





'''simple docstring'''







from __future__ import annotations

from typing import TypedDict
class 				__SCREAMING_SNAKE_CASE  (lowerCamelCase_      ):






				"""simple docstring"""







				__a            =42
				__a            =42






def  _lowerCamelCase				( lowercase						:      str						)	->	list[str]:
				if not isinstance(lowercase    ,							lowercase						):
								raise TypeError("The parameter s type must be str."						)

				return [s[i:] + s[:i] for i in range(len(lowercase						)						)]






def  _lowerCamelCase				( lowercase						:      str						)	->	BWTTransformDict:
				if not isinstance(lowercase    ,							lowercase						):
								raise TypeError("The parameter s type must be str."						)
				if not s:
								raise ValueError("The parameter s must not be empty."						)

				_a								= all_rotations(lowercase						)
				rotations.sort()  # sort the list of rotations in alphabetically order
				# make a string composed of the last char of each rotation
				_a								= {
				    "bwt_string": "".join([word[-1] for word in rotations]						),
				    "idx_original_string": rotations.index(lowercase						),
				}
				return response






def  _lowerCamelCase				( lowercase						:      str    ,							lowercase						:      int						)	->	str:
				if not isinstance(lowercase    ,							lowercase						):
								raise TypeError("The parameter bwt_string type must be str."						)
				if not bwt_string:
								raise ValueError("The parameter bwt_string must not be empty."						)
				try:
								_a								= int(lowercase						)
				except ValueError:
								raise TypeError(
								    "The parameter idx_original_string type must be int or passive"
								    " of cast to int."						)
				if idx_original_string < 0:
								raise ValueError("The parameter idx_original_string must not be lower than 0."						)
				if idx_original_string >= len(lowercase						):
								raise ValueError(
								    "The parameter idx_original_string must be lower than" " len(bwt_string)."						)

				_a								= [""] * len(lowercase						)
				for _ in range(len(lowercase						)						):
								for i in range(len(lowercase						)						):
												_a								= bwt_string[i] + ordered_rotations[i]
								ordered_rotations.sort()
				return ordered_rotations[idx_original_string]


if __name__ == "__main__":
					lowerCAmelCase_					:    str      	=			'Provide a string that I will generate its BWT transform: '
					lowerCAmelCase_					:    List[str]      	=			input(entry_msg).strip()
					lowerCAmelCase_					:    List[str]      	=			bwt_transform(s)
					print(
					    f"""Burrows Wheeler transform for string '{s}' results """
					    f"""in '{result['bwt_string']}'"""
					)
					lowerCAmelCase_					:    Optional[Any]      	=			reverse_bwt(result['bwt_string'], result['idx_original_string'])
					print(
					    f"""Reversing Burrows Wheeler transform for entry '{result['bwt_string']}' """
					    f"""we get original string '{original_string}'"""
					)







'''simple docstring'''







import math
class 				__SCREAMING_SNAKE_CASE  :






				"""simple docstring"""







				def   UpperCamelCase__   (  self     :					List[str]					,				__a     :					list[list[float]]					,				__a     :					list[int]					):
								_a								= 0.0
								_a								= 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   UpperCamelCase__   (  self     :					List[Any]					,				__a     :					list[list[int | float]]					,				__a     :					list[int]					,				__a     :					int					,				__a     :					float					):
								for i in range(len(__a					)					):
												weights[j][i] += alpha * (sample[i] - weights[j][i])
								return weights






def  _lowerCamelCase				( )	->	None:
				# 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(lowercase						):
								for j in range(len(lowercase						)						):
												# training sample
												_a								= training_samples[j]

												# Compute the winning vector
												_a								= self_organizing_map.get_winner(lowercase    ,							lowercase						)

												# Update the winning vector
												_a								= self_organizing_map.update(lowercase    ,							lowercase    ,							lowercase    ,							lowercase						)

    # classify test sample
				_a								= [0, 0, 0, 1]
				_a								= self_organizing_map.get_winner(lowercase    ,							lowercase						)

				# 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()
