





'''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)






'''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()

