


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



     """simple docstring"""






     return np.dot(UpperCamelCase_							,UpperCamelCase_    )

class   A__ :







    """simple docstring"""

    def __init__(	self      , *,
        __snake_case = np.inf      , __snake_case = "linear"      , __snake_case = 0.0      , ):
         snake_case					=    regularization
         snake_case					=    gamma
         if kernel == "linear":
              snake_case					=    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					=    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					=    F'''Unknown kernel: {kernel}'''
              raise ValueError(__snake_case   )
    def 		a_      (	self      , __snake_case      , __snake_case   ):
         return np.dot(__snake_case      , __snake_case   )
    def 		a_      (	self      , __snake_case      , __snake_case   ):
         return np.exp(-(self.gamma * norm_squared(vectora - vectora   ))   )
    def 		a_      (	self      , __snake_case      , __snake_case   ):
         snake_case					=    observations
         snake_case					=    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)			,  )					=    np.shape(__snake_case   )

         def to_minimize(__snake_case   ) -> float:
              snake_case					=    0
              ((snake_case)			,  )					=    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					=    LinearConstraint(__snake_case      , 0      , 0   )
         snake_case					=    Bounds(0      , self.regularization   )

         snake_case					=    minimize(
             __snake_case      , np.ones(__snake_case   )      , bounds=__snake_case      , constraints=[ly_contraint]   ).x
         snake_case					=    l_star

         # calculating mean offset of separation plane to points
         snake_case					=    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					=    s / n




    def 		a_      (	self      , __snake_case   ):
         snake_case					=    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 re

from filelock import FileLock


try:
 import nltk

 _SCREAMING_SNAKE_CASE      :			Union[str, Any]      				=   True
except (ImportError, ModuleNotFoundError):
 _SCREAMING_SNAKE_CASE      :			Optional[Any]      				=   False

if NLTK_AVAILABLE:
 with FileLock(".lock") as lock:
  nltk.download("punkt", quiet=True)
def       UpperCAmelCase__						(UpperCamelCase_    ):



     """simple docstring"""






     re.sub('''<n>'''							,''''''							,UpperCamelCase_    )  # remove pegasus newline char
     assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
     return "\n".join(nltk.sent_tokenize(UpperCamelCase_    )    )




