





"""simple docstring"""




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




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		)    ->       float:
  return np.dot(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)
class     SCREAMING_SNAKE_CASE       :


   def __init__(   self      :    Optional[Any]      ,					*,
       a      :    float = np.inf      ,					a      :    str = "linear"      ,					a      :    float = 0.0      ,					)->       None:





     """simple docstring"""







     lowercase__           =  regularization
     lowercase__           =  gamma
     if kernel == "linear":
       lowercase__           =  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'	)
       lowercase__           =  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:
       lowercase__           =  f"""Unknown kernel: {kernel}"""
       raise ValueError(a	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Dict      ,					a      :    ndarray      ,					a      :    ndarray	)->       float:





     """simple docstring"""







     return np.dot(a      ,					a	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    List[Any]      ,					a      :    ndarray      ,					a      :    ndarray	)->       float:





     """simple docstring"""







     return np.exp(-(self.gamma * norm_squared(vectora - vectora	))	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    str      ,					a      :    list[ndarray]      ,					a      :    ndarray	)->       None:





     """simple docstring"""







     lowercase__           =  observations
     lowercase__           =  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

     ((lowercase__   )      ,      )           =  np.shape(a	)

     def to_minimize(a      :    ndarray	) -> float:
       lowercase__           =  0
       ((lowercase__   )      ,      )           =  np.shape(a	)
       for i in range(a	):
         for j in range(a	):
           s += (
               candidate[i]
               * candidate[j]
               * classes[i]
               * classes[j]
               * self.kernel(observations[i]      ,					observations[j]	)
           )
       return 1 / 2 * s - sum(a	)

     lowercase__           =  LinearConstraint(a      ,					0      ,					0	)
     lowercase__           =  Bounds(0      ,					self.regularization	)

     lowercase__           =  minimize(
         a      ,					np.ones(a	)      ,					bounds=a      ,					constraints=[ly_contraint]	).x
     lowercase__           =  l_star

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


   def 	SCREAMING_SNAKE_CASE_							(   self      :    List[str]      ,					a      :    ndarray	)->       int:





     """simple docstring"""







     lowercase__           =  sum(
         self.optimum[n]
         * self.classes[n]
         * self.kernel(self.observations[n]      ,					a	)
         for n in range(len(self.classes	)	)	)
     return 1 if s + self.offset >= 0 else -1


if __name__ == "__main__":
      import doctest

      doctest.testmod()









from typing import Callable, Optional

from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream
class     SCREAMING_SNAKE_CASE       (UpperCAmelCase				):


   def __init__(   self      :    List[str]      ,					a      :    Callable      ,					a      :    Optional[Features] = None      ,					a      :    str = None      ,					a      :    bool = False      ,					a      :    bool = False      ,					a      :    Optional[dict] = None      ,					a      :    Optional[int] = None      ,					**a      :    str      ,					)->       Tuple:





     """simple docstring"""







     super().__init__(
         features=a      ,					cache_dir=a      ,					keep_in_memory=a      ,					streaming=a      ,					num_proc=a      ,					**a      ,					)
     lowercase__           =  Generator(
         cache_dir=a      ,					features=a      ,					generator=a      ,					gen_kwargs=a      ,					**a      ,					)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Any	)->       Dict:





     """simple docstring"""







     if self.streaming:
       lowercase__           =  self.builder.as_streaming_dataset(split='train'	)
     # Build regular (map-style) dataset
     else:
       lowercase__           =  None
       lowercase__           =  None
       lowercase__           =  None
       lowercase__           =  None

       self.builder.download_and_prepare(
           download_config=a      ,					download_mode=a      ,					verification_mode=a      ,					base_path=a      ,					num_proc=self.num_proc      ,					)
       lowercase__           =  self.builder.as_dataset(
           split='train'      ,					verification_mode=a      ,					in_memory=self.keep_in_memory	)
     return dataset


