

import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize







def        lowerCAmelCase_  (      __lowerCamelCase			):
							return np.dot(__lowerCamelCase							,			__lowerCamelCase			)

class        a	:




							"""simple docstring"""


							def __init__(							self :						int					,			*,
							    lowerCamelCase :						List[Any] = np.inf					,			lowerCamelCase :						List[str] = "linear"					,			lowerCamelCase :						List[str] = 0.0					,			)			->					None:
														__snake_case	:    Dict											=     regularization
														__snake_case	:    str											=     gamma
														if kernel == "linear":
																					__snake_case	:    Dict											=     self.__linear
														elif kernel == "rbf":
																					if self.gamma == 0:
																												raise ValueError("rbf kernel requires gamma"       )
																					if not isinstance(self.gamma					,			(float, int)       ):
																												raise ValueError("gamma must be float or int"       )
																					if not self.gamma > 0:
																												raise ValueError("gamma must be > 0"       )
																					__snake_case	:    Optional[int]											=     self.__rbf
																					# in the future, there could be a default value like in sklearn
																					# sklear: def_gamma = 1/(n_features * X.var()) (wiki)
																					# previously it was 1/(n_features)
														else:
																					__snake_case	:    Any											=     F'Unknown kernel: {kernel}'
																					raise ValueError(snake_case_       )

							def 					__snake_case      (							self :						int					,			lowerCamelCase :						List[Any]					,			lowerCamelCase :						str       )			->					float:
														return np.dot(snake_case_					,			snake_case_       )

							def 					__snake_case      (							self :						List[Any]					,			lowerCamelCase :						List[str]					,			lowerCamelCase :						List[Any]       )			->					float:
														return np.exp(-(self.gamma * norm_squared(vectora - vectora       ))       )

							def 					__snake_case      (							self :						Tuple					,			lowerCamelCase :						Optional[int]					,			lowerCamelCase :						List[Any]       )			->					None:
														__snake_case	:    Union[str, Any]											=     observations
														__snake_case	:    Any											=     classes

														# using Wolfe's Dual to calculate w.
														# Primal problem: minimize 1/2*norm_squared(w)
														#   constraint: yn(w . xn + b) >= 1
														#
														# With l a vector
														# Dual problem: maximize sum_n(ln) -
														#       1/2 * sum_n(sum_m(ln*lm*yn*ym*xn . xm))
														#   constraint: self.C >= ln >= 0
														#           and sum_n(ln*yn) = 0
														# Then we get w using w = sum_n(ln*yn*xn)
														# At the end we can get b ~= mean(yn - w . xn)
														#
														# Since we use kernels, we only need l_star to calculate b
														# and to classify observations

														((__snake_case						)				,    						)	:    int											=     np.shape(snake_case_       )

														def to_minimize(lowerCamelCase :						str       ) -> float:
																					__snake_case	:    List[Any]											=     0
																					((__snake_case						)				,    						)	:    Union[str, Any]											=     np.shape(snake_case_       )
																					for i in range(snake_case_       ):
																												for j in range(snake_case_       ):
																																			s += (
																																			    candidate[i]
																																			    * candidate[j]
																																			    * classes[i]
																																			    * classes[j]
																																			    * self.kernel(observations[i]					,			observations[j]       )
																																			)
																					return 1 / 2 * s - sum(snake_case_       )

														__snake_case	:    Optional[Any]											=     LinearConstraint(snake_case_					,			0					,			0       )
														__snake_case	:    Any											=     Bounds(0					,			self.regularization       )

														__snake_case	:    Optional[Any]											=     minimize(
														    snake_case_					,			np.ones(snake_case_       )					,			bounds=snake_case_					,			constraints=[ly_contraint]       ).x
														__snake_case	:    List[str]											=     l_star

														# calculating mean offset of separation plane to points
														__snake_case	:    List[Any]											=     0
														for i in range(snake_case_       ):
																					for j in range(snake_case_       ):
																												s += classes[i] - classes[i] * self.optimum[i] * self.kernel(
																												    observations[i]					,			observations[j]       )
														__snake_case	:    Tuple											=     s / n






							def 					__snake_case      (							self :						Tuple					,			lowerCamelCase :						str       )			->					int:
														__snake_case	:    Optional[Any]											=     sum(
														    self.optimum[n]
														    * self.classes[n]
														    * self.kernel(self.observations[n]					,			snake_case_       )
														    for n in range(len(self.classes       )       )       )
														return 1 if s + self.offset >= 0 else -1


if __name__ == "__main__":
			import doctest

			doctest.testmod()





import logging
import os
from logging import (
    CRITICAL,  # NOQA
    DEBUG,  # NOQA
    ERROR,  # NOQA
    FATAL,  # NOQA
    INFO,  # NOQA
    NOTSET,  # NOQA
    WARN,  # NOQA
    WARNING,  # NOQA
)
from typing import Optional

from tqdm import auto as tqdm_lib


_snake_case  :	int			      =       {
    "debug": logging.DEBUG,
    "info": logging.INFO,
    "warning": logging.WARNING,
    "error": logging.ERROR,
    "critical": logging.CRITICAL,
}

