

"""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"""



SCREAMING_SNAKE_CASE_					:	str							  =						'0.21.0'

from .accelerator import Accelerator
from .big_modeling import (
    cpu_offload,
    cpu_offload_with_hook,
    disk_offload,
    dispatch_model,
    init_empty_weights,
    init_on_device,
    load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_batches
from .launchers import debug_launcher, notebook_launcher
from .state import PartialState
from .utils import (
    DeepSpeedPlugin,
    DistributedDataParallelKwargs,
    DistributedType,
    FullyShardedDataParallelPlugin,
    GradScalerKwargs,
    InitProcessGroupKwargs,
    find_executable_batch_size,
    infer_auto_device_map,
    is_rich_available,
    load_checkpoint_in_model,
    synchronize_rng_states,
)


if is_rich_available():
 from .utils import rich






