

"""simple docstring"""





import matplotlib.pyplot as plt
import numpy as np

def 						__SCREAMING_SNAKE_CASE						(   A_		,  A_		,  A_				):
	lowerCAmelCase__			:		Any						       =			len(A_				)  # Number of training samples
	lowerCAmelCase__			:		List[Any]						       =			np.eye(A_				)  # Initialize weights as identity matrix
	for j in range(A_				):
		lowerCAmelCase__			:		Optional[int]						       =			point - x_train[j]
		lowerCAmelCase__			:		int						       =			np.exp(diff @ diff.T / (-2.0 * tau**2)				)

	return weights

def 						__SCREAMING_SNAKE_CASE						(   A_		,  A_		,  A_		,  A_				):
	lowerCAmelCase__			:		Dict						       =			weight_matrix(A_		,  A_		,  A_				)
	lowerCAmelCase__			:		List[str]						       =			np.linalg.inv(x_train.T @ weight_mat @ x_train				) @ (
	    x_train.T @ weight_mat @ y_train.T
	)

	return weight

def 						__SCREAMING_SNAKE_CASE						(   A_		,  A_		,  A_				):
	lowerCAmelCase__			:		Any						       =			np.zeros(len(A_				)				)  # Initialize array of predictions
	for i, item in enumerate(A_				):
		lowerCAmelCase__			:		str						       =			item @ local_weight(A_		,  A_		,  A_		,  A_				)

	return y_pred

def 						__SCREAMING_SNAKE_CASE						(   A_		,  A_		,  A_				):
	import seaborn as sns

	lowerCAmelCase__			:		Optional[Any]						       =			sns.load_dataset(A_				)
	lowerCAmelCase__			:		Optional[int]						       =			np.array(data[x_name]				)
	lowerCAmelCase__			:		Dict						       =			np.array(data[y_name]				)

	lowerCAmelCase__			:		Tuple						       =			np.ones(len(A_				)				)

	# pairing elements of one and x_data
	lowerCAmelCase__			:		Optional[int]						       =			np.column_stack((one, x_data)				)

	return x_train, x_data, y_data

def 						__SCREAMING_SNAKE_CASE						(   A_		,  A_		,  A_		,  A_		,  A_		,  A_		,  ):
	lowerCAmelCase__			:		Optional[int]						       =			np.sort(A_		,  axis=0				)
	plt.scatter(A_		,  A_		,  color='''blue'''				)
	plt.plot(
	    x_train_sorted[:, 1]		,  preds[x_train[:, 1].argsort(0				)]		,  color='''yellow'''		,  linewidth=5		,  )
	plt.title('''Local Weighted Regression'''				)
	plt.xlabel(A_				)
	plt.ylabel(A_				)
	plt.show()


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							# Demo with a dataset from the seaborn module
							__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase						:   Any							   =	load_data('''tips''', '''total_bill''', '''tip''')
							__UpperCamelCase						:   Optional[Any]							   =	local_weight_regression(training_data_x, tip, 5)
							plot_preds(training_data_x, predictions, total_bill, tip, '''total_bill''', '''tip''')







"""simple docstring"""





from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import divide, inta, multiply, ravel, sort, zeros_like

def 						__SCREAMING_SNAKE_CASE						(   A_		,  A_=3				):
	lowerCAmelCase__			:		Union[str, Any]						       =			int(mask / 2				)
	# copy image size
	lowerCAmelCase__			:		Dict						       =			zeros_like(A_				)
	for i in range(A_		,  gray_img.shape[0] - bd				):
		for j in range(A_		,  gray_img.shape[1] - bd				):
			# get mask according with mask
			lowerCAmelCase__			:		str						       =			ravel(gray_img[i - bd : i + bd + 1, j - bd : j + bd + 1]				)
			# calculate mask median
			lowerCAmelCase__			:		str						       =			sort(A_				)[inta(divide((multiply(A_		,  A_				))		,  2				) + 1				)]
			lowerCAmelCase__			:		List[str]						       =			median
	return median_img


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

							# get values with two different mask size
							__UpperCamelCase						:   int							   =	median_filter(gray, 3)
							__UpperCamelCase						:   Tuple							   =	median_filter(gray, 5)

							# show result images
							imshow('''median filter with 3x3 mask''', medianaxa)
							imshow('''median filter with 5x5 mask''', medianaxa)
							waitKey(0)




