code: 
  
  
  import random
  
  
  
  class   __lowerCamelCase :
  
  
  
  
  
  
  
         def __init__( self:   Optional[Any],A_:   list[list[float]],A_:   list[int],A_:   float = 0.0_1,A_:   int = 1000,A_:   float = -1,):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              __UpperCamelCase   = sample
              if len(self.sample				) == 0:
                   raise ValueError('Sample data can not be empty'				)
              __UpperCamelCase   = 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   = learning_rate
              __UpperCamelCase   = epoch_number
              __UpperCamelCase   = bias
              __UpperCamelCase   = len(A_				)
              __UpperCamelCase   = len(sample[0]				)  # number of columns in dataset
              __UpperCamelCase   = []
  
  
  
  
  
  
  
         def 		snake_case_  ( self:   Optional[Any]				):
  
  
  
  
  
  
              '''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				)
  
              __UpperCamelCase   = 0
  
              while True:
                   __UpperCamelCase   = False
                   for i in range(self.number_sample				):
                        __UpperCamelCase   = 0
                        for j in range(self.col_sample + 1				):
                             __UpperCamelCase   = u + self.weight[j] * self.sample[i][j]
                        __UpperCamelCase   = self.sign(A_				)
                        if y != self.target[i]:
                             for j in range(self.col_sample + 1				):
                                  __UpperCamelCase   = (
                                      self.weight[j]
                                      + self.learning_rate
                                      * (self.target[i] - y)
                                      * self.sample[i][j]
                                  )
                             __UpperCamelCase   = True
              # print('Epoch: \n',epoch_count)
                   __UpperCamelCase   = 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 		snake_case_  ( self:   str,A_:   list[float]				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              if len(self.sample				) == 0:
                   raise ValueError('Sample data can not be empty'				)
              sample.insert(0,self.bias				)
              __UpperCamelCase   = 0
              for i in range(self.col_sample + 1				):
                   __UpperCamelCase   = u + self.weight[i] * sample[i]
  
              __UpperCamelCase   = self.sign(A_				)
  
              if y == -1:
                   print(('Sample: ', sample)				)
                   print('classification: P1'				)
              else:
                   print(('Sample: ', sample)				)
                   print('classification: P2'				)
  
  
  
  
  
  
  
         def 		snake_case_  ( self:   Union[str, Any],A_:   float				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              return 1 if u >= 0 else -1
  
  
  
  __snake_case             =	[
      [-0.6_508, 0.1_097, 4.0_009],
      [-1.4_492, 0.8_896, 4.4_005],
      [2.0_850, 0.6_876, 12.0_710],
      [0.2_626, 1.1_476, 7.7_985],
      [0.6_418, 1.0_234, 7.0_427],
      [0.2_569, 0.6_730, 8.3_265],
      [1.1_155, 0.6_043, 7.4_446],
      [0.0_914, 0.3_399, 7.0_677],
      [0.0_121, 0.5_256, 4.6_316],
      [-0.0_429, 0.4_660, 5.4_323],
      [0.4_340, 0.6_870, 8.2_287],
      [0.2_735, 1.0_287, 7.1_934],
      [0.4_839, 0.4_851, 7.4_850],
      [0.4_089, -0.1_267, 5.5_019],
      [1.4_391, 0.1_614, 8.5_843],
      [-0.9_115, -0.1_973, 2.1_962],
      [0.3_654, 1.0_475, 7.4_858],
      [0.2_144, 0.7_515, 7.1_699],
      [0.2_013, 1.0_014, 6.5_489],
      [0.6_483, 0.2_183, 5.8_991],
      [-0.1_147, 0.2_242, 7.2_435],
      [-0.7_970, 0.8_795, 3.8_762],
      [-1.0_625, 0.6_366, 2.4_707],
      [0.5_307, 0.1_285, 5.6_883],
      [-1.2_200, 0.7_777, 1.7_252],
      [0.3_957, 0.1_076, 5.6_623],
      [-0.1_013, 0.5_989, 7.1_812],
      [2.4_482, 0.9_455, 11.2_095],
      [2.0_149, 0.6_192, 10.9_263],
      [0.2_012, 0.2_611, 5.4_631],
  ]
  
  __snake_case             =	[
      -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()
  
      __snake_case             =	Perceptron(
          sample=samples, target=target, learning_rate=0.01, epoch_number=1_0_0_0, bias=-1
      )
      network.training()
      print('''Finished training perceptron''')
      print('''Enter values to predict or q to exit''')
      while True:
          __snake_case             =	[]
          for i in range(len(samples[0])):
              __snake_case             =	input('''value: ''').strip()
              if user_input == "q":
                  break
              __snake_case             =	float(user_input)
              sample.insert(i, observation)
          network.sort(sample)
  
  
  
style_context: 
  
  
  from __future__ import annotations
  
  
  
  
  
  
  def 		_A		(		_lowercase   )				->   int | float:
  
  
       """simple docstring"""
  
  
       if len(_lowercase   ) == 0:
            raise ValueError('find_max() arg is an empty sequence'   )
       __UpperCamelCase   = nums[0]
       for x in nums:
            if x > max_num:
                 __UpperCamelCase   = x
       return max_num
  
  
  if __name__ == "__main__":
      import doctest
  
      doctest.testmod(verbose=True)
  
  
  