





'''simple docstring'''
# flake8: noqa
# Lint as: python3
a__							:							Union[str, Any] =[
    '''VerificationMode''',
    '''Version''',
    '''disable_progress_bar''',
    '''enable_progress_bar''',
    '''is_progress_bar_enabled''',
    '''experimental''',
]

from .info_utils import VerificationMode
from .logging import disable_progress_bar, enable_progress_bar, is_progress_bar_enabled
from .version import Version
from .experimental import experimental




'''simple docstring'''
import math




class 			lowerCamelCase_   :






      def lowercase_							(					self				:    Optional[Any]						,						_A				:    list[list[float]]						,						_A				:    list[int] ):


          '''simple docstring'''


          UpperCAmelCase__			:		List[str]													=				0.0
          UpperCAmelCase__			:		Union[str, Any]													=				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 lowercase_							(					self				:    Dict						,						_A				:    list[list[int | float]]						,						_A				:    list[int]						,						_A				:    int						,						_A				:    float ):


          '''simple docstring'''


          for i in range(len(_A ) ):
              weights[j][i] += alpha * (sample[i] - weights[j][i])
          return weights





def 					a__       (					)    ->	None:
    # Training Examples ( m, n )
    UpperCAmelCase__			:		Optional[Any]													=				[[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]

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

    # training
    UpperCAmelCase__			:		Optional[Any]													=				SelfOrganizingMap()
    UpperCAmelCase__			:		Tuple													=				3
    UpperCAmelCase__			:		Dict													=				0.5

    for _ in range(lowerCAmelCase__						):
        for j in range(len(lowerCAmelCase__						)						):
            # training sample
            UpperCAmelCase__			:		Optional[int]													=				training_samples[j]

            # Compute the winning vector
            UpperCAmelCase__			:		str													=				self_organizing_map.get_winner(lowerCAmelCase__  ,					lowerCAmelCase__						)

            # Update the winning vector
            UpperCAmelCase__			:		int													=				self_organizing_map.update(lowerCAmelCase__  ,					lowerCAmelCase__  ,					lowerCAmelCase__  ,					lowerCAmelCase__						)

    # classify test sample
    UpperCAmelCase__			:		str													=				[0, 0, 0, 1]
    UpperCAmelCase__			:		int													=				self_organizing_map.get_winner(lowerCAmelCase__  ,					lowerCAmelCase__						)

    # 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()
