





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




def 				snake_case_      (		SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  return np.dot(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )


class       __SCREAMING_SNAKE_CASE	:



     '''simple docstring'''






     def __init__(					self						,   *,
         lowerCamelCase = np.inf						,   lowerCamelCase = "linear"						,   lowerCamelCase = 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(A_	)

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase	):
       return np.dot(A_						,   A_	)

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase	):
       return np.exp(-(self.gamma * norm_squared(vectora - vectora	))	)

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase	):
       _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(A_	)

       def to_minimize(lowerCamelCase	) -> float:
         _snake_case     		=				0
         ((_snake_case	)		,			)     		=				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_	)

       _snake_case     		=				LinearConstraint(A_						,   0						,   0	)
       _snake_case     		=				Bounds(0						,   self.regularization	)

       _snake_case     		=				minimize(
           A_						,   np.ones(A_	)						,   bounds=A_						,   constraints=[ly_contraint]	).x
       _snake_case     		=				l_star

       # calculating mean offset of separation plane to points
       _snake_case     		=				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]	)
       _snake_case     		=				s / n






     def 	UpperCamelCase(					self						,   lowerCamelCase	):
       _snake_case     		=				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()









'''simple docstring'''



import math


class       __SCREAMING_SNAKE_CASE	:



     '''simple docstring'''






     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase	):
       _snake_case     		=				0.0
       _snake_case     		=				0.0
       for i in range(len(lowerCamelCase	)	):
         da += math.pow((sample[i] - weights[0][i])						,   2	)
         da += math.pow((sample[i] - weights[1][i])						,   2	)
         return 0 if da > da else 1
       return 0






     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase	):
       for i in range(len(lowerCamelCase	)	):
         weights[j][i] += alpha * (sample[i] - weights[j][i])
       return weights







def 				snake_case_      (		):







  '''simple docstring'''



  _snake_case     		=				[[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]

  # weight initialization ( n, C )
  _snake_case     		=				[[0.2, 0.6, 0.5, 0.9], [0.8, 0.4, 0.7, 0.3]]

  # training
  _snake_case     		=				SelfOrganizingMap()
  _snake_case     		=				3
  _snake_case     		=				0.5

  for _ in range(SCREAMING_SNAKE_CASE__       ):
    for j in range(len(SCREAMING_SNAKE_CASE__       )       ):
      # training sample
      _snake_case     		=				training_samples[j]

      # Compute the winning vector
      _snake_case     		=				self_organizing_map.get_winner(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )

      # Update the winning vector
      _snake_case     		=				self_organizing_map.update(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )

    # classify test sample
  _snake_case     		=				[0, 0, 0, 1]
  _snake_case     		=				self_organizing_map.get_winner(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )

  # results
  print(f'''Clusters that the test sample belongs to : {winner}'''       )
  print(f'''Weights that have been trained : {weights}'''       )


# running the main() function
if __name__ == "__main__":
 main()


