

"""simple docstring"""



import math



class 					a						:



    """simple docstring"""



    def 				UpperCamelCase	(     self: Tuple    ,						UpperCamelCase: list[list[float]]    ,						UpperCamelCase: list[int]       ):






         """simple docstring"""


         A__										=  0.0
         A__										=  0.0
         for i in range(len(UpperCamelCase       )       ):
              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: Any    ,						UpperCamelCase: list[list[int | float]]    ,						UpperCamelCase: list[int]    ,						UpperCamelCase: int    ,						UpperCamelCase: float       ):






         """simple docstring"""


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





def  _snake_case     (    ):
     # Training Examples ( m, n )
     A__										=  [[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]

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

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

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

               # Compute the winning vector
               A__										=  self_organizing_map.get_winner(UpperCAmelCase_					,   UpperCAmelCase_			)

               # Update the winning vector
               A__										=  self_organizing_map.update(UpperCAmelCase_					,   UpperCAmelCase_					,   UpperCAmelCase_					,   UpperCAmelCase_			)

    # classify test sample
     A__										=  [0, 0, 0, 1]
     A__										=  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_torch_available


SCREAMING_SNAKE_CASE_					:	Any							  =						{
    'configuration_jukebox': [
        'JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'JukeboxConfig',
        'JukeboxPriorConfig',
        'JukeboxVQVAEConfig',
    ],
    'tokenization_jukebox': ['JukeboxTokenizer'],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE_					:	Union[str, Any]							  =						[
     'JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST',
     'JukeboxModel',
     'JukeboxPreTrainedModel',
     'JukeboxVQVAE',
     'JukeboxPrior',
 ]

if TYPE_CHECKING:
 from .configuration_jukebox import (
     JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP,
     JukeboxConfig,
     JukeboxPriorConfig,
     JukeboxVQVAEConfig,
 )
 from .tokenization_jukebox import JukeboxTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_jukebox import (
      JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST,
      JukeboxModel,
      JukeboxPreTrainedModel,
      JukeboxPrior,
      JukeboxVQVAE,
  )

else:
 import sys

 SCREAMING_SNAKE_CASE_					:	Optional[int]							  =						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






