



"""simple docstring"""
from sklearn.neural_network import MLPClassifier

__SCREAMING_SNAKE_CASE     :  List[Any]										=					[[0.0, 0.0], [1.0, 1.0], [1.0, 0.0], [0.0, 1.0]]
__SCREAMING_SNAKE_CASE     :  Tuple										=					[0, 1, 0, 0]


__SCREAMING_SNAKE_CASE     :  Union[str, Any]										=					MLPClassifier(
    solver='''lbfgs''', alpha=1e-5, hidden_layer_sizes=(5, 2), random_state=1
)

clf.fit(X, y)


__SCREAMING_SNAKE_CASE     :  Tuple										=					[[0.0, 0.0], [0.0, 1.0], [1.0, 1.0]]
__SCREAMING_SNAKE_CASE     :  Union[str, Any]										=					clf.predict(test)



def 				lowerCAmelCase_(      lowercase_   :							Union[str, Any]							)		->				Tuple:
				return list(lowercase_							)


if __name__ == "__main__":
			import doctest

			doctest.testmod()



import math
import os

import cva
import numpy as np

__UpperCAmelCase      = 255.0





def 	snake_case_      (__A     :						float					,   __A     :						float     )			->	float:
   __lowerCAmelCase  :			Any					    =						np.mean((original - contrast) ** 2     )
   if mse == 0:
      return 1_0_0

   return 2_0 * math.logaa(PIXEL_MAX / math.sqrt(__A     )     )





def 	snake_case_      ()			->	None:
   __lowerCAmelCase  :			int					    =						os.path.dirname(os.path.realpath(__A     )     )
   # Loading images (original image and compressed image)
   __lowerCAmelCase  :			Tuple					    =						cva.imread(os.path.join(__A					,   """image_data/original_image.png"""     )     )
   __lowerCAmelCase  :			Dict					    =						cva.imread(os.path.join(__A					,   """image_data/compressed_image.png"""     )					,   1     )

   __lowerCAmelCase  :			Optional[int]					    =						cva.imread(os.path.join(__A					,   """image_data/PSNR-example-base.png"""     )     )
   __lowerCAmelCase  :			Optional[int]					    =						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()


