

import random

class      _a				:




      """simple docstring"""

      def __init__(      self       :							List[Any]	, UpperCAmelCase       :							list[list[float]]	, UpperCAmelCase       :							list[int]	, UpperCAmelCase       :							float = 0.01	, UpperCAmelCase       :							int = 1000	, UpperCAmelCase       :							float = -1	, ):
            A_	     =							sample
            if len(self.sample  ) == 0:
                  raise ValueError("Sample data can not be empty"  )
            A_	     =							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"  )
            A_	     =							learning_rate
            A_	     =							epoch_number
            A_	     =							bias
            A_	     =							len(UpperCAmelCase  )
            A_	     =							len(sample[0]  )  # number of columns in dataset
            A_	     =							[]





      def 		__A  (      self       :							str  ):
            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  )

            A_	     =							0

            while True:
                  A_	     =							False
                  for i in range(self.number_sample  ):
                        A_	     =							0
                        for j in range(self.col_sample + 1  ):
                              A_	     =							u + self.weight[j] * self.sample[i][j]
                        A_	     =							self.sign(UpperCAmelCase  )
                        if y != self.target[i]:
                              for j in range(self.col_sample + 1  ):
                                    A_	     =							(
                                        self.weight[j]
                                        + self.learning_rate
                                        * (self.target[i] - y)
                                        * self.sample[i][j]
                                    )
                              A_	     =							True
            # print('Epoch: \n',epoch_count)
                  A_	     =							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       :							int	, UpperCAmelCase       :							list[float]  ):
            if len(self.sample  ) == 0:
                  raise ValueError("Sample data can not be empty"  )
            sample.insert(0	, self.bias  )
            A_	     =							0
            for i in range(self.col_sample + 1  ):
                  A_	     =							u + self.weight[i] * sample[i]

            A_	     =							self.sign(UpperCAmelCase  )

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







      def 		__A  (      self       :							int	, UpperCAmelCase       :							float  ):
            return 1 if u >= 0 else -1




__a						:str            = [
    [-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						:Tuple            = [
    -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						:Optional[Any]            = input('value: ').strip()
                  if user_input == "q":
                        break
                  __a						:str            = float(user_input)
                  sample.insert(i, observation)
            network.sort(sample)


__a						:Dict            = {hex(i)[2:]: i for i in range(16)}  # Use [:2] to strip off the leading '0x'

def 			__snake_case	(   __UpperCamelCase  :   str						):



      """simple docstring"""

      A_	     =							hex_string.strip().lower()
      if not hex_string:
            raise ValueError("Empty string was passed to the function"						)
      A_	     =							hex_string[0] == "-"
      if is_negative:
            A_	     =							hex_string[1:]
      if not all(char in hex_table for char in hex_string						):
            raise ValueError("Non-hexadecimal value was passed to the function"						)
      A_	     =							0
      for char in hex_string:
            A_	     =							16 * decimal_number + hex_table[char]
      return -decimal_number if is_negative else decimal_number


if __name__ == "__main__":
      from doctest import testmod

      testmod()