


from ..utils import DummyObject, requires_backends

class   A__ (   metaclass=SCREAMING_SNAKE_CASE__     ):







    """simple docstring"""

    __magic_name__        =		['''torch''', '''scipy''']
    def __init__(	self      , *__snake_case      , **__snake_case   ):
         requires_backends(self      , ['''torch''', '''scipy''']   )
    @classmethod
    def 		a_      (	cls      , *__snake_case      , **__snake_case   ):
         requires_backends(cls      , ['''torch''', '''scipy''']   )




    @classmethod
    def 		a_      (	cls      , *__snake_case      , **__snake_case   ):
         requires_backends(cls      , ['''torch''', '''scipy''']   )








import math

class   A__ :







    """simple docstring"""

    def 		a_      (	self      , __snake_case      , __snake_case   ):
         snake_case					=    0.0
         snake_case					=    0.0
         for i in range(len(__snake_case   )   ):
              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 		a_      (	self      , __snake_case      , __snake_case      , __snake_case      , __snake_case   ):
         for i in range(len(__snake_case   )   ):
              weights[j][i] += alpha * (sample[i] - weights[j][i])
         return weights






def       UpperCAmelCase__						():



     """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(UpperCamelCase_    ):
          for j in range(len(UpperCamelCase_    )    ):
               # training sample
               snake_case					=    training_samples[j]

               # Compute the winning vector
               snake_case					=    self_organizing_map.get_winner(UpperCamelCase_							,UpperCamelCase_    )

               # Update the winning vector
               snake_case					=    self_organizing_map.update(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_    )

    # classify test sample
     snake_case					=    [0, 0, 0, 1]
     snake_case					=    self_organizing_map.get_winner(UpperCamelCase_							,UpperCamelCase_    )

     # 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()




