code: import random
  
  class      UpperCAmelCase_						:
  
  						'''simple docstring'''
  
  
  
  
  						def __init__(					self   ,							_A   ,							_A   ,							_A = 0.0_1   ,							_A = 1_000   ,							_A = -1   ,							):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      sample
  													if len(self.sample	) == 0:
  																				raise ValueError('Sample data can not be empty'	)
  													__SCREAMING_SNAKE_CASE								=      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'	)
  													__SCREAMING_SNAKE_CASE								=      learning_rate
  													__SCREAMING_SNAKE_CASE								=      epoch_number
  													__SCREAMING_SNAKE_CASE								=      bias
  													__SCREAMING_SNAKE_CASE								=      len(_A	)
  													__SCREAMING_SNAKE_CASE								=      len(sample[0]	)  # number of columns in dataset
  													__SCREAMING_SNAKE_CASE								=      []
  
  
  
  
  
  
  						def  _A			(					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	)
  
  													__SCREAMING_SNAKE_CASE								=      0
  
  													while True:
  																				__SCREAMING_SNAKE_CASE								=      False
  																				for i in range(self.number_sample	):
  																											__SCREAMING_SNAKE_CASE								=      0
  																											for j in range(self.col_sample + 1	):
  																																		__SCREAMING_SNAKE_CASE								=      u + self.weight[j] * self.sample[i][j]
  																											__SCREAMING_SNAKE_CASE								=      self.sign(_A	)
  																											if y != self.target[i]:
  																																		for j in range(self.col_sample + 1	):
  																																									__SCREAMING_SNAKE_CASE								=      (
  																																									    self.weight[j]
  																																									    + self.learning_rate
  																																									    * (self.target[i] - y)
  																																									    * self.sample[i][j]
  																																									)
  																																		__SCREAMING_SNAKE_CASE								=      True
              # print('Epoch: \n',epoch_count)
  																				__SCREAMING_SNAKE_CASE								=      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   ,							_A	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													if len(self.sample	) == 0:
  																				raise ValueError('Sample data can not be empty'	)
  													sample.insert(0   ,							self.bias	)
  													__SCREAMING_SNAKE_CASE								=      0
  													for i in range(self.col_sample + 1	):
  																				__SCREAMING_SNAKE_CASE								=      u + self.weight[i] * sample[i]
  
  													__SCREAMING_SNAKE_CASE								=      self.sign(_A	)
  
  													if y == -1:
  																				print(('Sample: ', sample)	)
  																				print('classification: P1'	)
  													else:
  																				print(('Sample: ', sample)	)
  																				print('classification: P2'	)
  
  
  
  
  
  
  						def  _A			(					self   ,							_A	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													return 1 if u >= 0 else -1
  
  
  
  
  
  
  
  lowerCAmelCase__	:  Optional[int] 		=[
      [-0.6_508, 0.1_097, 4.0_009],
      [-1.4_492, 0.8_896, 4.4_005],
      [2.0_850, 0.6_876, 12.0710],
      [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, 11.2095],
      [2.0_149, 0.6_192, 10.9263],
      [0.2_012, 0.2_611, 5.4_631],
  ]
  
  lowerCAmelCase__	:  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()
  
  			lowerCAmelCase__	:  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:
  						lowerCAmelCase__	:  list 		=[]
  						for i in range(len(samples[0])):
  									lowerCAmelCase__	:  Dict 		=input('''value: ''').strip()
  									if user_input == "q":
  												break
  									lowerCAmelCase__	:  Union[str, Any] 		=float(user_input)
  									sample.insert(i, observation)
  						network.sort(sample)
  
  
  
  
  
  
  
style_context: 
  
  
  
  def 			__lowercase     (				a__     )       ->				list:
  							__SCREAMING_SNAKE_CASE								=      1.3
  							__SCREAMING_SNAKE_CASE								=      len(a__     )
  							__SCREAMING_SNAKE_CASE								=      False
  
  							while not completed:
  														# Update the gap value for a next comb
  														__SCREAMING_SNAKE_CASE								=      int(gap / shrink_factor     )
  														if gap <= 1:
  																					__SCREAMING_SNAKE_CASE								=      True
  
  														__SCREAMING_SNAKE_CASE								=      0
  														while index + gap < len(a__     ):
  																					if data[index] > data[index + gap]:
  																												# Swap values
  																												__SCREAMING_SNAKE_CASE							, __SCREAMING_SNAKE_CASE								=      data[index + gap], data[index]
  																												__SCREAMING_SNAKE_CASE								=      False
  																					index += 1
  
  							return data
  
  
  if __name__ == "__main__":
  			import doctest
  
  			doctest.testmod()
  
  			lowerCAmelCase__	:  int 		=input('''Enter numbers separated by a comma:\n''').strip()
  			lowerCAmelCase__	:  Dict 		=[int(item) for item in user_input.split(''',''')]
  			print(comb_sort(unsorted))
  
  
  
  
  
  
  