





"""simple docstring"""
class  _UpperCAmelCase       :


					def __init__( self      :int		):
												A		=  [0]
												A		=  0


					def      lowerCamelCase	( self      :Optional[int]					,	__UpperCamelCase      :int		):
												A		=  self.__heap[i]
												while i // 2 > 0:
																			if self.__heap[i] > self.__heap[i // 2]:
																										A		=  self.__heap[i // 2]
																										A		=  temporary
																			i //= 2


					def      lowerCamelCase	( self      :Any					,	__UpperCamelCase      :int		):
												self.__heap.append(__UpperCamelCase		)
												self.__size += 1
												self.__swap_up(self.__size		)


					def      lowerCamelCase	( self      :Dict					,	__UpperCamelCase      :int		):
												while self.__size >= 2 * i:
																			if 2 * i + 1 > self.__size:
																										A		=  2 * i
																			else:
																										if self.__heap[2 * i] > self.__heap[2 * i + 1]:
																																	A		=  2 * i
																										else:
																																	A		=  2 * i + 1
																			A		=  self.__heap[i]
																			if self.__heap[i] < self.__heap[bigger_child]:
																										A		=  self.__heap[bigger_child]
																										A		=  temporary
																			A		=  bigger_child


					def      lowerCamelCase	( self      :Dict		):
												A		=  self.__heap[1]
												A		=  self.__heap[self.__size]
												self.__size -= 1
												self.__heap.pop()
												self.__swap_down(1		)
												return max_value


					@property
					def      lowerCamelCase	( self      :Tuple		):
												return self.__heap[1:]


					def __len__( self      :List[str]		):
												return self.__size


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					# create an instance of BinaryHeap
					_snake_case					:		Union[str, Any]							=			BinaryHeap()
					binary_heap.insert(6)
					binary_heap.insert(10)
					binary_heap.insert(15)
					binary_heap.insert(12)
					# pop root(max-values because it is max heap)
					print(binary_heap.pop())  # 15
					print(binary_heap.pop())  # 12
					# get the list and size after operations
					print(binary_heap.get_list)
					print(len(binary_heap))









"""simple docstring"""
import numpy as np
import requests






def  A__    (      ):
							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  A__    (      UpperCamelCase ,  UpperCamelCase ,  UpperCamelCase ,  UpperCamelCase ,  UpperCamelCase	):
							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  A__    (      UpperCamelCase ,  UpperCamelCase ,  UpperCamelCase ,  UpperCamelCase	):
							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  A__    (      UpperCamelCase ,  UpperCamelCase	):
							A		=  100_000
							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  A__    (      UpperCamelCase ,  UpperCamelCase	):
							A		=  sum(abs(y - predicted_y[i]	) for i, y in enumerate(UpperCamelCase	)	)
							return total / len(UpperCamelCase	)






def  A__    (      ):
							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()