_snake_case  :	Dict			      =       logging.WARNING







def        lowerCAmelCase_  (      ):
							__snake_case	:    Union[str, Any]											=     os.getenv("DATASETS_VERBOSITY"							,			__lowerCamelCase			)
							if env_level_str:
														if env_level_str in log_levels:
																					return log_levels[env_level_str]
														else:
																					logging.getLogger().warning(
																					    F'Unknown option DATASETS_VERBOSITY={env_level_str}, '
																					    F'has to be one of: { ", ".join(log_levels.keys()			) }'			)
							return _default_log_level







def        lowerCAmelCase_  (      ):
							return __name__.split("."			)[0]







def        lowerCAmelCase_  (      ):
							return logging.getLogger(_get_library_name()			)







def        lowerCAmelCase_  (      ):
							# Apply our default configuration to the library root logger.
							__snake_case	:    str											=     _get_library_root_logger()
							library_root_logger.setLevel(_get_default_logging_level()			)







def        lowerCAmelCase_  (      ):
							__snake_case	:    Dict											=     _get_library_root_logger()
							library_root_logger.setLevel(logging.NOTSET			)







def        lowerCAmelCase_  (      __lowerCamelCase = None			):
							if name is None:
														__snake_case	:    Tuple											=     _get_library_name()
							return logging.getLogger(__lowerCamelCase			)







def        lowerCAmelCase_  (      ):
							return _get_library_root_logger().getEffectiveLevel()







def        lowerCAmelCase_  (      __lowerCamelCase			):
							_get_library_root_logger().setLevel(__lowerCamelCase			)







def        lowerCAmelCase_  (      ):
							return set_verbosity(__lowerCamelCase			)







def        lowerCAmelCase_  (      ):
							return set_verbosity(__lowerCamelCase			)







def        lowerCAmelCase_  (      ):
							return set_verbosity(__lowerCamelCase			)







def        lowerCAmelCase_  (      ):
							return set_verbosity(__lowerCamelCase			)







def        lowerCAmelCase_  (      ):
							__snake_case	:    List[str]											=     False







def        lowerCAmelCase_  (      ):
							__snake_case	:    Tuple											=     True


# Configure the library root logger at the module level (singleton-like)
_configure_library_root_logger()

class        a	:




							"""simple docstring"""


							def __init__(							self :						int					,			*lowerCamelCase :						Optional[Any]					,			**lowerCamelCase :						List[str]       )			->					Optional[int]:  # pylint: disable=unused-argument
														__snake_case	:    int											=     args[0] if args else None

							def __iter__(							self :						Dict       )			->					Optional[int]:
														return iter(self._iterator       )

							def __getattr__(							self :						int					,			lowerCamelCase :						Optional[Any]       )			->					List[Any]:

														def empty_fn(*lowerCamelCase :						Optional[int]					,			**lowerCamelCase :						List[Any]       ):  # pylint: disable=unused-argument
																					return

														return empty_fn

							def __enter__(							self :						List[str]       )			->					int:
														return self






							def __exit__(							self :						Dict					,			lowerCamelCase :						Any					,			lowerCamelCase :						List[Any]					,			lowerCamelCase :						str       )			->					Dict:
														return
_snake_case  :	Optional[Any]			      =       True

class        a	:




							"""simple docstring"""


							def __call__(							self :						str					,			*lowerCamelCase :						Optional[Any]					,			lowerCamelCase :						Tuple=False					,			**lowerCamelCase :						Union[str, Any]       )			->					Optional[Any]:
														if _tqdm_active and not disable:
																					return tqdm_lib.tqdm(*lowerCamelCase					,			**lowerCamelCase       )
														else:
																					return EmptyTqdm(*lowerCamelCase					,			**lowerCamelCase       )

							def 					__snake_case      (							self :						str					,			*lowerCamelCase :						str					,			**lowerCamelCase :						Tuple       )			->					str:
														__snake_case	:    Optional[Any]											=     None
														if _tqdm_active:
																					return tqdm_lib.tqdm.set_lock(*lowerCamelCase					,			**lowerCamelCase       )






							def 					__snake_case      (							self :						List[Any]       )			->					List[Any]:
														if _tqdm_active:
																					return tqdm_lib.tqdm.get_lock()
_snake_case  :	int			      =       _tqdm_cls()







def        lowerCAmelCase_  (      ):
							global _tqdm_active
							return bool(_tqdm_active			)







def        lowerCAmelCase_  (      ):
							global _tqdm_active
							__snake_case	:    Tuple											=     True







def        lowerCAmelCase_  (      ):
							global _tqdm_active
							__snake_case	:    List[Any]											=     False

