


'''simple docstring'''
class 					lowerCAmelCase_     :






       def __init__(   self ,    _UpperCamelCase      )->			None:
            _A				=       data
            _A				=       None
            _A				=       None






def 				lowerCamelCase_   (   __UpperCamelCase				:					Optional[Any]       ,						__UpperCamelCase				:					Union[str, Any]		)				->  BinaryTreeNode | None:







     """simple docstring"""







     if node is None:
          _A				=       BinaryTreeNode(__UpperCamelCase		)
          return node

     # binary search tree is not empty,
     # so we will insert it into the tree
     # if new_value is less than value of data in node,
     #  add it to left subtree and proceed recursively
     if new_value < node.data:
          _A				=       insert(node.left_child       ,						__UpperCamelCase		)
     else:
          # if new_value is greater than value of data in node,
          #  add it to right subtree and proceed recursively
          _A				=       insert(node.right_child       ,						__UpperCamelCase		)
     return node

def 				lowerCamelCase_   (   __UpperCamelCase				:					Optional[Any]		)				->  list[int]:  # if node is None,return







     """simple docstring"""







     if node:
          _A				=       inorder(node.left_child		)
          _A				=       [*inorder_array, node.data]
          _A				=       inorder_array + inorder(node.right_child		)
     else:
          _A				=       []
     return inorder_array

def 				lowerCamelCase_   (   )				->  BinaryTreeNode | None:







     """simple docstring"""
     _A				=       insert(__UpperCamelCase       ,						1_5		)
     insert(__UpperCamelCase       ,						1_0		)
     insert(__UpperCamelCase       ,						2_5		)
     insert(__UpperCamelCase       ,						6		)
     insert(__UpperCamelCase       ,						1_4		)
     insert(__UpperCamelCase       ,						2_0		)
     insert(__UpperCamelCase       ,						6_0		)
     return root

def 				lowerCamelCase_   (   )				->  None:







     """simple docstring"""
     _A				=       make_tree()
     print('Printing values of binary search tree in Inorder Traversal.'		)
     inorder(__UpperCamelCase		)


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      main()









'''simple docstring'''
import numpy as np
import requests

def 				lowerCamelCase_   (   )				->  int:







     """simple docstring"""
     _A				=       requests.get(
         'https://raw.githubusercontent.com/yashLadha/The_Math_of_Intelligence/'
         'master/Week1/ADRvsRating.csv'		)
     _A				=       response.text.splitlines()
     _A				=       []
     for item in lines:
          _A				=       item.split(','		)
          data.append(__UpperCamelCase		)
     data.pop(0		)  # This is for removing the labels from the list
     _A				=       np.matrix(__UpperCamelCase		)
     return dataset

def 				lowerCamelCase_   (   __UpperCamelCase				:					Union[str, Any]       ,						__UpperCamelCase				:					Tuple       ,						__UpperCamelCase				:					int       ,						__UpperCamelCase				:					Tuple       ,						__UpperCamelCase				:					Dict		)				->  int:







     """simple docstring"""
     _A				=       len_data

     _A				=       np.dot(__UpperCamelCase       ,						data_x.transpose()		)
     prod -= data_y.transpose()
     _A				=       np.dot(__UpperCamelCase       ,						__UpperCamelCase		)
     _A				=       theta - (alpha / n) * sum_grad
     return theta

def 				lowerCamelCase_   (   __UpperCamelCase				:					Optional[int]       ,						__UpperCamelCase				:					Any       ,						__UpperCamelCase				:					List[str]       ,						__UpperCamelCase				:					Optional[int]		)				->  Dict:







     """simple docstring"""
     _A				=       np.dot(__UpperCamelCase       ,						data_x.transpose()		)
     prod -= data_y.transpose()
     _A				=       np.sum(np.square(__UpperCamelCase		)		)
     _A				=       sum_elem / (2 * len_data)
     return error

def 				lowerCamelCase_   (   __UpperCamelCase				:					Dict       ,						__UpperCamelCase				:					List[str]		)				->  int:







     """simple docstring"""
     _A				=       1_0_0_0_0_0
     _A				=       0.0_00_15_50

     _A				=       data_x.shape[1]
     _A				=       data_x.shape[0] - 1

     _A				=       np.zeros((1, no_features)		)

     for i in range(0       ,						__UpperCamelCase		):
          _A				=       run_steep_gradient_descent(__UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase		)
          _A				=       sum_of_square_error(__UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase		)
          print(F'At Iteration {i + 1} - Error is {error:.5f}'		)

     return theta

def 				lowerCamelCase_   (   __UpperCamelCase				:					Dict       ,						__UpperCamelCase				:					Tuple		)				->  Union[str, Any]:







     """simple docstring"""
     _A				=       sum(abs(y - predicted_y[i]		) for i, y in enumerate(__UpperCamelCase		)		)
     return total / len(__UpperCamelCase		)

def 				lowerCamelCase_   (   )				->  Optional[Any]:







     """simple docstring"""
     _A				=       collect_dataset()

     _A				=       data.shape[0]
     _A				=       np.c_[np.ones(__UpperCamelCase		), data[:, :-1]].astype(__UpperCamelCase		)
     _A				=       data[:, -1].astype(__UpperCamelCase		)

     _A				=       run_linear_regression(__UpperCamelCase       ,						__UpperCamelCase		)
     _A				=       theta.shape[1]
     print('Resultant Feature vector : '		)
     for i in range(0       ,						__UpperCamelCase		):
          print(F'{theta[0, i]:.5f}'		)


if __name__ == "__main__":
    main()





