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 :  str      ,   *,
     lowercase__ :  float = np.inf      ,   lowercase__ :  str = "linear"      ,   lowercase__ :  float = 0.0      ,   ):







        '''simple docstring'''



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




 def 		__snake_case						(      self :  List[str]      ,   lowercase__ :  ndarray      ,   lowercase__ :  ndarray):







        '''simple docstring'''



        return np.dot(lowercase__      ,   lowercase__)




 def 		__snake_case						(      self :  Dict      ,   lowercase__ :  ndarray      ,   lowercase__ :  ndarray):







        '''simple docstring'''



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




 def 		__snake_case						(      self :  List[Any]      ,   lowercase__ :  list[ndarray]      ,   lowercase__ :  ndarray):







        '''simple docstring'''



        lowerCAmelCase__					     = observations
        lowerCAmelCase__					     = 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

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

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

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

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

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




 def 		__snake_case						(      self :  List[Any]      ,   lowercase__ :  ndarray):







        '''simple docstring'''



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


if __name__ == "__main__":
    import doctest

    doctest.testmod()

def 				__lowerCamelCase		(						lowerCAmelCase__     ):
       lowerCAmelCase__					     = len(lowerCAmelCase__     )
       for i in range(lowerCAmelCase__     ):
              for j in range(i + 1     ,  lowerCAmelCase__     ):
                     if numbers[j] < numbers[i]:
                            lowerCAmelCase__     , lowerCAmelCase__					     = numbers[j], numbers[i]
       return numbers


if __name__ == "__main__":
    lowerCAmelCase__     						=			input('Enter numbers separated by a comma:\n').strip()
    lowerCAmelCase__     						=			[int(item) for item in user_input.split(',')]
    print(exchange_sort(unsorted))
