


import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize
def 				__lowerCamelCase						( UpperCAmelCase_						:		ndarray						):




      """simple docstring"""





      return np.dot(UpperCAmelCase_ ,  UpperCAmelCase_						)







class 		_snake_case  :




 def __init__(		self					,  *,
     _lowerCamelCase = np.inf					,  _lowerCamelCase = "linear"					,  _lowerCamelCase = 0.0					,  ):
       a						:List[str] 							=       regularization
       a						:Optional[Any] 							=       gamma
       if kernel == "linear":
             a						:Optional[Any] 							=       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'''    )
             a						:List[Any] 							=       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:
             a						:Dict 							=       F'''Unknown kernel: {kernel}'''
             raise ValueError(_lowerCamelCase    )




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase    ):
       return np.dot(_lowerCamelCase					,  _lowerCamelCase    )




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase    ):
       return np.exp(-(self.gamma * norm_squared(vectora - vectora    ))    )




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase    ):
       a						:str 							=       observations
       a						: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

       ((a)							,				)						:Tuple 							=       np.shape(_lowerCamelCase    )

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

       a						:str 							=       LinearConstraint(_lowerCamelCase					,  0					,  0    )
       a						:Tuple 							=       Bounds(0					,  self.regularization    )

       a						:List[str] 							=       minimize(
           _lowerCamelCase					,  np.ones(_lowerCamelCase    )					,  bounds=_lowerCamelCase					,  constraints=[ly_contraint]    ).x
       a						:str 							=       l_star

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




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


if __name__ == "__main__":
     import doctest

     doctest.testmod()









import sys

from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core


# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers

snake_case				:    Union[str, Any]       				=     '''python tqdm regex requests packaging filelock numpy tokenizers'''.split()
if sys.version_info < (3, 7):
     pkgs_to_check_at_runtime.append('''dataclasses''')
if sys.version_info < (3, 8):
     pkgs_to_check_at_runtime.append('''importlib_metadata''')

for pkg in pkgs_to_check_at_runtime:
     if pkg in deps:
          if pkg == "tokenizers":
               # must be loaded here, or else tqdm check may fail
               from .utils import is_tokenizers_available

               if not is_tokenizers_available():
                    continue  # not required, check version only if installed

          require_version_core(deps[pkg])
     else:
          raise ValueError(F"""can't find {pkg} in {deps.keys()}, check dependency_versions_table.py""")
def 				__lowerCamelCase						( UpperCAmelCase_						:		Any ,  UpperCAmelCase_						:		Optional[int]=None						):




      """simple docstring"""





      require_version(deps[pkg] ,  UpperCAmelCase_						)





