
# 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 						lowercase_      (       A__		)							->							List[str]:
    """simple docstring"""

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



def 						lowercase_      (       A__			,		A__		)							->							int:
    """simple docstring"""

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



def 						lowercase_      (       A__			,		A__			,		A__		)							->							Dict:
    """simple docstring"""

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



def 						lowercase_      (       A__			,		A__			,		A__			,		A__=7_0000		)							->							List[Any]:
    """simple docstring"""

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

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


# In[68]:

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

   _A   =			0.1
   _A   =			logistic_reg(alpha, x, y, max_iterations=7_00_00)
   print("theta: ", theta)  # printing the theta i.e our weights vector



   def 						lowercase_      (       A__		)							->							Union[str, Any]:
       """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")
   ((_A) ,  (_A))   =			(x[:, 0].min(), x[:, 0].max())
   ((_A) ,  (_A))   =			(x[:, 1].min(), x[:, 1].max())
   ((_A) ,  (_A))   =			np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
   _A   =			np.c_[xxa.ravel(), xxa.ravel()]
   _A   =			predict_prob(grid).reshape(xxa.shape)
   plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors="black")

   plt.legend()





   plt.show()



class 					lowerCamelCase		:





     def __init__(self				:   List[Any]    ,   _A				:   str       )						->						Any:
         # we need a list not a string, so do something to change the type
         snake_case								= arr.split(","       )





     def 		UpperCAmelCase(self				:   str       )						->						str:
         snake_case								= [int(self.array[0]       )] * len(self.array       )
         snake_case								= [int(self.array[0]       )] * len(self.array       )
         for i in range(1    ,   len(self.array       )       ):
             snake_case								= max(
                 int(self.array[i]       ) + sum_value[i - 1]    ,   int(self.array[i]       )       )
             snake_case								= max(sum_value[i]    ,   rear[i - 1]       )
         return rear[len(self.array       ) - 1]


if __name__ == "__main__":
   _A   =			input("please input some numbers:")
   _A   =			SubArray(whole_array)
   _A   =			array.solve_sub_array()
   print(("the results is:", re))

