

'''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        _lowerCAmelCase					(						_UpperCamelCase						:     List[Any]				)      ->			Dict:
     """simple docstring"""




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


def        _lowerCAmelCase					(						_UpperCamelCase						:     str							, _UpperCamelCase						:     int				)      ->			Optional[int]:
     """simple docstring"""




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


def        _lowerCAmelCase					(						_UpperCamelCase						:     str							, _UpperCamelCase						:     Optional[int]							, _UpperCamelCase						:     List[Any]				)      ->			Any:
     """simple docstring"""




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


def        _lowerCAmelCase					(						_UpperCamelCase						:     Union[str, Any]							, _UpperCamelCase						:     List[str]							, _UpperCamelCase						:     Union[str, Any]							, _UpperCamelCase						:     Dict=7_00_00				)      ->			Tuple:
     """simple docstring"""




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

     for iterations in range(_UpperCamelCase				):
          _SCREAMING_SNAKE_CASE							    =np.dot(_UpperCamelCase							, _UpperCamelCase				)
          _SCREAMING_SNAKE_CASE							    =sigmoid_function(_UpperCamelCase				)
          _SCREAMING_SNAKE_CASE							    =np.dot(x.T							, h - y				) / y.size
          _SCREAMING_SNAKE_CASE							    =theta - alpha * gradient  # updating the weights
          _SCREAMING_SNAKE_CASE							    =np.dot(_UpperCamelCase							, _UpperCamelCase				)
          _SCREAMING_SNAKE_CASE							    =sigmoid_function(_UpperCamelCase				)
          _SCREAMING_SNAKE_CASE							    =cost_function(_UpperCamelCase							, _UpperCamelCase				)
          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 :      Optional[Any]					 =     datasets.load_iris()
     lowerCamelCase :      Tuple					 =     iris.data[:, :2]
     lowerCamelCase :      Any					 =     (iris.target != 0) * 1

     lowerCamelCase :      List[Any]					 =     0.1
     lowerCamelCase :      Any					 =     logistic_reg(alpha, x, y, max_iterations=7_0_0_0_0)
     print("theta: ", theta)  # printing the theta i.e our weights vector


     def        _lowerCAmelCase					(						_UpperCamelCase						:     Union[str, Any]				)      ->			Union[str, Any]:
          """simple docstring"""




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

     plt.figure(figsize=(1_0, 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)) :      Any					 =     (x[:, 0].min(), x[:, 0].max())
     ((lowerCamelCase) ,    (lowerCamelCase)) :      List[str]					 =     (x[:, 1].min(), x[:, 1].max())
     ((lowerCamelCase) ,    (lowerCamelCase)) :      Optional[Any]					 =     np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
     lowerCamelCase :      Union[str, Any]					 =     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'''
class 							A__    :





  def __init__(		self      :	Union[str, Any]							,					_a      :	int )	->    None:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =size
       _SCREAMING_SNAKE_CASE							    =[0] * size
       _SCREAMING_SNAKE_CASE							    =[0] * size





  @staticmethod
  def 						A  (		_a      :	int )	->    int:





       '''simple docstring'''






       return index | (index + 1)





  @staticmethod
  def 						A  (		_a      :	int )	->    int:





       '''simple docstring'''






       return (index & (index + 1)) - 1





  def 						A  (		self      :	Tuple							,					_a      :	int							,					_a      :	int )	->    None:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =value
       while index < self.size:
            _SCREAMING_SNAKE_CASE							    =self.get_prev(_a ) + 1
            if current_left_border == index:
                 _SCREAMING_SNAKE_CASE							    =value
            else:
                 _SCREAMING_SNAKE_CASE							    =max(_a							,					_a							,					_a )
            _SCREAMING_SNAKE_CASE							    =self.get_next(_a )





  def 						A  (		self      :	int							,					_a      :	int							,					_a      :	int )	->    int:





       '''simple docstring'''






       right -= 1  # Because of right is exclusive
       _SCREAMING_SNAKE_CASE							    =0
       while left <= right:
            _SCREAMING_SNAKE_CASE							    =self.get_prev(_a )
            if left <= current_left:
                 _SCREAMING_SNAKE_CASE							    =max(_a							,					self.tree[right] )
                 _SCREAMING_SNAKE_CASE							    =current_left
            else:
                 _SCREAMING_SNAKE_CASE							    =max(_a							,					self.arr[right] )
                 right -= 1
       return result


if __name__ == "__main__":
     import doctest

     doctest.testmod()
