


"""simple docstring"""






import random


class      __lowerCamelCase	:

     '''simple docstring'''


     def __init__(   self     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase = 0.01     ,					__UpperCAmelCase = 1000     ,					__UpperCAmelCase = -1     ,					)							->				None:
          _a     						=					sample
          if len(self.sample				) == 0:
               raise ValueError('''Sample data can not be empty'''				)
          _a     						=					target
          if len(self.target				) == 0:
               raise ValueError('''Target data can not be empty'''				)
          if len(self.sample				) != len(self.target				):
               raise ValueError('''Sample data and Target data do not have matching lengths'''				)
          _a     						=					learning_rate
          _a     						=					epoch_number
          _a     						=					bias
          _a     						=					len(__UpperCAmelCase				)
          _a     						=					len(sample[0]				)  # number of columns in dataset
          _a     						=					[]


     def 				_UpperCAmelCase   (   self				)							->				None:
          for sample in self.sample:
               sample.insert(0     ,					self.bias				)

          for _ in range(self.col_sample				):
               self.weight.append(random.random()				)

          self.weight.insert(0     ,					self.bias				)

          _a     						=					0

          while True:
               _a     						=					False
               for i in range(self.number_sample				):
                    _a     						=					0
                    for j in range(self.col_sample + 1				):
                         _a     						=					u + self.weight[j] * self.sample[i][j]
                    _a     						=					self.sign(__UpperCAmelCase				)
                    if y != self.target[i]:
                         for j in range(self.col_sample + 1				):
                              _a     						=					(
                                  self.weight[j]
                                  + self.learning_rate
                                  * (self.target[i] - y)
                                  * self.sample[i][j]
                              )
                         _a     						=					True
            # print('Epoch: \n',epoch_count)
               _a     						=					epoch_count + 1
               # if you want control the epoch or just by error
               if not has_misclassified:
                    print(('''\nEpoch:\n''', epoch_count)				)
                    print('''------------------------\n'''				)
                    # if epoch_count > self.epoch_number or not error:
                    break


     def 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase				)							->				None:
          if len(self.sample				) == 0:
               raise ValueError('''Sample data can not be empty'''				)
          sample.insert(0     ,					self.bias				)
          _a     						=					0
          for i in range(self.col_sample + 1				):
               _a     						=					u + self.weight[i] * sample[i]

          _a     						=					self.sign(__UpperCAmelCase				)

          if y == -1:
               print(('''Sample: ''', sample)				)
               print('''classification: P1'''				)
          else:
               print(('''Sample: ''', sample)				)
               print('''classification: P2'''				)





     def 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase				)							->				int:
          return 1 if u >= 0 else -1







__snake_case												=							[
    [-0.6508, 0.1097, 4.0009],
    [-1.4492, 0.8896, 4.4005],
    [2.0850, 0.6876, 12.0710],
    [0.2626, 1.1476, 7.7985],
    [0.6418, 1.0234, 7.0427],
    [0.2569, 0.6730, 8.3265],
    [1.1155, 0.6043, 7.4446],
    [0.0914, 0.3399, 7.0677],
    [0.0121, 0.5256, 4.6316],
    [-0.0429, 0.4660, 5.4323],
    [0.4340, 0.6870, 8.2287],
    [0.2735, 1.0287, 7.1934],
    [0.4839, 0.4851, 7.4850],
    [0.4089, -0.1267, 5.5019],
    [1.4391, 0.1614, 8.5843],
    [-0.9115, -0.1973, 2.1962],
    [0.3654, 1.0475, 7.4858],
    [0.2144, 0.7515, 7.1699],
    [0.2013, 1.0014, 6.5489],
    [0.6483, 0.2183, 5.8991],
    [-0.1147, 0.2242, 7.2435],
    [-0.7970, 0.8795, 3.8762],
    [-1.0625, 0.6366, 2.4707],
    [0.5307, 0.1285, 5.6883],
    [-1.2200, 0.7777, 1.7252],
    [0.3957, 0.1076, 5.6623],
    [-0.1013, 0.5989, 7.1812],
    [2.4482, 0.9455, 11.2095],
    [2.0149, 0.6192, 10.9263],
    [0.2012, 0.2611, 5.4631],
]

__snake_case												=							[
    -1,
    -1,
    -1,
    1,
    1,
    -1,
    1,
    -1,
    1,
    1,
    -1,
    1,
    -1,
    -1,
    -1,
    -1,
    1,
    1,
    1,
    1,
    -1,
    1,
    1,
    1,
    1,
    -1,
    -1,
    1,
    -1,
    1,
]


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   __snake_case												=							Perceptron(
       sample=samples, target=target, learning_rate=0.01, epoch_number=1000, bias=-1
   )
   network.training()
   print('''Finished training perceptron''')
   print('''Enter values to predict or q to exit''')
   while True:
      __snake_case												=							[]
      for i in range(len(samples[0])):
         __snake_case												=							input('''value: ''').strip()
         if user_input == "q":
            break
         __snake_case												=							float(user_input)
         sample.insert(i, observation)
      network.sort(sample)



"""simple docstring"""






import math
import os

import cva
import numpy as np

__snake_case												=							255.0
def    A_ ( _lowerCAmelCase  :       float,  _lowerCAmelCase  :       float							):






     """simple docstring"""

     _a     						=					np.mean((original - contrast) ** 2							)
     if mse == 0:
          return 1_00

     return 20 * math.logaa(PIXEL_MAX / math.sqrt(_lowerCAmelCase							)							)
def    A_ ( ):






     """simple docstring"""

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

     _a     						=					cva.imread(os.path.join(_lowerCAmelCase,  '''image_data/PSNR-example-base.png'''							)							)
     _a     						=					cva.imread(
         os.path.join(_lowerCAmelCase,  '''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(_lowerCAmelCase,  _lowerCAmelCase							)} dB'							)

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


if __name__ == "__main__":
   main()