





'''simple docstring'''







def  _lowerCamelCase				( lowercase						:      float    ,							lowercase						:      float    ,							lowercase						:      float    ,							lowercase						:      float    ,							lowercase						:      float    ,							)	->	float:
				_a								= [redshift, radiation_density, matter_density, dark_energy]
				if any(p < 0 for p in parameters						):
								raise ValueError("All input parameters must be positive"						)

				if any(p > 1 for p in parameters[1:4]						):
								raise ValueError("Relative densities cannot be greater than one"						)
				else:
								_a								= 1 - (matter_density + radiation_density + dark_energy)

								_a								= (
								    radiation_density * (redshift + 1) ** 4
								    + matter_density * (redshift + 1) ** 3
								    + curvature * (redshift + 1) ** 2
								    + dark_energy
								)

								_a								= hubble_constant * e_a ** (1 / 2)
								return hubble


if __name__ == "__main__":
					import doctest

					# run doctest
					doctest.testmod()

					# demo LCDM approximation
					lowerCAmelCase_					:    Optional[Any]      	=			0.3

					print(
					    hubble_parameter(
					        hubble_constant=68.3,
					        radiation_density=1e-4,
					        matter_density=matter_density,
					        dark_energy=1 - matter_density,
					        redshift=0,
					    )
					)







'''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()
