





"""simple docstring"""
from __future__ import annotations






class  _UpperCAmelCase       :


					def __init__( self      :Optional[Any]					,	__UpperCamelCase      :int		):
												A		=  vertices
												A		=  [[0] * vertices for _ in range(__UpperCamelCase		)]


					def      lowerCamelCase	( self      :Optional[Any]					,	__UpperCamelCase      :list[int]		):
												print("Vertex \t Distance from Source"		)
												for vertex in range(self.vertices		):
																			print(__UpperCamelCase					,	"\t\t"					,	distances_from_source[vertex]		)


					def      lowerCamelCase	( self      :Union[str, Any]					,	__UpperCamelCase      :list[int]					,	__UpperCamelCase      :list[bool]		):
												A		=  1e7
												A		=  0

												# Search not nearest vertex not in the shortest path tree
												for vertex in range(self.vertices		):
																			if distances_from_source[vertex] < minimum and visited[vertex] is False:
																										A		=  distances_from_source[vertex]
																										A		=  vertex
												return min_index


					def      lowerCamelCase	( self      :List[Any]					,	__UpperCamelCase      :int		):
												A		=  [int(1e7		)] * self.vertices  # distances from the source
												A		=  0
												A		=  [False] * self.vertices

												for _ in range(self.vertices		):
																			A		=  self.minimum_distance(__UpperCamelCase					,	__UpperCamelCase		)
																			A		=  True

																			# Update dist value of the adjacent vertices
																			# of the picked vertex only if the current
																			# distance is greater than new distance and
																			# the vertex in not in the shortest path tree
																			for v in range(self.vertices		):
																										if (
																										    self.graph[u][v] > 0
																										    and visited[v] is False
																										    and distances[v] > distances[u] + self.graph[u][v]
																										):
																																	A		=  distances[u] + self.graph[u][v]

												self.print_solution(__UpperCamelCase		)


if __name__ == "__main__":
		_snake_case					:		List[Any]							=			Graph(9)
		_snake_case					:		List[str]							=			[
		    [0, 4, 0, 0, 0, 0, 0, 8, 0],
		    [4, 0, 8, 0, 0, 0, 0, 11, 0],
		    [0, 8, 0, 7, 0, 4, 0, 0, 2],
		    [0, 0, 7, 0, 9, 14, 0, 0, 0],
		    [0, 0, 0, 9, 0, 10, 0, 0, 0],
		    [0, 0, 4, 14, 10, 0, 2, 0, 0],
		    [0, 0, 0, 0, 0, 2, 0, 1, 6],
		    [8, 11, 0, 0, 0, 0, 1, 0, 7],
		    [0, 0, 2, 0, 0, 0, 6, 7, 0],
		]
		graph.dijkstra(0)









"""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()


