


'''simple docstring'''

import numpy as np
import requests

def _SCREAMING_SNAKE_CASE				():






       """simple docstring"""







       lowercase__				      =			requests.get(
           '''https://raw.githubusercontent.com/yashLadha/The_Math_of_Intelligence/'''
           '''master/Week1/ADRvsRating.csv'''	)
       lowercase__				      =			response.text.splitlines()
       lowercase__				      =			[]
       for item in lines:
              lowercase__				      =			item.split(''','''	)
              data.append(__UpperCamelCase	)
       data.pop(0	)  # This is for removing the labels from the list
       lowercase__				      =			np.matrix(__UpperCamelCase	)
       return dataset

def _SCREAMING_SNAKE_CASE				(A  ,			A  ,			A  ,			A  ,			A	):






       """simple docstring"""







       lowercase__				      =			len_data

       lowercase__				      =			np.dot(__UpperCamelCase  ,			data_x.transpose()	)
       prod -= data_y.transpose()
       lowercase__				      =			np.dot(__UpperCamelCase  ,			__UpperCamelCase	)
       lowercase__				      =			theta - (alpha / n) * sum_grad
       return theta

def _SCREAMING_SNAKE_CASE				(A  ,			A  ,			A  ,			A	):






       """simple docstring"""







       lowercase__				      =			np.dot(__UpperCamelCase  ,			data_x.transpose()	)
       prod -= data_y.transpose()
       lowercase__				      =			np.sum(np.square(__UpperCamelCase	)	)
       lowercase__				      =			sum_elem / (2 * len_data)
       return error

def _SCREAMING_SNAKE_CASE				(A  ,			A	):






       """simple docstring"""







       lowercase__				      =			100_000
       lowercase__				      =			0.0_001_550

       lowercase__				      =			data_x.shape[1]
       lowercase__				      =			data_x.shape[0] - 1

       lowercase__				      =			np.zeros((1, no_features)	)

       for i in range(0  ,			__UpperCamelCase	):
              lowercase__				      =			run_steep_gradient_descent(__UpperCamelCase  ,			__UpperCamelCase  ,			__UpperCamelCase  ,			__UpperCamelCase  ,			__UpperCamelCase	)
              lowercase__				      =			sum_of_square_error(__UpperCamelCase  ,			__UpperCamelCase  ,			__UpperCamelCase  ,			__UpperCamelCase	)
              print(f"At Iteration {i + 1} - Error is {error:.5f}"	)

       return theta

def _SCREAMING_SNAKE_CASE				(A  ,			A	):






       """simple docstring"""







       lowercase__				      =			sum(abs(y - predicted_y[i]	) for i, y in enumerate(__UpperCamelCase	)	)
       return total / len(__UpperCamelCase	)

def _SCREAMING_SNAKE_CASE				():






       """simple docstring"""







       lowercase__				      =			collect_dataset()

       lowercase__				      =			data.shape[0]
       lowercase__				      =			np.c_[np.ones(__UpperCamelCase	), data[:, :-1]].astype(__UpperCamelCase	)
       lowercase__				      =			data[:, -1].astype(__UpperCamelCase	)

       lowercase__				      =			run_linear_regression(__UpperCamelCase  ,			__UpperCamelCase	)
       lowercase__				      =			theta.shape[1]
       print('''Resultant Feature vector : '''	)
       for i in range(0  ,			__UpperCamelCase	):
              print(f"{theta[0, i]:.5f}"	)


if __name__ == "__main__":
 main()








'''simple docstring'''

import math
import os

import cva
import numpy as np

lowerCamelCase							:       Optional[Any]  						=     2_5_5.0

def _SCREAMING_SNAKE_CASE				(A  ,			A	)	->	float:






       """simple docstring"""







       lowercase__				      =			np.mean((original - contrast) ** 2	)
       if mse == 0:
              return 100

       return 20 * math.logaa(PIXEL_MAX / math.sqrt(A	)	)

def _SCREAMING_SNAKE_CASE				()	->	None:






       """simple docstring"""







       lowercase__				      =			os.path.dirname(os.path.realpath(A	)	)
       # Loading images (original image and compressed image)
       lowercase__				      =			cva.imread(os.path.join(A  ,			'''image_data/original_image.png'''	)	)
       lowercase__				      =			cva.imread(os.path.join(A  ,			'''image_data/compressed_image.png'''	)  ,			1	)

       lowercase__				      =			cva.imread(os.path.join(A  ,			'''image_data/PSNR-example-base.png'''	)	)
       lowercase__				      =			cva.imread(
           os.path.join(A  ,			'''image_data/PSNR-example-comp-10.jpg'''	)  ,			1	)

       # Value expected: 29.73dB
       print('''-- First Test --'''	)
       print(f"PSNR value is {peak_signal_to_noise_ratio(A  ,			A	)} dB"	)

       # # Value expected: 31.53dB (Wikipedia Example)
       print('''\n-- Second Test --'''	)
       print(f"PSNR value is {peak_signal_to_noise_ratio(A  ,			A	)} dB"	)


if __name__ == "__main__":
 main()




