


# 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			:    int						):

      """simple docstring"""




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

def       lowercase_		(		_A			:    Any    ,					_A			:    Optional[int]						):

      """simple docstring"""




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

def       lowercase_		(		_A			:    Optional[Any]    ,					_A			:    Dict    ,					_A			:    Union[str, Any]						):

      """simple docstring"""




      lowerCamelCase__		:				Union[str, Any]    =  np.dot(_A    ,					_A						)
      return np.sum(y * scores - np.log(1 + np.exp(_A						)						)						)

def       lowercase_		(		_A			:    Union[str, Any]    ,					_A			:    Dict    ,					_A			:    Tuple    ,					_A			:    Dict=70000						):

      """simple docstring"""




      lowerCamelCase__		:				int    =  np.zeros(x.shape[1]						)

      for iterations in range(_A						):
            lowerCamelCase__		:				Optional[Any]    =  np.dot(_A    ,					_A						)
            lowerCamelCase__		:				Optional[int]    =  sigmoid_function(_A						)
            lowerCamelCase__		:				Union[str, Any]    =  np.dot(x.T    ,					h - y						) / y.size
            lowerCamelCase__		:				Union[str, Any]    =  theta - alpha * gradient  # updating the weights
            lowerCamelCase__		:				List[Any]    =  np.dot(_A    ,					_A						)
            lowerCamelCase__		:				List[str]    =  sigmoid_function(_A						)
            lowerCamelCase__		:				List[str]    =  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      :    List[Any]		=					datasets.load_iris()
    A      :    List[str]		=					iris.data[:, :2]
    A      :    str		=					(iris.target != 0) * 1

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

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

    plt.legend()


    plt.show()






class 			_lowercase :


       """simple docstring"""




       def __init__(   self							:			List[Any]					,      __lowerCamelCase							:			int       ):


             '''simple docstring'''



             lowerCamelCase__		:				Optional[Any]    =  size
             lowerCamelCase__		:				List[str]    =  [0] * size
             lowerCamelCase__		:				str    =  [0] * size





       @staticmethod
       def 						lowerCAmelCase     (   __lowerCamelCase							:			int       ):


             '''simple docstring'''



             return index | (index + 1)





       @staticmethod
       def 						lowerCAmelCase     (   __lowerCamelCase							:			int       ):


             '''simple docstring'''



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





       def 						lowerCAmelCase     (   self							:			int					,      __lowerCamelCase							:			int					,      __lowerCamelCase							:			int       ):


             '''simple docstring'''



             lowerCamelCase__		:				Union[str, Any]    =  value
             while index < self.size:
                   lowerCamelCase__		:				Tuple    =  self.get_prev(__lowerCamelCase       ) + 1
                   if current_left_border == index:
                         lowerCamelCase__		:				Optional[Any]    =  value
                   else:
                         lowerCamelCase__		:				str    =  max(__lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase       )
                   lowerCamelCase__		:				Dict    =  self.get_next(__lowerCamelCase       )





       def 						lowerCAmelCase     (   self							:			List[Any]					,      __lowerCamelCase							:			int					,      __lowerCamelCase							:			int       ):


             '''simple docstring'''



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


if __name__ == "__main__":
    import doctest

    doctest.testmod()


