code: 
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  
  
  import random
  
  
  
  
  
  class 					_UpperCAmelCase							:
  		def __init__(						self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE = 0.01,__SCREAMING_SNAKE_CASE = 10_00,__SCREAMING_SNAKE_CASE = -1,):
  
  
  
  					'''simple docstring'''
  
  					__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 						lowerCamelCase__    (						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    )
  
  					__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 						lowerCamelCase__    (						self,__SCREAMING_SNAKE_CASE    ):
  
  
  
  					'''simple docstring'''
  
  					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 						lowerCamelCase__    (						self,__SCREAMING_SNAKE_CASE    ):
  
  
  
  					'''simple docstring'''
  
  					return 1 if u >= 0 else -1
  
  
  
  
  
  _a      :				Tuple											=					[
      [-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      :				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()
  
  	_a      :				str											=					Perceptron(
  	    sample=samples, target=target, learning_rate=0.01, epoch_number=1_0_0_0, 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      :				List[Any]											=					input("""value: """).strip()
  			if user_input == "q":
  				break
  			_a      :				List[str]											=					float(user_input)
  			sample.insert(i, observation)
  		network.sort(sample)
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  
  
  from cva import destroyAllWindows, imread, imshow, waitKey
  
  
  
  
  
  def      _lowerCAmelCase		(		lowercase      ,			lowercase			)      ->      int:
  			__lowerCAmelCase		, __lowerCAmelCase      		=       img.shape[0], img.shape[1]
  
  			def to_grayscale(lowercase      ,			lowercase      ,			lowercase			):
  						return 0.21_26 * red + 0.5_87 * green + 0.1_14 * blue
  
  			def normalize(lowercase			):
  						return min(lowercase      ,			255			)
  
  			for i in range(lowercase			):
  						for j in range(lowercase			):
  									__lowerCAmelCase      		=       int(to_grayscale(*img[i][j]			)			)
  									__lowerCAmelCase      		=       [
  									    normalize(lowercase			),
  									    normalize(greyscale + factor			),
  									    normalize(greyscale + 2 * factor			),
  									]
  
  			return img
  
  
  if __name__ == "__main__":
  	# read original image
  	_a      :				Any											=					{
  	    percentage: imread("""image_data/lena.jpg""", 1) for percentage in (1_0, 2_0, 3_0, 4_0)
  	}
  
  	for percentage, img in images.items():
  		make_sepia(img, percentage)
  
  	for percentage, img in images.items():
  		imshow(f'Original image with sepia (factor: {percentage})', img)
  
  	waitKey(0)
  	destroyAllWindows()
  
  