




"""simple docstring"""




import random
class 							lowerCamelCase		:







   def __init__(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ = 0.01    ,      SCREAMING_SNAKE_CASE_ = 1000    ,      SCREAMING_SNAKE_CASE_ = -1    ,      ):
       UpperCamelCase				:   Tuple          =							sample
       if len(self.sample ) == 0:
           raise ValueError("""Sample data can not be empty""" )
       UpperCamelCase				:   List[Any]          =							target
       if len(self.target ) == 0:
           raise ValueError("""Target data can not be empty""" )
       if len(self.sample ) != len(self.target ):
           raise ValueError("""Sample data and Target data do not have matching lengths""" )
       UpperCamelCase				:   List[str]          =							learning_rate
       UpperCamelCase				:   Tuple          =							epoch_number
       UpperCamelCase				:   List[Any]          =							bias
       UpperCamelCase				:   Optional[int]          =							len(SCREAMING_SNAKE_CASE_ )
       UpperCamelCase				:   str          =							len(sample[0] )  # number of columns in dataset
       UpperCamelCase				:   Dict          =							[]







   def       a_							(							self ):






       for sample in self.sample:
           sample.insert(0    ,      self.bias )

       for _ in range(self.col_sample ):
           self.weight.append(random.random() )

       self.weight.insert(0    ,      self.bias )

       UpperCamelCase				:   Optional[int]          =							0

       while True:
           UpperCamelCase				:   Tuple          =							False
           for i in range(self.number_sample ):
               UpperCamelCase				:   List[str]          =							0
               for j in range(self.col_sample + 1 ):
                   UpperCamelCase				:   Optional[Any]          =							u + self.weight[j] * self.sample[i][j]
               UpperCamelCase				:   List[Any]          =							self.sign(SCREAMING_SNAKE_CASE_ )
               if y != self.target[i]:
                   for j in range(self.col_sample + 1 ):
                       UpperCamelCase				:   List[str]          =							(
                           self.weight[j]
                           + self.learning_rate
                           * (self.target[i] - y)
                           * self.sample[i][j]
                       )
                   UpperCamelCase				:   List[Any]          =							True
            # print('Epoch: \n',epoch_count)
           UpperCamelCase				:   List[Any]          =							epoch_count + 1
           # if you want control the epoch or just by error
           if not has_misclassified:
               print(("""\nEpoch:\n""", epoch_count) )
               print("""------------------------\n""" )
               # if epoch_count > self.epoch_number or not error:
               break







   def       a_							(							self    ,      SCREAMING_SNAKE_CASE_ ):






       if len(self.sample ) == 0:
           raise ValueError("""Sample data can not be empty""" )
       sample.insert(0    ,      self.bias )
       UpperCamelCase				:   str          =							0
       for i in range(self.col_sample + 1 ):
           UpperCamelCase				:   Dict          =							u + self.weight[i] * sample[i]

       UpperCamelCase				:   Any          =							self.sign(SCREAMING_SNAKE_CASE_ )

       if y == -1:
           print(("""Sample: """, sample) )
           print("""classification: P1""" )
       else:
           print(("""Sample: """, sample) )
           print("""classification: P2""" )







   def       a_							(							self    ,      SCREAMING_SNAKE_CASE_ ):
       return 1 if u >= 0 else -1







__A :		Optional[int]      							=    [
    [-0.6508, 0.1097, 4.0009],
    [-1.4492, 0.8896, 4.4005],
    [2.0850, 0.6876, 12.0710],
    [0.2626, 1.1476, 7.7985],
    [0.6418, 1.0234, 7.0427],
    [0.2569, 0.6730, 8.3265],
    [1.1155, 0.6043, 7.4446],
    [0.0914, 0.3399, 7.0677],
    [0.0121, 0.5256, 4.6316],
    [-0.0429, 0.4660, 5.4323],
    [0.4340, 0.6870, 8.2287],
    [0.2735, 1.0287, 7.1934],
    [0.4839, 0.4851, 7.4850],
    [0.4089, -0.1267, 5.5019],
    [1.4391, 0.1614, 8.5843],
    [-0.9115, -0.1973, 2.1962],
    [0.3654, 1.0475, 7.4858],
    [0.2144, 0.7515, 7.1699],
    [0.2013, 1.0014, 6.5489],
    [0.6483, 0.2183, 5.8991],
    [-0.1147, 0.2242, 7.2435],
    [-0.7970, 0.8795, 3.8762],
    [-1.0625, 0.6366, 2.4707],
    [0.5307, 0.1285, 5.6883],
    [-1.2200, 0.7777, 1.7252],
    [0.3957, 0.1076, 5.6623],
    [-0.1013, 0.5989, 7.1812],
    [2.4482, 0.9455, 11.2095],
    [2.0149, 0.6192, 10.9263],
    [0.2012, 0.2611, 5.4631],
]

__A :		Optional[int]      							=    [
    -1,
    -1,
    -1,
    1,
    1,
    -1,
    1,
    -1,
    1,
    1,
    -1,
    1,
    -1,
    -1,
    -1,
    -1,
    1,
    1,
    1,
    1,
    -1,
    1,
    1,
    1,
    1,
    -1,
    -1,
    1,
    -1,
    1,
]


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       __A :		str      							=    Perceptron(
           sample=samples, target=target, learning_rate=0.01, epoch_number=1000, bias=-1
       )
       network.training()
       print('''Finished training perceptron''')
       print('''Enter values to predict or q to exit''')
       while True:
              __A :		list      							=    []
              for i in range(len(samples[0])):
                     __A :		Dict      							=    input('''value: ''').strip()
                     if user_input == "q":
                            break
                     __A :		Union[str, Any]      							=    float(user_input)
                     sample.insert(i, observation)
              network.sort(sample)










"""simple docstring"""




import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def 					A_			(		snake_case_     :       float = np.pi / 2						,snake_case_     :       float = np.pi / 2						,snake_case_     :       float = np.pi / 2    ):







    '''simple docstring'''
    UpperCamelCase				:   Union[str, Any]          =							QuantumRegister(3						,"""qr"""    )  # Define the number of quantum bits
    UpperCamelCase				:   Any          =							ClassicalRegister(1						,"""cr"""    )  # Define the number of classical bits

    UpperCamelCase				:   Any          =							QuantumCircuit(snake_case_						,snake_case_    )  # Define the quantum circuit.

    # Build the circuit
    quantum_circuit.u(snake_case_						,snake_case_						,snake_case_						,0    )  # Quantum State to teleport
    quantum_circuit.h(1    )  # add hadamard gate
    quantum_circuit.cx(
        1						,2    )  # add control gate with qubit 1 as control and 2 as target.
    quantum_circuit.cx(0						,1    )
    quantum_circuit.h(0    )
    quantum_circuit.cz(0						,2    )  # add control z gate.
    quantum_circuit.cx(1						,2    )

    quantum_circuit.measure([2]						,[0]    )  # measure the qubit.

    # Simulate the circuit using qasm simulator
    UpperCamelCase				:   Tuple          =							Aer.get_backend("""aer_simulator"""    )
    UpperCamelCase				:   Any          =							execute(snake_case_						,snake_case_						,shots=1_0_0_0    )

    return job.result().get_counts(snake_case_    )


if __name__ == "__main__":
  print(
      '''Total count for teleported state is: '''
      F'''{quantum_teleportation(np.pi/2, np.pi/2, np.pi/2)}'''
  )




