code: import math
  
  
  
  
  
  
  class _lowercase					:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  							def __init__(    self     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														_lowercase				=	x
  														_lowercase				=	y
  														_lowercase				=	z
  
  
  
  							def __repr__(    self  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														return F'''Point({self.x}, {self.y}, {self.z})'''
  
  
  
  
  
  
  def 					__lowerCAmelCase			(  _A ,_A					):
  
  
  
  							"""simple docstring"""
  
  
  							return math.sqrt(abs((b.x - a.x) ** 2 + (b.y - a.y) ** 2 + (b.z - a.z) ** 2					)					)
  
  
  if __name__ == "__main__":
  				import doctest
  
  				doctest.testmod()
  
  
  
style_context: import random
  
  
  class _lowercase					:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  							def __init__(    self     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase = 0.01     ,   UpperCAmelCase = 1000     ,   UpperCAmelCase = -1     ,   ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														_lowercase				=	sample
  														if len(self.sample  ) == 0:
  																					raise ValueError("""Sample data can not be empty"""  )
  														_lowercase				=	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				=	learning_rate
  														_lowercase				=	epoch_number
  														_lowercase				=	bias
  														_lowercase				=	len(UpperCAmelCase  )
  														_lowercase				=	len(sample[0]  )  # number of columns in dataset
  														_lowercase				=	[]
  
  
  
  							def     _UpperCAmelCase       (    self  ):
  
  
  
  
  
  
  
  														'''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  )
  
  														_lowercase				=	0
  
  														while True:
  																					_lowercase				=	False
  																					for i in range(self.number_sample  ):
  																												_lowercase				=	0
  																												for j in range(self.col_sample + 1  ):
  																																			_lowercase				=	u + self.weight[j] * self.sample[i][j]
  																												_lowercase				=	self.sign(UpperCAmelCase  )
  																												if y != self.target[i]:
  																																			for j in range(self.col_sample + 1  ):
  																																										_lowercase				=	(
  																																										    self.weight[j]
  																																										    + self.learning_rate
  																																										    * (self.target[i] - y)
  																																										    * self.sample[i][j]
  																																										)
  																																			_lowercase				=	True
              # print('Epoch: \n',epoch_count)
  																					_lowercase				=	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     ,   UpperCAmelCase  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														if len(self.sample  ) == 0:
  																					raise ValueError("""Sample data can not be empty"""  )
  														sample.insert(0     ,   self.bias  )
  														_lowercase				=	0
  														for i in range(self.col_sample + 1  ):
  																					_lowercase				=	u + self.weight[i] * sample[i]
  
  														_lowercase				=	self.sign(UpperCAmelCase  )
  
  														if y == -1:
  																					print(("""Sample: """, sample)  )
  																					print("""classification: P1"""  )
  														else:
  																					print(("""Sample: """, sample)  )
  																					print("""classification: P2"""  )
  
  
  
  							def     _UpperCAmelCase       (    self     ,   UpperCAmelCase  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														return 1 if u >= 0 else -1
  
  
  
  
  
  
  A_:      Optional[int]        =			[
      [-0.6_5_0_8, 0.1_0_9_7, 4.0_0_0_9],
      [-1.4_4_9_2, 0.8_8_9_6, 4.4_0_0_5],
      [2.0_8_5_0, 0.6_8_7_6, 1_2.0_7_1_0],
      [0.2_6_2_6, 1.1_4_7_6, 7.7_9_8_5],
      [0.6_4_1_8, 1.0_2_3_4, 7.0_4_2_7],
      [0.2_5_6_9, 0.6_7_3_0, 8.3_2_6_5],
      [1.1_1_5_5, 0.6_0_4_3, 7.4_4_4_6],
      [0.0_9_1_4, 0.3_3_9_9, 7.0_6_7_7],
      [0.0_1_2_1, 0.5_2_5_6, 4.6_3_1_6],
      [-0.0_4_2_9, 0.4_6_6_0, 5.4_3_2_3],
      [0.4_3_4_0, 0.6_8_7_0, 8.2_2_8_7],
      [0.2_7_3_5, 1.0_2_8_7, 7.1_9_3_4],
      [0.4_8_3_9, 0.4_8_5_1, 7.4_8_5_0],
      [0.4_0_8_9, -0.1_2_6_7, 5.5_0_1_9],
      [1.4_3_9_1, 0.1_6_1_4, 8.5_8_4_3],
      [-0.9_1_1_5, -0.1_9_7_3, 2.1_9_6_2],
      [0.3_6_5_4, 1.0_4_7_5, 7.4_8_5_8],
      [0.2_1_4_4, 0.7_5_1_5, 7.1_6_9_9],
      [0.2_0_1_3, 1.0_0_1_4, 6.5_4_8_9],
      [0.6_4_8_3, 0.2_1_8_3, 5.8_9_9_1],
      [-0.1_1_4_7, 0.2_2_4_2, 7.2_4_3_5],
      [-0.7_9_7_0, 0.8_7_9_5, 3.8_7_6_2],
      [-1.0_6_2_5, 0.6_3_6_6, 2.4_7_0_7],
      [0.5_3_0_7, 0.1_2_8_5, 5.6_8_8_3],
      [-1.2_2_0_0, 0.7_7_7_7, 1.7_2_5_2],
      [0.3_9_5_7, 0.1_0_7_6, 5.6_6_2_3],
      [-0.1_0_1_3, 0.5_9_8_9, 7.1_8_1_2],
      [2.4_4_8_2, 0.9_4_5_5, 1_1.2_0_9_5],
      [2.0_1_4_9, 0.6_1_9_2, 1_0.9_2_6_3],
      [0.2_0_1_2, 0.2_6_1_1, 5.4_6_3_1],
  ]
  
  A_:      Optional[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()
  
  				A_:      str        =			Perceptron(
  				    sample=samples, target=target, learning_rate=0.0_1, 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_:      Dict        =			input('value: ').strip()
  												if user_input == "q":
  																break
  												A_:      Union[str, Any]        =			float(user_input)
  												sample.insert(i, observation)
  								network.sort(sample)
  
  
  