


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)






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)


