code: import random
  
  
  class _lowercase					:
  
  
  
  
  
         """simple docstring"""
  
  
  
  
         def __init__(    self     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase = 0.01     ,   UpperCAmelCase = 1000     ,   UpperCAmelCase = -1     ,   ):
  
  
  
  
  
  
  
                '''simple docstring'''
  
  
  
  
  
  
                _lowercase				=	sample
                if len(self.sample  ) == 0:
                       raise ValueError("""Sample data can not be empty"""  )
                _lowercase				=	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"""  )
                _lowercase				=	learning_rate
                _lowercase				=	epoch_number
                _lowercase				=	bias
                _lowercase				=	len(UpperCAmelCase  )
                _lowercase				=	len(sample[0]  )  # number of columns in dataset
                _lowercase				=	[]
  
  
  
         def     _UpperCAmelCase       (    self  ):
  
  
  
  
  
  
  
                '''simple docstring'''
  
  
  
  
  
  
                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  )
  
                _lowercase				=	0
  
                while True:
                       _lowercase				=	False
                       for i in range(self.number_sample  ):
                              _lowercase				=	0
                              for j in range(self.col_sample + 1  ):
                                     _lowercase				=	u + self.weight[j] * self.sample[i][j]
                              _lowercase				=	self.sign(UpperCAmelCase  )
                              if y != self.target[i]:
                                     for j in range(self.col_sample + 1  ):
                                            _lowercase				=	(
                                                self.weight[j]
                                                + self.learning_rate
                                                * (self.target[i] - y)
                                                * self.sample[i][j]
                                            )
                                     _lowercase				=	True
              # print('Epoch: \n',epoch_count)
                       _lowercase				=	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     _UpperCAmelCase       (    self     ,   UpperCAmelCase  ):
  
  
  
  
  
  
  
                '''simple docstring'''
  
  
  
  
  
  
                if len(self.sample  ) == 0:
                       raise ValueError("""Sample data can not be empty"""  )
                sample.insert(0     ,   self.bias  )
                _lowercase				=	0
                for i in range(self.col_sample + 1  ):
                       _lowercase				=	u + self.weight[i] * sample[i]
  
                _lowercase				=	self.sign(UpperCAmelCase  )
  
                if y == -1:
                       print(("""Sample: """, sample)  )
                       print("""classification: P1"""  )
                else:
                       print(("""Sample: """, sample)  )
                       print("""classification: P2"""  )
  
  
  
         def     _UpperCAmelCase       (    self     ,   UpperCAmelCase  ):
  
  
  
  
  
  
  
                '''simple docstring'''
  
  
  
  
  
  
                return 1 if u >= 0 else -1
  
  
  
  
  
