






import random






class 					a						:






    def __init__(	self					:							str	,						__SCREAMING_SNAKE_CASE					:							list[list[float]]	,						__SCREAMING_SNAKE_CASE					:							list[int]	,						__SCREAMING_SNAKE_CASE					:							float = 0.01	,						__SCREAMING_SNAKE_CASE					:							int = 1000	,						__SCREAMING_SNAKE_CASE					:							float = -1	,						)					->		None:
      lowerCamelCase_   					=						sample
      if len(self.sample ) == 0:
        raise ValueError('Sample data can not be empty' )
      lowerCamelCase_   					=						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' )
      lowerCamelCase_   					=						learning_rate
      lowerCamelCase_   					=						epoch_number
      lowerCamelCase_   					=						bias
      lowerCamelCase_   					=						len(__SCREAMING_SNAKE_CASE )
      lowerCamelCase_   					=						len(sample[0] )  # number of columns in dataset
      lowerCamelCase_   					=						[]






    def        UpperCamelCase	(	self					:							str )					->		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 )

      lowerCamelCase_   					=						0

      while True:
        lowerCamelCase_   					=						False
        for i in range(self.number_sample ):
          lowerCamelCase_   					=						0
          for j in range(self.col_sample + 1 ):
            lowerCamelCase_   					=						u + self.weight[j] * self.sample[i][j]
          lowerCamelCase_   					=						self.sign(__SCREAMING_SNAKE_CASE )
          if y != self.target[i]:
            for j in range(self.col_sample + 1 ):
              lowerCamelCase_   					=						(
                  self.weight[j]
                  + self.learning_rate
                  * (self.target[i] - y)
                  * self.sample[i][j]
              )
            lowerCamelCase_   					=						True
            # print('Epoch: \n',epoch_count)
        lowerCamelCase_   					=						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					:							Tuple	,						__SCREAMING_SNAKE_CASE					:							list[float] )					->		None:
      if len(self.sample ) == 0:
        raise ValueError('Sample data can not be empty' )
      sample.insert(0	,						self.bias )
      lowerCamelCase_   					=						0
      for i in range(self.col_sample + 1 ):
        lowerCamelCase_   					=						u + self.weight[i] * sample[i]

      lowerCamelCase_   					=						self.sign(__SCREAMING_SNAKE_CASE )

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






    def        UpperCamelCase	(	self					:							Optional[Any]	,						__SCREAMING_SNAKE_CASE					:							float )					->		int:
      return 1 if u >= 0 else -1
_SCREAMING_SNAKE_CASE :    List[Any]    =						[
    [-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],
]

_SCREAMING_SNAKE_CASE :    Union[str, Any]    =						[
    -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()

  _SCREAMING_SNAKE_CASE :    int    =						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:
    _SCREAMING_SNAKE_CASE :    list    =						[]
    for i in range(len(samples[0])):
      _SCREAMING_SNAKE_CASE :    List[str]    =						input('''value: ''').strip()
      if user_input == "q":
        break
      _SCREAMING_SNAKE_CASE :    Optional[int]    =						float(user_input)
      sample.insert(i, observation)
    network.sort(sample)












"""simple docstring"""





def 					lowerCamelCase__	(							_lowerCamelCase	:		int = 1000			)				->    int:
  lowerCamelCase_   					=						0
  lowerCamelCase_   					=						(n - 1) // 3
  total += ((terms) * (6 + (terms - 1) * 3)) // 2  # total of an A.P.
  lowerCamelCase_   					=						(n - 1) // 5
  total += ((terms) * (10 + (terms - 1) * 5)) // 2
  lowerCamelCase_   					=						(n - 1) // 15
  total -= ((terms) * (30 + (terms - 1) * 15)) // 2
  return total


if __name__ == "__main__":
  print(F'''{solution() = }''')




