




import random

class   UpperCAmelCase    :






   def __init__(						self :							str    ,       UpperCamelCase_ :							list[list[float]]    ,       UpperCamelCase_ :							list[int]    ,       UpperCamelCase_ :							float = 0.01    ,       UpperCamelCase_ :							int = 10_00    ,       UpperCamelCase_ :							float = -1    ,       )->      None:
     _lowercase:			str      =     sample
     if len(self.sample  ) == 0:
       raise ValueError('Sample data can not be empty'  )
     _lowercase:			Any      =     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'  )
     _lowercase:			str      =     learning_rate
     _lowercase:			List[str]      =     epoch_number
     _lowercase:			Optional[Any]      =     bias
     _lowercase:			int      =     len(UpperCamelCase_  )
     _lowercase:			Dict      =     len(sample[0]  )  # number of columns in dataset
     _lowercase:			list      =     []






   def 			_a    (						self :							Union[str, Any]  )->      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  )

     _lowercase:			Optional[Any]      =     0

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

     _lowercase:			Optional[Any]      =     self.sign(UpperCamelCase_  )

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






   def 			_a    (						self :							List[Any]    ,       UpperCamelCase_ :							float  )->      int:
     return 1 if u >= 0 else -1
UpperCAmelCase__    :							Optional[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],
]

UpperCAmelCase__    :							int       		=     [
    -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()

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






from math import sqrt


def 		_lowerCamelCase					(       UpperCamelCase				)      ->		list[int]:





  """simple docstring"""





  _lowercase:			Dict      =     [1]
  for i in range(2       ,  int(sqrt(UpperCamelCase				)				) + 1       ,  1				):
    if number % i == 0:
      values.append(UpperCamelCase				)
      if int(number // i				) != i:
        values.append(int(number // i				)				)
  return sorted(UpperCamelCase				)


def 		_lowerCamelCase					(       UpperCamelCase				)      ->		bool:





  """simple docstring"""





  return sum(factors(UpperCamelCase				)				) > n


def 		_lowerCamelCase					(       UpperCamelCase				)      ->		bool:





  """simple docstring"""





  _lowercase:			Dict      =     factors(UpperCamelCase				)
  _lowercase:			int      =     len(UpperCamelCase				)
  _lowercase:			Optional[int]      =     [[0 for i in range(number + 1				)] for j in range(r + 1				)]
  for i in range(r + 1				):
    _lowercase:			List[Any]      =     True

  for i in range(1       ,  number + 1				):
    _lowercase:			Optional[int]      =     False

  for i in range(1       ,  r + 1				):
    for j in range(1       ,  number + 1				):
      if j < values[i - 1]:
        _lowercase:			Optional[int]      =     subset[i - 1][j]
      else:
        _lowercase:			Optional[Any]      =     subset[i - 1][j] or subset[i - 1][j - values[i - 1]]

  return subset[r][number] != 0


def 		_lowerCamelCase					(       UpperCamelCase				)      ->		bool:





  """simple docstring"""





  return abundant(UpperCamelCase				) and not semi_perfect(UpperCamelCase				)


if __name__ == "__main__":
   import doctest

   doctest.testmod(verbose=True)
   for number in (69, 70, 71):
      print(f'''{number} is {'' if weird(number) else 'not '}weird.''')