  
  A_:      Optional[int]        =			[
      [-0.6_5_0_8, 0.1_0_9_7, 4.0_0_0_9],
      [-1.4_4_9_2, 0.8_8_9_6, 4.4_0_0_5],
      [2.0_8_5_0, 0.6_8_7_6, 12.0710],
      [0.2_6_2_6, 1.1_4_7_6, 7.7_9_8_5],
      [0.6_4_1_8, 1.0_2_3_4, 7.0_4_2_7],
      [0.2_5_6_9, 0.6_7_3_0, 8.3_2_6_5],
      [1.1_1_5_5, 0.6_0_4_3, 7.4_4_4_6],
      [0.0_9_1_4, 0.3_3_9_9, 7.0_6_7_7],
      [0.0_1_2_1, 0.5_2_5_6, 4.6_3_1_6],
      [-0.0_4_2_9, 0.4_6_6_0, 5.4_3_2_3],
      [0.4_3_4_0, 0.6_8_7_0, 8.2_2_8_7],
      [0.2_7_3_5, 1.0_2_8_7, 7.1_9_3_4],
      [0.4_8_3_9, 0.4_8_5_1, 7.4_8_5_0],
      [0.4_0_8_9, -0.1_2_6_7, 5.5_0_1_9],
      [1.4_3_9_1, 0.1_6_1_4, 8.5_8_4_3],
      [-0.9_1_1_5, -0.1_9_7_3, 2.1_9_6_2],
      [0.3_6_5_4, 1.0_4_7_5, 7.4_8_5_8],
      [0.2_1_4_4, 0.7_5_1_5, 7.1_6_9_9],
      [0.2_0_1_3, 1.0_0_1_4, 6.5_4_8_9],
      [0.6_4_8_3, 0.2_1_8_3, 5.8_9_9_1],
      [-0.1_1_4_7, 0.2_2_4_2, 7.2_4_3_5],
      [-0.7_9_7_0, 0.8_7_9_5, 3.8_7_6_2],
      [-1.0_6_2_5, 0.6_3_6_6, 2.4_7_0_7],
      [0.5_3_0_7, 0.1_2_8_5, 5.6_8_8_3],
      [-1.2_2_0_0, 0.7_7_7_7, 1.7_2_5_2],
      [0.3_9_5_7, 0.1_0_7_6, 5.6_6_2_3],
      [-0.1_0_1_3, 0.5_9_8_9, 7.1_8_1_2],
      [2.4_4_8_2, 0.9_4_5_5, 11.2095],
      [2.0_1_4_9, 0.6_1_9_2, 10.9263],
      [0.2_0_1_2, 0.2_6_1_1, 5.4_6_3_1],
  ]
  
  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=0.0_1, 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)
  
  
  
style_context: import numpy as np
  
  
  def 					__lowerCAmelCase			(  _A ,_A="utf-8" ,_A="surrogatepass"					):
  
  
  
  							"""simple docstring"""
  
  
  							_lowercase				=	bin(int.from_bytes(text.encode(_A ,_A					) ,"""big"""					)					)[2:]
  							return bits.zfill(8 * ((len(_A					) + 7) // 8)					)
  
  
  def 					__lowerCAmelCase			(  _A ,_A="utf-8" ,_A="surrogatepass"					):
  
  
  
  							"""simple docstring"""
  
  
  							_lowercase				=	int(_A ,2					)
  							return n.to_bytes((n.bit_length() + 7) // 8 ,"""big"""					).decode(_A ,_A					) or "\0"
  
  
  def 					__lowerCAmelCase			(  _A ,_A					):
  
  
  
  							"""simple docstring"""
  
  
  							if size_par + len(_A					) <= 2**size_par - (len(_A					) - 1):
  														raise ValueError("""size of parity don't match with size of data"""					)
  
  							_lowercase				=	[]
  							_lowercase				=	[]
  							_lowercase				=	[bin(_A					)[2:] for x in range(1 ,size_par + len(_A					) + 1					)]
  
  							# sorted information data for the size of the output data
  							_lowercase				=	[]
  							# data position template + parity
  							_lowercase				=	[]
  							# parity bit counter
  							_lowercase				=	0
  							# counter position of data bits
  							_lowercase				=	0
  
  							for x in range(1 ,size_par + len(_A					) + 1					):
  														# Performs a template of bit positions - who should be given,
  														# and who should be parity
  														if qtd_bp < size_par:
  																					if (np.log(_A					) / np.log(2					)).is_integer():
  																												data_out_gab.append("""P"""					)
  																												_lowercase				=	qtd_bp + 1
  																					else:
  																												data_out_gab.append("""D"""					)
  														else:
  																					data_out_gab.append("""D"""					)
  
  														# Sorts the data to the new output size
  														if data_out_gab[-1] == "D":
  																					data_ord.append(data[cont_data]					)
  																					cont_data += 1
  														else:
  																					data_ord.append(_A					)
  
