




'''simple docstring'''







import numpy

# List of input, output pairs
lowercase      :   Any      =       (
    ((5, 2, 3), 15),
    ((6, 5, 9), 25),
    ((11, 12, 13), 41),
    ((1, 1, 1), 8),
    ((11, 12, 13), 41),
)
lowercase      :   Optional[int]      =       (((515, 22, 13), 555), ((61, 35, 49), 150))
lowercase      :   List[str]      =       [2, 4, 1, 5]
lowercase      :   Tuple      =       len(train_data)
lowercase      :   Tuple      =       0.0_0_9



def        lowerCamelCase__				(      __lowercase     ,   __lowercase="train"				):
     return calculate_hypothesis_value(__lowercase     ,   __lowercase				) - output(
         __lowercase     ,   __lowercase				)



def        lowerCamelCase__				(      __lowercase				):
     snake_case			:      Tuple        =			0
     for i in range(len(__lowercase				) - 1				):
          hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
     hyp_val += parameter_vector[0]
     return hyp_val



def        lowerCamelCase__				(      __lowercase     ,   __lowercase				):
     if data_set == "train":
          return train_data[example_no][1]
     elif data_set == "test":
          return test_data[example_no][1]
     return None



def        lowerCamelCase__				(      __lowercase     ,   __lowercase				):
     if data_set == "train":
          return _hypothesis_value(train_data[example_no][0]				)
     elif data_set == "test":
          return _hypothesis_value(test_data[example_no][0]				)
     return None



def        lowerCamelCase__				(      __lowercase     ,   __lowercase=m				):
     snake_case			:      str        =			0
     for i in range(__lowercase				):
          if index == -1:
               summation_value += _error(__lowercase				)
          else:
               summation_value += _error(__lowercase				) * train_data[i][0][index]
     return summation_value



def        lowerCamelCase__				(      __lowercase				):
     snake_case			:      Optional[Any]        =			summation_of_cost_derivative(__lowercase     ,   __lowercase				) / m
     return cost_derivative_value



def        lowerCamelCase__				(      ):
     global parameter_vector
     # Tune these values to set a tolerance value for predicted output
     snake_case			:      List[str]        =			0.00_0002
     snake_case			:      Union[str, Any]        =			0
     snake_case			:      Any        =			0
     while True:
          j += 1
          snake_case			:      Optional[int]        =			[0, 0, 0, 0]
          for i in range(0     ,   len(__lowercase				)				):
               snake_case			:      int        =			get_cost_derivative(i - 1				)
               snake_case			:      Dict        =			(
                   parameter_vector[i] - LEARNING_RATE * cost_derivative
               )
          if numpy.allclose(
              __lowercase     ,   __lowercase     ,   atol=__lowercase     ,   rtol=__lowercase     ,   ):
               break
          snake_case			:      Dict        =			temp_parameter_vector
     print(("""Number of iterations:""", j)				)



def        lowerCamelCase__				(      ):
     for i in range(len(__lowercase				)				):
          print(("""Actual output value:""", output(__lowercase     ,   """test"""				))				)
          print(("""Hypothesis output:""", calculate_hypothesis_value(__lowercase     ,   """test"""				))				)


if __name__ == "__main__":
      run_gradient_descent()
      print("""\nTesting gradient descent for a linear hypothesis function.\n""")
      test_gradient_descent()









'''simple docstring'''







import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image

from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processing.dithering import burkes as bs
from digital_image_processing.edge_detection import canny
from digital_image_processing.filters import convolve as conv
from digital_image_processing.filters import gaussian_filter as gg
from digital_image_processing.filters import local_binary_pattern as lbp
from digital_image_processing.filters import median_filter as med
from digital_image_processing.filters import sobel_filter as sob
from digital_image_processing.resize import resize as rs

lowercase      :   Any      =       imread(R"""digital_image_processing/image_data/lena_small.jpg""")
lowercase      :   List[str]      =       cvtColor(img, COLOR_BGR2GRAY)



def        lowerCamelCase__				(      ):
     snake_case			:      Optional[int]        =			cn.convert_to_negative(__lowercase				)
     # assert negative_img array for at least one True
     assert negative_img.any()



def        lowerCamelCase__				(      ):
     with Image.open("""digital_image_processing/image_data/lena_small.jpg"""				) as img:
          # Work around assertion for response
          assert str(cc.change_contrast(__lowercase     ,   110				)				).startswith(
              """<PIL.Image.Image image mode=RGB size=100x100 at"""				)



def        lowerCamelCase__				(      ):
     snake_case			:      Union[str, Any]        =			canny.gen_gaussian_kernel(9     ,   sigma=1.4				)
     # Assert ambiguous array
     assert resp.all()



def        lowerCamelCase__				(      ):
     snake_case			:      Optional[Any]        =			imread("""digital_image_processing/image_data/lena_small.jpg"""     ,   0				)
     # assert ambiguous array for all == True
     assert canny_img.all()
     snake_case			:      Any        =			canny.canny(__lowercase				)
     # assert canny array for at least one True
     assert canny_array.any()



def        lowerCamelCase__				(      ):
     assert gg.gaussian_filter(__lowercase     ,   5     ,   sigma=0.9				).all()



def        lowerCamelCase__				(      ):
     # laplace diagonals
     snake_case			:      int        =			array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]]				)
     snake_case			:      Tuple        =			conv.img_convolve(__lowercase     ,   __lowercase				).astype(__lowercase				)
     assert res.any()



def        lowerCamelCase__				(      ):
     assert med.median_filter(__lowercase     ,   3				).any()



def        lowerCamelCase__				(      ):
     snake_case ,		snake_case			:      Dict        =			sob.sobel_filter(__lowercase				)
     assert grad.any() and theta.any()



def        lowerCamelCase__				(      ):
     snake_case			:      int        =			sp.make_sepia(__lowercase     ,   20				)
     assert sepia.all()



def        lowerCamelCase__				(      __lowercase = "digital_image_processing/image_data/lena_small.jpg"				):
     snake_case			:      int        =			bs.Burkes(imread(__lowercase     ,   1				)     ,   120				)
     burkes.process()
     assert burkes.output_img.any()



def        lowerCamelCase__				(      __lowercase = "digital_image_processing/image_data/lena_small.jpg"     ,   ):
     snake_case			:      Dict        =			rs.NearestNeighbour(imread(__lowercase     ,   1				)     ,   400     ,   200				)
     nn.process()
     assert nn.output.any()







def        lowerCamelCase__				(      ):
     snake_case			:      int        =			"""digital_image_processing/image_data/lena.jpg"""

     # Reading the image and converting it to grayscale.
     snake_case			:      Tuple        =			imread(__lowercase     ,   0				)

     # Test for get_neighbors_pixel function() return not None
     snake_case			:      Optional[int]        =			0
     snake_case			:      Tuple        =			0
     snake_case			:      Any        =			image[x_coordinate][y_coordinate]

     snake_case			:      Any        =			lbp.get_neighbors_pixel(
         __lowercase     ,   __lowercase     ,   __lowercase     ,   __lowercase				)

     assert neighbors_pixels is not None

     # Test for local_binary_pattern function()
     # Create a numpy array as the same height and width of read image
     snake_case			:      List[Any]        =			np.zeros((image.shape[0], image.shape[1])				)

     # Iterating through the image and calculating the local binary pattern value
     # for each pixel.
     for i in range(0     ,   image.shape[0]				):
          for j in range(0     ,   image.shape[1]				):
               snake_case			:      Optional[Any]        =			lbp.local_binary_value(__lowercase     ,   __lowercase     ,   __lowercase				)

     assert lbp_image.any()



