次p=p*p) 7=111 -> :param x: :param n: :return:"""
<|body_0|>
def myPow2(self, x: float, n: int) -> float:
"""正向乘方逼近 :param x: :param n: :return:"""
... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Solution:
def myPow(self, x: float, n: int) -> float:
"""二进制拆分 2^15 = 2^8*2^7 = 1111=1000+111 -> 8=1000=((2*2)**2)**2(三次p=p*p) 7=111 -> :param x: :param n: :return:"""
res = 1
p = x
t = abs(n)
while t:
print(t, p, res)
if t & 1:
r... | the_stack_v2_python_sparse | 50_Pow(x, n).py | lovehhf/LeetCode | train | 0 | |
7b7b3b49bc50b725f8977e5ca80c15e4091d1bb0 | [
"self.trigramCount = collections.defaultdict(lambda: 0)\nbg = LaplaceBigramLanguageModel(corpus)\nself.bigramCount = bg.bigramCount\nself.uniGram = bg.uniGram.uniDict\nself.train(corpus)\nself.vocab = len(self.bigramCount.keys())",
"for sentence in corpus.corpus:\n line = sentence.data\n for i in range(1, l... | <|body_start_0|>
self.trigramCount = collections.defaultdict(lambda: 0)
bg = LaplaceBigramLanguageModel(corpus)
self.bigramCount = bg.bigramCount
self.uniGram = bg.uniGram.uniDict
self.train(corpus)
self.vocab = len(self.bigramCount.keys())
<|end_body_0|>
<|body_start_1|... | CustomLanguageModel | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CustomLanguageModel:
def __init__(self, corpus):
"""Initialize your data structures in the constructor."""
<|body_0|>
def train(self, corpus):
"""Takes a corpus and trains your language model. Compute any counts or other corpus statistics in this function."""
... | stack_v2_sparse_classes_75kplus_train_065998 | 1,386 | no_license | [
{
"docstring": "Initialize your data structures in the constructor.",
"name": "__init__",
"signature": "def __init__(self, corpus)"
},
{
"docstring": "Takes a corpus and trains your language model. Compute any counts or other corpus statistics in this function.",
"name": "train",
"signat... | 3 | stack_v2_sparse_classes_30k_val_001611 | Implement the Python class `CustomLanguageModel` described below.
Class description:
Implement the CustomLanguageModel class.
Method signatures and docstrings:
- def __init__(self, corpus): Initialize your data structures in the constructor.
- def train(self, corpus): Takes a corpus and trains your language model. Co... | Implement the Python class `CustomLanguageModel` described below.
Class description:
Implement the CustomLanguageModel class.
Method signatures and docstrings:
- def __init__(self, corpus): Initialize your data structures in the constructor.
- def train(self, corpus): Takes a corpus and trains your language model. Co... | c85a5d0caf702efac27a50c17bf7a03c5768be9f | <|skeleton|>
class CustomLanguageModel:
def __init__(self, corpus):
"""Initialize your data structures in the constructor."""
<|body_0|>
def train(self, corpus):
"""Takes a corpus and trains your language model. Compute any counts or other corpus statistics in this function."""
... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class CustomLanguageModel:
def __init__(self, corpus):
"""Initialize your data structures in the constructor."""
self.trigramCount = collections.defaultdict(lambda: 0)
bg = LaplaceBigramLanguageModel(corpus)
self.bigramCount = bg.bigramCount
self.uniGram = bg.uniGram.uniDict
... | the_stack_v2_python_sparse | LanguageModels/src/CustomLanguageModel.py | rahulr56/NLP | train | 0 | |
d68bb7481b214e1616af35579e0acc89f2ed318c | [
"if isinstance(dc, intdisc.discount_calculator):\n self.dc_ = dc\nelse:\n raise ValueError('dc must be of type discount_calculator')\nif options and isinstance(options, dict):\n self.options = options.copy()\nelse:\n raise ValueError('options must be of type dictionary')\nself.dbg = dbg\nself.results = ... | <|body_start_0|>
if isinstance(dc, intdisc.discount_calculator):
self.dc_ = dc
else:
raise ValueError('dc must be of type discount_calculator')
if options and isinstance(options, dict):
self.options = options.copy()
else:
raise ValueError('... | Class for evaliuating quality of discount class | discount_calculator_review | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class discount_calculator_review:
"""Class for evaliuating quality of discount class"""
def __init__(self, options, dc, dbg=True):
"""instantiates review tool"""
<|body_0|>
def review(self):
"""Constructs dictionary of comparisons based on the init_dict specification""... | stack_v2_sparse_classes_75kplus_train_065999 | 3,662 | permissive | [
{
"docstring": "instantiates review tool",
"name": "__init__",
"signature": "def __init__(self, options, dc, dbg=True)"
},
{
"docstring": "Constructs dictionary of comparisons based on the init_dict specification",
"name": "review",
"signature": "def review(self)"
},
{
"docstring... | 3 | null | Implement the Python class `discount_calculator_review` described below.
Class description:
Class for evaliuating quality of discount class
Method signatures and docstrings:
- def __init__(self, options, dc, dbg=True): instantiates review tool
- def review(self): Constructs dictionary of comparisons based on the init... | Implement the Python class `discount_calculator_review` described below.
Class description:
Class for evaliuating quality of discount class
Method signatures and docstrings:
- def __init__(self, options, dc, dbg=True): instantiates review tool
- def review(self): Constructs dictionary of comparisons based on the init... | 2e08363642fd4e2afb5ab76596d75aa10f2a6e3b | <|skeleton|>
class discount_calculator_review:
"""Class for evaliuating quality of discount class"""
def __init__(self, options, dc, dbg=True):
"""instantiates review tool"""
<|body_0|>
def review(self):
"""Constructs dictionary of comparisons based on the init_dict specification""... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class discount_calculator_review:
"""Class for evaliuating quality of discount class"""
def __init__(self, options, dc, dbg=True):
"""instantiates review tool"""
if isinstance(dc, intdisc.discount_calculator):
self.dc_ = dc
else:
raise ValueError('dc must be of t... | the_stack_v2_python_sparse | src/ir_discount_review.py | slpenn13/pythoninterestrates | train | 0 |
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