





'''simple docstring'''




from itertools import product

from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_							)   ->Dict:
	snake_case__	       =   k_size // 2
	snake_case__       ,  snake_case__	       =   mgrid[0 - center : k_size - center, 0 - center : k_size - center]
	snake_case__	       =   1 / (2 * pi * sigma) * exp(-(square(snake_case__							) + square(snake_case__							)) / (2 * square(snake_case__							))							)
	return g




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)   ->str:
	snake_case__       ,  snake_case__	       =   image.shape[0], image.shape[1]
	# dst image height and width
	snake_case__	       =   height - k_size + 1
	snake_case__	       =   width - k_size + 1

	# im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
	snake_case__	       =   zeros((dst_height * dst_width, k_size * k_size)							)
	snake_case__	       =   0
	for i, j in product(range(snake_case__							)					,				range(snake_case__							)							):
		snake_case__	       =   ravel(image[i : i + k_size, j : j + k_size]							)
		snake_case__	       =   window
		row += 1

	#  turn the kernel into shape(k*k, 1)
	snake_case__	       =   gen_gaussian_kernel(snake_case__					,				snake_case__							)
	snake_case__	       =   ravel(snake_case__							)

	# reshape and get the dst image
	snake_case__	       =   dot(snake_case__					,				snake_case__							).reshape(snake_case__					,				snake_case__							).astype(snake_case__							)

	return dst


if __name__ == "__main__":
			# read original image
			a__					:			List[str]          =     imread(r'''../image_data/lena.jpg''')
			# turn image in gray scale value
			a__					:			Optional[Any]          =     cvtColor(img, COLOR_BGR2GRAY)

			# get values with two different mask size
			a__					:			Optional[int]          =     gaussian_filter(gray, 3, sigma=1)
			a__					:			Tuple          =     gaussian_filter(gray, 5, sigma=0.8)

			# show result images
			imshow('''gaussian filter with 3x3 mask''', gaussianaxa)
			imshow('''gaussian filter with 5x5 mask''', gaussianaxa)
			waitKey()








from collections import Counter

import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split

_lowerCAmelCase	:   int    =   datasets.load_iris()

_lowerCAmelCase	:   Tuple    =   np.array(data["data"])
_lowerCAmelCase	:   Any    =   np.array(data["target"])
_lowerCAmelCase	:   Union[str, Any]    =   data["target_names"]

_lowerCAmelCase,		_lowerCAmelCase,		_lowerCAmelCase,		_lowerCAmelCase	:   Optional[Any]    =   train_test_split(X, y)







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__					)	->     Tuple:



				"""simple docstring"""





				return np.linalg.norm(np.array(snake_case__					) - np.array(snake_case__					)					)







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__    ,			snake_case__    ,			snake_case__    ,			snake_case__=5					)	->     Dict:



				"""simple docstring"""





				lowerCAmelCase__		  =  zip(snake_case__    ,			snake_case__					)
				# List of distances of all points from the point to be classified
				lowerCAmelCase__		  =  []
				for data_point in data:
								lowerCAmelCase__		  =  euclidean_distance(data_point[0]    ,			snake_case__					)
								distances.append((distance, data_point[1])					)
				# Choosing 'k' points with the least distances.
				lowerCAmelCase__		  =  [i[1] for i in sorted(snake_case__					)[:k]]
				# Most commonly occurring class among them
				# is the class into which the point is classified
				lowerCAmelCase__		  =  Counter(snake_case__					).most_common(1					)[0][0]
				return classes[result]


if __name__ == "__main__":
	print(classifier(X_train, y_train, classes, [4.4, 3.1, 1.3, 1.4]))