      # Calculates parity
  							_lowercase				=	0  # parity bit counter
  							for bp in range(1 ,size_par + 1					):
  														# Bit counter one for a given parity
  														_lowercase				=	0
  														# counter to control the loop reading
  														_lowercase				=	0
  														for x in data_ord:
  																					if x is not None:
  																												try:
  																																			_lowercase				=	(bin_pos[cont_loop])[-1 * (bp)]
  																												except IndexError:
  																																			_lowercase				=	"""0"""
  																												if aux == "1" and x == "1":
  																																			cont_bo += 1
  																					cont_loop += 1
  														parity.append(cont_bo % 2					)
  
  														qtd_bp += 1
  
  							# Mount the message
  							_lowercase				=	0  # parity bit counter
  							for x in range(0 ,size_par + len(_A					)					):
  														if data_ord[x] is None:
  																					data_out.append(str(parity[cont_bp]					)					)
  																					cont_bp += 1
  														else:
  																					data_out.append(data_ord[x]					)
  
  							return data_out
  
  
  def 					__lowerCAmelCase			(  _A ,_A					):
  
  
  
  							"""simple docstring"""
  
  
  							_lowercase				=	[]
  							# Parity bit counter
  							_lowercase				=	0
  							# Counter p data bit reading
  							_lowercase				=	0
  							# list of parity received
  							_lowercase				=	[]
  							_lowercase				=	[]
  
  							for x in range(1 ,len(_A					) + 1					):
  														# Performs a template of bit positions - who should be given,
  														#  and who should be parity
  														if qtd_bp < size_par and (np.log(_A					) / np.log(2					)).is_integer():
  																					data_out_gab.append("""P"""					)
  																					_lowercase				=	qtd_bp + 1
  														else:
  																					data_out_gab.append("""D"""					)
  
  														# Sorts the data to the new output size
  														if data_out_gab[-1] == "D":
  																					data_output.append(data[cont_data]					)
  														else:
  																					parity_received.append(data[cont_data]					)
  														cont_data += 1
  
  							# -----------calculates the parity with the data
  							_lowercase				=	[]
  							_lowercase				=	[]
  							_lowercase				=	[bin(_A					)[2:] for x in range(1 ,size_par + len(_A					) + 1					)]
  
  							#  sorted information data for the size of the output data
  							_lowercase				=	[]
  							# Data position feedback + parity
  							_lowercase				=	[]
  							# Parity bit counter
  							_lowercase				=	0
  							# Counter p data bit reading
  							_lowercase				=	0
  
  							for x in range(1 ,size_par + len(_A					) + 1					):
  														# Performs a template position of bits - who should be given,
  														# and who should be parity
  														if qtd_bp < size_par and (np.log(_A					) / np.log(2					)).is_integer():
  																					data_out_gab.append("""P"""					)
  																					_lowercase				=	qtd_bp + 1
  														else:
  																					data_out_gab.append("""D"""					)
  
  														# Sorts the data to the new output size
  														if data_out_gab[-1] == "D":
  																					data_ord.append(data_output[cont_data]					)
  																					cont_data += 1
  														else:
  																					data_ord.append(_A					)
  
      # Calculates parity
  							_lowercase				=	0  # parity bit counter
  							for bp in range(1 ,size_par + 1					):
  														# Bit counter one for a certain parity
  														_lowercase				=	0
  														# Counter to control loop reading
  														_lowercase				=	0
  														for x in data_ord:
  																					if x is not None:
  																												try:
  																																			_lowercase				=	(bin_pos[cont_loop])[-1 * (bp)]
  																												except IndexError:
  																																			_lowercase				=	"""0"""
  																												if aux == "1" and x == "1":
  																																			cont_bo += 1
  																					cont_loop += 1
  														parity.append(str(cont_bo % 2					)					)
  
  														qtd_bp += 1
  
  							# Mount the message
  							_lowercase				=	0  # Parity bit counter
  							for x in range(0 ,size_par + len(_A					)					):
  														if data_ord[x] is None:
  																					data_out.append(str(parity[cont_bp]					)					)
  																					cont_bp += 1
  														else:
  																					data_out.append(data_ord[x]					)
  
  							_lowercase				=	parity_received == parity
  							return data_output, ack
  
  
  