


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




def 	lowerCamelCase_							(       UpperCAmelCase__   ):



						"""simple docstring"""




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




def 	lowerCamelCase_							(       UpperCAmelCase__							,     UpperCAmelCase__   ):



						"""simple docstring"""




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




def 	lowerCamelCase_							(       UpperCAmelCase__							,     UpperCAmelCase__							,     UpperCAmelCase__   ):



						"""simple docstring"""




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




def 	lowerCamelCase_							(       UpperCAmelCase__							,     UpperCAmelCase__							,     UpperCAmelCase__							,     UpperCAmelCase__=70_000   ):



						"""simple docstring"""




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

						for iterations in range(SCREAMING_SNAKE_CASE_   ):
												a_		      =		np.dot(SCREAMING_SNAKE_CASE_							,     SCREAMING_SNAKE_CASE_   )
												a_		      =		sigmoid_function(SCREAMING_SNAKE_CASE_   )
												a_		      =		np.dot(x.T							,     h - y   ) / y.size
												a_		      =		theta - alpha * gradient  # updating the weights
												a_		      =		np.dot(SCREAMING_SNAKE_CASE_							,     SCREAMING_SNAKE_CASE_   )
												a_		      =		sigmoid_function(SCREAMING_SNAKE_CASE_   )
												a_		      =		cost_function(SCREAMING_SNAKE_CASE_							,     SCREAMING_SNAKE_CASE_   )
												if iterations % 100 == 0:
																		print(F"loss: {j} \t"   )  # printing the loss after every 100 iterations
						return theta


# In[68]:

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

	A_     :       Optional[int]   							=0.1
	A_     :       int   							=logistic_reg(alpha, x, y, max_iterations=70000)
	print("""theta: """, theta)  # printing the theta i.e our weights vector




	def 	lowerCamelCase_							(       UpperCAmelCase__   ):



							"""simple docstring"""




							return sigmoid_function(
							    np.dot(SCREAMING_SNAKE_CASE_							,     SCREAMING_SNAKE_CASE_   )   )  # 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""")
	((A_)							,						(A_))     :       List[Any]   							=(x[:, 0].min(), x[:, 0].max())
	((A_)							,						(A_))     :       Tuple   							=(x[:, 1].min(), x[:, 1].max())
	((A_)							,						(A_))     :       Any   							=np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
	A_     :       Tuple   							=np.c_[xxa.ravel(), xxa.ravel()]
	A_     :       Dict   							=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 			lowerCamelCase(							SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_     )		->		Image:

			def brightness(SCREAMING_SNAKE_CASE_     ) -> float:
						return 128 + level + (c - 128)

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


if __name__ == "__main__":
							# Load image
							with Image.open("image_data/lena.jpg") as img:
														# Change brightness to 100
														_SCREAMING_SNAKE_CASE			=						change_brightness(img, 1_00)
														brigt_img.save("image_data/lena_brightness.png", format="png")




