






'''simple docstring'''





import numpy as np
import requests



def 			__UpperCAmelCase				()		->				Optional[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(lowercase__				)
						data.pop(0				)  # This is for removing the labels from the list
						a_				     =   np.matrix(lowercase__				)
						return dataset



def 			__UpperCAmelCase				(lowercase__	,lowercase__	,lowercase__	,lowercase__	,lowercase__				)		->				Optional[Any]:

						'''simple docstring'''


						a_				     =   len_data

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



def 			__UpperCAmelCase				(lowercase__	,lowercase__	,lowercase__	,lowercase__				)		->				str:

						'''simple docstring'''


						a_				     =   np.dot(lowercase__	,data_x.transpose()				)
						prod -= data_y.transpose()
						a_				     =   np.sum(np.square(lowercase__				)				)
						a_				     =   sum_elem / (2 * len_data)
						return error



def 			__UpperCAmelCase				(lowercase__	,lowercase__				)		->				Optional[Any]:

						'''simple docstring'''


						a_				     =   100000
						a_				     =   0.000_1550

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

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

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

						return theta



def 			__UpperCAmelCase				(lowercase__	,lowercase__				)		->				List[str]:

						'''simple docstring'''


						a_				     =   sum(abs(y - predicted_y[i]				) for i, y in enumerate(lowercase__				)				)
						return total / len(lowercase__				)



def 			__UpperCAmelCase				()		->				Dict:

						'''simple docstring'''


						a_				     =   collect_dataset()

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

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


if __name__ == "__main__":
							main()











'''simple docstring'''





from __future__ import annotations





class 		SCREAMING_SNAKE_CASE__   :



		def __init__(    self:    List[str]				,   a:    int = 0)  ->None:






								'''simple docstring'''


								a_				     =   value
								a_				     =   None
								a_				     =   None





def 			__UpperCAmelCase				(lowercase__	,lowercase__				)		->				Node | None:

						'''simple docstring'''


						if treea is None:
												return treea
						if treea is None:
												return treea

						a_				     =   treea.value + treea.value
						a_				     =   merge_two_binary_trees(treea.left	,treea.left				)
						a_				     =   merge_two_binary_trees(treea.right	,treea.right				)
						return treea



def 			__UpperCAmelCase				(lowercase__				)		->				None:

						'''simple docstring'''


						if root:
												print(root.value				)
												print_preorder(root.left				)
												print_preorder(root.right				)


if __name__ == "__main__":
							a_      = Node(1)
							a_      = Node(2)
							a_      = Node(3)
							a_      = Node(4)

							a_      = Node(2)
							a_      = Node(4)
							a_      = Node(6)
							a_      = Node(9)
							a_      = Node(5)

							print('Tree1 is: ')
							print_preorder(treea)
							print('Tree2 is: ')
							print_preorder(treea)
							a_      = merge_two_binary_trees(treea, treea)
							print('Merged Tree is: ')
							print_preorder(merged_tree)



