
"""simple docstring"""


import math


class 			A__	:


     """simple docstring"""

     def 		a__						(	self:						str     ,		__a:						list[list[float]]     ,		__a:						list[int]	)->    int:
           lowerCamelCase					:					Dict        =	0.0
           lowerCamelCase					:					Tuple        =	0.0
           for i in range(len(__a	)	):
                 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:						Dict     ,		__a:						list[list[int | float]]     ,		__a:						list[int]     ,		__a:						int     ,		__a:						float	)->    list[list[int | float]]:
           for i in range(len(__a	)	):
                 weights[j][i] += alpha * (sample[i] - weights[j][i])
           return weights




def   snake_case		(     )   ->      None:
      # Training Examples ( m, n )
      lowerCamelCase					:					Union[str, Any]        =	[[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]

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

      # training
      lowerCamelCase					:					Optional[int]        =	SelfOrganizingMap()
      lowerCamelCase					:					List[Any]        =	3
      lowerCamelCase					:					Tuple        =	0.5

      for _ in range(UpperCamelCase__    ):
            for j in range(len(UpperCamelCase__    )    ):
                  # training sample
                  lowerCamelCase					:					Any        =	training_samples[j]

                  # Compute the winning vector
                  lowerCamelCase					:					Optional[int]        =	self_organizing_map.get_winner(UpperCamelCase__			,				UpperCamelCase__    )

                  # Update the winning vector
                  lowerCamelCase					:					List[Any]        =	self_organizing_map.update(UpperCamelCase__			,				UpperCamelCase__			,				UpperCamelCase__			,				UpperCamelCase__    )

    # classify test sample
      lowerCamelCase					:					Dict        =	[0, 0, 0, 1]
      lowerCamelCase					:					Dict        =	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()








"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


__lowerCamelCase      :str	    =		{}

try:
    if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase      :Optional[Any]	    =		['GPTSw3Tokenizer']


if TYPE_CHECKING:
    try:
        if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_gpt_swa import GPTSwaTokenizer

else:
    import sys

    __lowerCamelCase      :Tuple	    =		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




