


import numpy as np






def 	a__ (							A__,			A__,			A__							):
			SCREAMING_SNAKE_CASE_		:    str 		=				n_draws * class_counts / class_counts.sum()
			# floored means we don't overshoot n_samples, but probably undershoot
			SCREAMING_SNAKE_CASE_		:    Any 		=				np.floor(A__							)
			# we add samples according to how much "left over" probability
			# they had, until we arrive at n_samples
			SCREAMING_SNAKE_CASE_		:    List[Any] 		=				int(n_draws - floored.sum()							)
			if need_to_add > 0:
						SCREAMING_SNAKE_CASE_		:    Any 		=				continuous - floored
						SCREAMING_SNAKE_CASE_		:    Dict 		=				np.sort(np.unique(A__							)							)[::-1]
						# add according to remainder, but break ties
						# randomly to avoid biases
						for value in values:
									((SCREAMING_SNAKE_CASE_)		,			)		:    Optional[int] 		=				np.where(remainder == value							)
									# if we need_to_add less than what's in inds
									# we draw randomly from them.
									# if we need to add more, we add them all and
									# go to the next value
									SCREAMING_SNAKE_CASE_		:    int 		=				min(len(A__							),			A__							)
									SCREAMING_SNAKE_CASE_		:    Dict 		=				rng.choice(A__,			size=A__,			replace=A__							)
									floored[inds] += 1
									need_to_add -= add_now
									if need_to_add == 0:
												break
			return floored.astype(np.intaa							)






def 	a__ (							A__,			A__,			A__,			A__,			A__=1_0							):
			SCREAMING_SNAKE_CASE_		,			SCREAMING_SNAKE_CASE_		:    List[Any] 		=				np.unique(A__,			return_inverse=A__							)
			SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				classes.shape[0]
			SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				np.bincount(A__							)
			if np.min(A__							) < 2:
						raise ValueError('Minimum class count error'							)
			if n_train < n_classes:
						raise ValueError(
						    'The train_size = %d should be greater or ' 'equal to the number of classes = %d' % (n_train, n_classes)							)
			if n_test < n_classes:
						raise ValueError(
						    'The test_size = %d should be greater or ' 'equal to the number of classes = %d' % (n_test, n_classes)							)
			SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				np.split(np.argsort(A__,			kind='mergesort'							),			np.cumsum(A__							)[:-1]							)
			for _ in range(A__							):
						SCREAMING_SNAKE_CASE_		:    Dict 		=				approximate_mode(A__,			A__,			A__							)
						SCREAMING_SNAKE_CASE_		:    List[str] 		=				class_counts - n_i
						SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				approximate_mode(A__,			A__,			A__							)

						SCREAMING_SNAKE_CASE_		:    int 		=				[]
						SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				[]

						for i in range(A__							):
									SCREAMING_SNAKE_CASE_		:    List[Any] 		=				rng.permutation(class_counts[i]							)
									SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				class_indices[i].take(A__,			mode='clip'							)
									train.extend(perm_indices_class_i[: n_i[i]]							)
									test.extend(perm_indices_class_i[n_i[i] : n_i[i] + t_i[i]]							)
						SCREAMING_SNAKE_CASE_		:    Tuple 		=				rng.permutation(A__							)
						SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				rng.permutation(A__							)

						yield train, test









import cva
import numpy as np






def 	a__ (							A__,			A__,			A__,			A__							):

			try:
						return int(image[x_coordinate][y_coordinate] >= center							)
			except (IndexError, TypeError):
						return 0






def 	a__ (							A__,			A__,			A__							):
			SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				image[x_coordinate][y_coordinate]
			SCREAMING_SNAKE_CASE_		:    str 		=				[1, 2, 4, 8, 1_6, 3_2, 6_4, 1_2_8]

			# skip get_neighbors_pixel if center is null
			if center is None:
						return 0

			# Starting from the top right, assigning value to pixels clockwise
			SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				[
			    get_neighbors_pixel(A__,			x_coordinate - 1,			y_coordinate + 1,			A__							),
			    get_neighbors_pixel(A__,			A__,			y_coordinate + 1,			A__							),
			    get_neighbors_pixel(A__,			x_coordinate - 1,			A__,			A__							),
			    get_neighbors_pixel(A__,			x_coordinate + 1,			y_coordinate + 1,			A__							),
			    get_neighbors_pixel(A__,			x_coordinate + 1,			A__,			A__							),
			    get_neighbors_pixel(A__,			x_coordinate + 1,			y_coordinate - 1,			A__							),
			    get_neighbors_pixel(A__,			A__,			y_coordinate - 1,			A__							),
			    get_neighbors_pixel(A__,			x_coordinate - 1,			y_coordinate - 1,			A__							),
			]

			# Converting the binary value to decimal.
			return sum(
			    binary_value * power for binary_value, power in zip(A__,			A__							)							)


if __name__ == "__main__":
				# Reading the image and converting it to grayscale.
				lowerCAmelCase__ :			List[Any]         =cva.imread(
				    'digital_image_processing/image_data/lena.jpg', cva.IMREAD_GRAYSCALE
				)

				# Create a numpy array as the same height and width of read image
				lowerCAmelCase__ :			List[Any]         =np.zeros((image.shape[0], image.shape[1]))

				# Iterating through the image and calculating the
				# local binary pattern value for each pixel.
				for i in range(0, image.shape[0]):
								for j in range(0, image.shape[1]):
												lowerCAmelCase__ :			Optional[Any]         =local_binary_value(image, i, j)

				cva.imshow('local binary pattern', lbp_image)
				cva.waitKey(0)
				cva.destroyAllWindows()





