




"""simple docstring"""




# Logistic Regression from scratch

# In[62]:

# In[63]:

# importing all the required libraries

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



def 					a__				(       snake_case__       )		->      Tuple:
			return 1 / (1 + np.exp(-z       ))



def 					a__				(       snake_case__ ,				snake_case__       )		->      Optional[Any]:
			return (-y * np.log(snake_case__       ) - (1 - y) * np.log(1 - h       )).mean()



def 					a__				(       snake_case__ ,				snake_case__ ,				snake_case__       )		->      Any:
			lowerCamelCase       =      np.dot(snake_case__ ,				snake_case__       )
			return np.sum(y * scores - np.log(1 + np.exp(snake_case__       )       )       )



def 					a__				(       snake_case__ ,				snake_case__ ,				snake_case__ ,				snake_case__=7_00_00       )		->      Optional[int]:
			lowerCamelCase       =      np.zeros(x.shape[1]       )

			for iterations in range(snake_case__       ):
						lowerCamelCase       =      np.dot(snake_case__ ,				snake_case__       )
						lowerCamelCase       =      sigmoid_function(snake_case__       )
						lowerCamelCase       =      np.dot(x.T ,				h - y       ) / y.size
						lowerCamelCase       =      theta - alpha * gradient  # updating the weights
						lowerCamelCase       =      np.dot(snake_case__ ,				snake_case__       )
						lowerCamelCase       =      sigmoid_function(snake_case__       )
						lowerCamelCase       =      cost_function(snake_case__ ,				snake_case__       )
						if iterations % 1_00 == 0:
									print(F'loss: {j} \t'       )  # printing the loss after every 100 iterations
			return theta


# In[68]:

if __name__ == "__main__":
					lowerCAmelCase							:    Union[str, Any]  							=							datasets.load_iris()
					lowerCAmelCase							:    Optional[Any]  							=							iris.data[:, :2]
					lowerCAmelCase							:    Optional[int]  							=							(iris.target != 0) * 1

					lowerCAmelCase							:    List[str]  							=							0.1
					lowerCAmelCase							:    Dict  							=							logistic_reg(alpha, x, y, max_iterations=7_0000)
					print("""theta: """, theta)  # printing the theta i.e our weights vector



					def 					a__				(       snake_case__       )		->      Tuple:
								return sigmoid_function(
								    np.dot(snake_case__ ,				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""")
					((lowerCAmelCase)							,		(lowerCAmelCase))							:    int  							=							(x[:, 0].min(), x[:, 0].max())
					((lowerCAmelCase)							,		(lowerCAmelCase))							:    Dict  							=							(x[:, 1].min(), x[:, 1].max())
					((lowerCAmelCase)							,		(lowerCAmelCase))							:    Union[str, Any]  							=							np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
					lowerCAmelCase							:    str  							=							np.c_[xxa.ravel(), xxa.ravel()]
					lowerCAmelCase							:    Optional[Any]  							=							predict_prob(grid).reshape(xxa.shape)
					plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors="""black""")

					plt.legend()


					plt.show()









"""simple docstring"""




from ..utils import DummyObject, requires_backends






class 					__magic_name__							( metaclass=UpperCAmelCase__       ):






				'''simple docstring'''
				__UpperCamelCase    						=		["note_seq"]





				def __init__( self	,				*_a	,				**_a				):





							"""simple docstring"""
							requires_backends(self	,				["""note_seq"""]				)





				@classmethod
				def      _lowerCAmelCase       ( cls	,				*_a	,				**_a				):





							"""simple docstring"""
							requires_backends(cls	,				["""note_seq"""]				)





				@classmethod
				def      _lowerCAmelCase       ( cls	,				*_a	,				**_a				):





							"""simple docstring"""
							requires_backends(cls	,				["""note_seq"""]				)



