


import math

import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase = 3				)    ->				qiskit.result.counts.Counts:
  if isinstance(__lowerCAmelCase							,   __lowerCAmelCase				):
    raise TypeError('''number of qubits must be a integer.'''				)
  if number_of_qubits <= 0:
    raise ValueError('''number of qubits must be > 0.'''				)
  if math.floor(__lowerCAmelCase				) != number_of_qubits:
    raise ValueError('''number of qubits must be exact integer.'''				)
  if number_of_qubits > 10:
    raise ValueError('''number of qubits too large to simulate(>10).'''				)

  snake_case__						= QuantumRegister(__lowerCAmelCase							,   '''qr'''				)
  snake_case__						= ClassicalRegister(__lowerCAmelCase							,   '''cr'''				)

  snake_case__						= QuantumCircuit(__lowerCAmelCase							,   __lowerCAmelCase				)

  snake_case__						= number_of_qubits

  for i in range(__lowerCAmelCase				):
    quantum_circuit.h(number_of_qubits - i - 1				)
    counter -= 1
    for j in range(__lowerCAmelCase				):
      quantum_circuit.cp(np.pi / 2 ** (counter - j)							,   __lowerCAmelCase							,   __lowerCAmelCase				)

  for k in range(number_of_qubits // 2				):
    quantum_circuit.swap(__lowerCAmelCase							,   number_of_qubits - k - 1				)

  # measure all the qubits
  quantum_circuit.measure(__lowerCAmelCase							,   __lowerCAmelCase				)
  # simulate with 10000 shots
  snake_case__						= Aer.get_backend('''qasm_simulator'''				)
  snake_case__						= execute(__lowerCAmelCase							,   __lowerCAmelCase							,   shots=1_0000				)

  return job.result().get_counts(__lowerCAmelCase				)


if __name__ == "__main__":
   print(
       F"""Total count for quantum fourier transform state is: \
    {quantum_fourier_transform(3)}"""
   )





import math
class 		__magic_name__						:





     '''simple docstring'''



     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[int]     ,		_a:list[list[float]]     ,		_a:list[int]   ):
       snake_case__						= 0.0
       snake_case__						= 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 	SCREAMING_SNAKE_CASE__						(					self:Tuple     ,		_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 SCREAMING_SNAKE_CASE	(			)    ->				None:
  # Training Examples ( m, n )
  snake_case__						= [[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]

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

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

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

      # Compute the winning vector
      snake_case__						= self_organizing_map.get_winner(__lowerCAmelCase							,   __lowerCAmelCase				)

      # Update the winning vector
      snake_case__						= self_organizing_map.update(__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase				)

    # classify test sample
  snake_case__						= [0, 0, 0, 1]
  snake_case__						= 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()

