



import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets
def 		UpperCamelCase     (       snake_case__	:   Optional[int]     )     ->					Tuple:
   return 1 / (1 + np.exp(-z     ))
def 		UpperCamelCase     (       snake_case__	:   Optional[int]	,					snake_case__	:   Union[str, Any]     )     ->					Optional[int]:
   return (-y * np.log(_UpperCamelCase     ) - (1 - y) * np.log(1 - h     )).mean()
def 		UpperCamelCase     (       snake_case__	:   Dict	,					snake_case__	:   Union[str, Any]	,					snake_case__	:   List[str]     )     ->					Tuple:
   UpperCamelCase   :  Optional[int]						     =     np.dot(_UpperCamelCase	,					_UpperCamelCase     )
   return np.sum(y * scores - np.log(1 + np.exp(_UpperCamelCase     )     )     )
def 		UpperCamelCase     (       snake_case__	:   str	,					snake_case__	:   Any	,					snake_case__	:   Union[str, Any]	,					snake_case__	:   Tuple=70000     )     ->					List[Any]:
   UpperCamelCase   :  Any						     =     np.zeros(x.shape[1]     )

   for iterations in range(_UpperCamelCase     ):
      UpperCamelCase   :  str						     =     np.dot(_UpperCamelCase	,					_UpperCamelCase     )
      UpperCamelCase   :  str						     =     sigmoid_function(_UpperCamelCase     )
      UpperCamelCase   :  List[Any]						     =     np.dot(x.T	,					h - y     ) / y.size
      UpperCamelCase   :  int						     =     theta - alpha * gradient  # updating the weights
      UpperCamelCase   :  Tuple						     =     np.dot(_UpperCamelCase	,					_UpperCamelCase     )
      UpperCamelCase   :  Optional[Any]						     =     sigmoid_function(_UpperCamelCase     )
      UpperCamelCase   :  List[Any]						     =     cost_function(_UpperCamelCase	,					_UpperCamelCase     )
      if iterations % 100 == 0:
         print(F"""loss: {j} \t"""     )  # printing the loss after every 100 iterations
   return theta


# In[68]:

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

    __UpperCAmelCase	       =	0.1
    __UpperCAmelCase	       =	logistic_reg(alpha, x, y, max_iterations=70_000)
    print('''theta: ''', theta)  # printing the theta i.e our weights vector
    def 		UpperCamelCase     (       snake_case__	:   List[Any]     )     ->					List[str]:
       return sigmoid_function(
           np.dot(_UpperCamelCase	,					_UpperCamelCase     )     )  # 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''')
    (__UpperCAmelCase)	       =	(x[:, 0].min(), x[:, 0].max())
    (__UpperCAmelCase)	       =	(x[:, 1].min(), x[:, 1].max())
    (__UpperCAmelCase)	       =	np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
    __UpperCAmelCase	       =	np.c_[xxa.ravel(), xxa.ravel()]
    __UpperCAmelCase	       =	predict_prob(grid).reshape(xxa.shape)
    plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors='''black''')

    plt.legend()


    plt.show()









from abc import ABC, abstractmethod
from argparse import ArgumentParser



class     lowerCAmelCase_		(  a__							):
       @staticmethod
       @abstractmethod
       def  snake_case_	(      SCREAMING_SNAKE_CASE_	)					->		int:
          raise NotImplementedError()






       @abstractmethod
       def  snake_case_	(      self	)					->		str:
          raise NotImplementedError()



