"""simple docstring"""







import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey


class 			_lowercase		:

				def __init__(     self       :    Any       ,    _lowercase       :    Union[str, Any]       ,    _lowercase       :    int     ):


										'''simple docstring'''
										UpperCAmelCase__						=		0
										# max greyscale value for #FFFFFF
										UpperCAmelCase__						=		int(self.get_greyscale(2_5_5       ,    2_5_5       ,    2_5_5     )     )

										if not self.min_threshold < threshold < self.max_threshold:
																UpperCAmelCase__						=		f'Factor value should be from 0 to {self.max_threshold}'
																raise ValueError(_lowercase     )

										UpperCAmelCase__						=		input_img
										UpperCAmelCase__						=		threshold
										UpperCAmelCase__	,      UpperCAmelCase__						=		self.input_img.shape[1], self.input_img.shape[0]

										# error table size (+4 columns and +1 row) greater than input image because of
										# lack of if statements
										UpperCAmelCase__						=		[
										    [0 for _ in range(self.height + 4     )] for __ in range(self.width + 1     )
										]
										UpperCAmelCase__						=		np.ones((self.width, self.height, 3)       ,    np.uinta     ) * 2_5_5

				@classmethod
				def        A (     cls       :    Union[str, Any]       ,    _lowercase       :    int       ,    _lowercase       :    int       ,    _lowercase       :    int     ):


										'''simple docstring'''
										return 0.114 * blue + 0.587 * green + 0.2126 * red

				def        A (     self       :    Tuple     ):


										'''simple docstring'''
										for y in range(self.height     ):
																for x in range(self.width     ):
																						UpperCAmelCase__						=		int(self.get_greyscale(*self.input_img[y][x]     )     )
																						if self.threshold > greyscale + self.error_table[y][x]:
																												UpperCAmelCase__						=		(0, 0, 0)
																												UpperCAmelCase__						=		greyscale + self.error_table[x][y]
																						else:
																												UpperCAmelCase__						=		(2_5_5, 2_5_5, 2_5_5)
																												UpperCAmelCase__						=		greyscale + self.error_table[x][y] - 2_5_5
																						self.error_table[y][x + 1] += int(8 / 3_2 * current_error     )
																						self.error_table[y][x + 2] += int(4 / 3_2 * current_error     )
																						self.error_table[y + 1][x] += int(8 / 3_2 * current_error     )
																						self.error_table[y + 1][x + 1] += int(4 / 3_2 * current_error     )
																						self.error_table[y + 1][x + 2] += int(2 / 3_2 * current_error     )
																						self.error_table[y + 1][x - 1] += int(4 / 3_2 * current_error     )
																						self.error_table[y + 1][x - 2] += int(2 / 3_2 * current_error     )


if __name__ == "__main__":
						# create Burke's instances with original images in greyscale
						UpperCamelCase_       :       List[Any]							   =		[
						    Burkes(imread("""image_data/lena.jpg""", 1), threshold)
						    for threshold in (1, 126, 130, 140)
						]

						for burkes in burkes_instances:
												burkes.process()

						for burkes in burkes_instances:
												imshow(
												    F"""Original image with dithering threshold: {burkes.threshold}""",
												    burkes.output_img,
												)

						waitKey(0)
						destroyAllWindows()


"""simple docstring"""







from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

# Load iris file
UpperCamelCase_       :       Optional[int]							   =		load_iris()
iris.keys()


print(F"""Target names: \n {iris.target_names} """)
print(F"""\n Features: \n {iris.feature_names}""")

# Train set e Test set
UpperCamelCase_   ,						UpperCamelCase_   ,						UpperCamelCase_   ,						UpperCamelCase_       :       int							   =		train_test_split(
    iris["""data"""], iris["""target"""], random_state=4
)

# KNN

UpperCamelCase_       :       str							   =		KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)

# new array to test
UpperCamelCase_       :       Union[str, Any]							   =		[[1, 2, 1, 4], [2, 3, 4, 5]]

UpperCamelCase_       :       Union[str, Any]							   =		knn.predict(X_new)

print(
    F"""\nNew array: \n {X_new}\n\nTarget Names Prediction: \n"""
    F""" {iris["target_names"][prediction]}"""
)

