




'''simple docstring'''

import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets



def 							UpperCamelCase__			(			a__      ):


						'''simple docstring'''






						return 1 / (1 + np.exp(-z      ))



def 							UpperCamelCase__			(			a__			,      a__      ):


						'''simple docstring'''






						return (-y * np.log(a__      ) - (1 - y) * np.log(1 - h      )).mean()



def 							UpperCamelCase__			(			a__			,      a__			,      a__      ):


						'''simple docstring'''






						_lowerCAmelCase										=np.dot(a__			,      a__      )
						return np.sum(y * scores - np.log(1 + np.exp(a__      )      )      )



def 							UpperCamelCase__			(			a__			,      a__			,      a__			,      a__=7_0_0_0_0      ):


						'''simple docstring'''






						_lowerCAmelCase										=np.zeros(x.shape[1]      )

						for iterations in range(a__      ):
												_lowerCAmelCase										=np.dot(a__			,      a__      )
												_lowerCAmelCase										=sigmoid_function(a__      )
												_lowerCAmelCase										=np.dot(x.T			,      h - y      ) / y.size
												_lowerCAmelCase										=theta - alpha * gradient  # updating the weights
												_lowerCAmelCase										=np.dot(a__			,      a__      )
												_lowerCAmelCase										=sigmoid_function(a__      )
												_lowerCAmelCase										=cost_function(a__			,      a__      )
												if iterations % 1_0_0 == 0:
																		print(F'''loss: {j} \t'''      )  # printing the loss after every 100 iterations
						return theta


# In[68]:

if __name__ == "__main__":
							lowercase_     		= datasets.load_iris()
							lowercase_     		= iris.data[:, :2]
							lowercase_     		= (iris.target != 0) * 1

							lowercase_     		= 0.1
							lowercase_     		= logistic_reg(alpha, x, y, max_iterations=7_0000)
							print('''theta: ''', theta)  # printing the theta i.e our weights vector



							def 							UpperCamelCase__			(			a__      ):


													'''simple docstring'''






													return sigmoid_function(
													    np.dot(a__			,      a__      )      )  # predicting the value of probability from the logistic regression algorithm

							plt.figure(figsize=(10, 6))
							plt.scatter(x[y == 0][:, 0], x[y == 0][:, 1], color='''b''', label='''0''')
							plt.scatter(x[y == 1][:, 0], x[y == 1][:, 1], color='''r''', label='''1''')
							((lowercase_)		,			(lowercase_))     		= (x[:, 0].min(), x[:, 0].max())
							((lowercase_)		,			(lowercase_))     		= (x[:, 1].min(), x[:, 1].max())
							((lowercase_)		,			(lowercase_))     		= np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
							lowercase_     		= np.c_[xxa.ravel(), xxa.ravel()]
							lowercase_     		= predict_prob(grid).reshape(xxa.shape)
							plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors='''black''')

							plt.legend()


							plt.show()






'''simple docstring'''

from PIL import Image



def 							UpperCamelCase__			(			a__			,      a__      ):


						'''simple docstring'''






						def brightness(a__      ) -> float:
												return 1_2_8 + level + (c - 1_2_8)

						if not -255.0 <= level <= 255.0:
												raise ValueError('level must be between -255.0 (black) and 255.0 (white)'      )
						return img.point(a__      )


if __name__ == "__main__":
							# Load image
							with Image.open('''image_data/lena.jpg''') as img:
														# Change brightness to 100
														lowercase_     		= change_brightness(img, 100)
														brigt_img.save('''image_data/lena_brightness.png''', format='''png''')





