code: 
  
  
  
  """simple docstring"""
  def 				lowerCamelCase_		(UpperCamelCase__	:	Any  , UpperCamelCase__	:	Optional[int]							):
      return (input_a, input_a).count(0							) % 2
  
  
  
  
  
  def 				lowerCamelCase_		():
      assert xor_gate(0  , 0							) == 0
      assert xor_gate(0  , 1							) == 1
      assert xor_gate(1  , 0							) == 1
      assert xor_gate(1  , 1							) == 0
  
  
  if __name__ == "__main__":
         print(xor_gate(0, 0))
         print(xor_gate(0, 1))
  
  
  
style_context: 
  
  
  
  """simple docstring"""
  import random
  class 							_UpperCAmelCase							:
  
  
  
  
  
       '''simple docstring'''
  
  
  
       def __init__(      self ,			A ,			A ,			A = 0.01 ,			A = 1_0_0_0 ,			A = -1 ,			)							->      None:
           _UpperCAmelCase							:						Dict       							=					sample
           if len(self.sample	) == 0:
               raise ValueError('''Sample data can not be empty'''	)
           _UpperCAmelCase							:						Tuple       							=					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[Any]       							=					learning_rate
           _UpperCAmelCase							:						Union[str, Any]       							=					epoch_number
           _UpperCAmelCase							:						Any       							=					bias
           _UpperCAmelCase							:						str       							=					len(A	)
           _UpperCAmelCase							:						Tuple       							=					len(sample[0]	)  # number of columns in dataset
           _UpperCAmelCase							:						list       							=					[]
  
  
  
  
  
       def 				__lowerCAmelCase			(      self	)							->      None:
           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							:						Tuple       							=					0
  
           while True:
               _UpperCAmelCase							:						Tuple       							=					False
               for i in range(self.number_sample	):
                   _UpperCAmelCase							:						Optional[int]       							=					0
                   for j in range(self.col_sample + 1	):
                       _UpperCAmelCase							:						int       							=					u + self.weight[j] * self.sample[i][j]
                   _UpperCAmelCase							:						Tuple       							=					self.sign(A	)
                   if y != self.target[i]:
                       for j in range(self.col_sample + 1	):
                           _UpperCAmelCase							:						int       							=					(
                               self.weight[j]
                               + self.learning_rate
                               * (self.target[i] - y)
                               * self.sample[i][j]
                           )
                       _UpperCAmelCase							:						Union[str, Any]       							=					True
              # print('Epoch: \n',epoch_count)
               _UpperCAmelCase							:						int       							=					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 				__lowerCAmelCase			(      self ,			A	)							->      None:
           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							:						Optional[Any]       							=					u + self.weight[i] * sample[i]
  
           _UpperCAmelCase							:						Optional[int]       							=					self.sign(A	)
  
           if y == -1:
               print(('''Sample: ''', sample)	)
               print('''classification: P1'''	)
           else:
               print(('''Sample: ''', sample)	)
               print('''classification: P2'''	)
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A	)							->      int:
           return 1 if u >= 0 else -1
  
  
  
  _lowerCAmelCase					: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],
  ]
  
  _lowerCAmelCase					:Dict  =      [
      -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()
  
    _lowerCAmelCase					:int  =      Perceptron(
        sample=samples, target=target, learning_rate=0.01, epoch_number=1_000, bias=-1
    )
    network.training()
    print('Finished training perceptron')
    print('Enter values to predict or q to exit')
    while True:
      _lowerCAmelCase					:list  =      []
      for i in range(len(samples[0])):
        _lowerCAmelCase					:List[Any]  =      input('value: ').strip()
        if user_input == "q":
          break
        _lowerCAmelCase					:Any  =      float(user_input)
        sample.insert(i, observation)
      network.sort(sample)
  
  
  