



"""simple docstring"""







import math







class       UpperCamelCase__   :




      """simple docstring"""




      def      SCREAMING_SNAKE_CASE__ (      self				:      int    ,			SCREAMING_SNAKE_CASE_				:      list[list[float]]    ,			SCREAMING_SNAKE_CASE_				:      list[int]       ):
        lowerCAmelCase_   :				Optional[Any]								= 0.0
        lowerCAmelCase_   :				Optional[Any]								= 0.0
        for i in range(len(SCREAMING_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      SCREAMING_SNAKE_CASE__ (      self				:      Union[str, Any]    ,			SCREAMING_SNAKE_CASE_				:      list[list[int | float]]    ,			SCREAMING_SNAKE_CASE_				:      list[int]    ,			SCREAMING_SNAKE_CASE_				:      int    ,			SCREAMING_SNAKE_CASE_				:      float       ):
        for i in range(len(SCREAMING_SNAKE_CASE_       )       ):
          weights[j][i] += alpha * (sample[i] - weights[j][i])
        return weights




def 				UpperCamelCase_    (    )						->   None:

  """simple docstring"""




  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[Any]								= SelfOrganizingMap()
  lowerCAmelCase_   :				Tuple								= 3
  lowerCAmelCase_   :				Union[str, Any]								= 0.5

  for _ in range(lowerCAmelCase__						):
    for j in range(len(lowerCAmelCase__						)						):
      # training sample
      lowerCAmelCase_   :				List[Any]								= training_samples[j]

      # Compute the winning vector
      lowerCAmelCase_   :				Union[str, Any]								= self_organizing_map.get_winner(lowerCAmelCase__    ,   lowerCAmelCase__						)

      # Update the winning vector
      lowerCAmelCase_   :				str								= self_organizing_map.update(lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__						)

    # classify test sample
  lowerCAmelCase_   :				List[Any]								= [0, 0, 0, 1]
  lowerCAmelCase_   :				Any								= 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()










"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowercase__		:				int          =	{"""configuration_mbart""": ["""MBART_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MBartConfig""", """MBartOnnxConfig"""]}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase__		:				Optional[int]          =	["""MBartTokenizer"""]

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase__		:				Dict          =	["""MBartTokenizerFast"""]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase__		:				Any          =	[
         """MBART_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """MBartForCausalLM""",
         """MBartForConditionalGeneration""",
         """MBartForQuestionAnswering""",
         """MBartForSequenceClassification""",
         """MBartModel""",
         """MBartPreTrainedModel""",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase__		:				Tuple          =	[
         """TFMBartForConditionalGeneration""",
         """TFMBartModel""",
         """TFMBartPreTrainedModel""",
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase__		:				Tuple          =	[
         """FlaxMBartForConditionalGeneration""",
         """FlaxMBartForQuestionAnswering""",
         """FlaxMBartForSequenceClassification""",
         """FlaxMBartModel""",
         """FlaxMBartPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_mbart import MBART_PRETRAINED_CONFIG_ARCHIVE_MAP, MBartConfig, MBartOnnxConfig

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_mbart import MBartTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_mbart_fast import MBartTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_mbart import (
              MBART_PRETRAINED_MODEL_ARCHIVE_LIST,
              MBartForCausalLM,
              MBartForConditionalGeneration,
              MBartForQuestionAnswering,
              MBartForSequenceClassification,
              MBartModel,
              MBartPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_mbart import TFMBartForConditionalGeneration, TFMBartModel, TFMBartPreTrainedModel

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_mbart import (
              FlaxMBartForConditionalGeneration,
              FlaxMBartForQuestionAnswering,
              FlaxMBartForSequenceClassification,
              FlaxMBartModel,
              FlaxMBartPreTrainedModel,
          )

else:
     import sys

     lowercase__		:				int          =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





