


from __future__ import annotations

from queue import Queue


class   lowerCamelCase__  :



 """simple docstring"""

 def __init__(						self				:				Dict			)      ->  List[Any]:
    _A						     =					{}

 def 						snake_case_			(						self				:				str			)      ->  Union[str, Any]:
    for i in self.vertices:
       print(__lowerCAmelCase ,	''' : ''' ,	''' -> '''.join([str(__lowerCAmelCase			) for j in self.vertices[i]]			)			)

 def 						snake_case_			(						self				:				Dict ,	__lowerCAmelCase				:				int ,	__lowerCAmelCase				:				int			)      ->  Union[str, Any]:
    if from_vertex in self.vertices:
       self.vertices[from_vertex].append(__lowerCAmelCase			)
    else:
       _A						     =					[to_vertex]

 def 						snake_case_			(						self				:				List[Any] ,	__lowerCAmelCase				:				int			)      ->  List[Any]:
    _A						     =					set()

    # create a first in first out queue to store all the vertices for BFS
    _A						     =					Queue()

    # mark the source node as visited and enqueue it
    visited.add(__lowerCAmelCase			)
    queue.put(__lowerCAmelCase			)

    while not queue.empty():
       _A						     =					queue.get()

       # loop through all adjacent vertex and enqueue it if not yet visited
       for adjacent_vertex in self.vertices[vertex]:
          if adjacent_vertex not in visited:
             queue.put(__lowerCAmelCase			)
             visited.add(__lowerCAmelCase			)
    return visited


if __name__ == "__main__":
   from doctest import testmod

   testmod(verbose=True)

   UpperCAmelCase_		   =				Graph()
   g.add_edge(0, 1)
   g.add_edge(0, 2)
   g.add_edge(1, 2)
   g.add_edge(2, 0)
   g.add_edge(2, 3)
   g.add_edge(3, 3)

   g.print_graph()
   # 0  :  1 -> 2
   # 1  :  2
   # 2  :  0 -> 3
   # 3  :  3

   assert sorted(g.bfs(2)) == [0, 1, 2, 3]








import numpy as np
import requests

def   SCREAMING_SNAKE_CASE_       (  )     ->    Any:
   _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(_snake_case							)
   data.pop(0							)  # This is for removing the labels from the list
   _A						     =					np.matrix(_snake_case							)
   return dataset

def   SCREAMING_SNAKE_CASE_       (  _snake_case  :int  ,      _snake_case  :Dict  ,      _snake_case  :Tuple  ,      _snake_case  :Tuple  ,      _snake_case  :str							)     ->    int:
   _A						     =					len_data

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

def   SCREAMING_SNAKE_CASE_       (  _snake_case  :Union[str, Any]  ,      _snake_case  :Any  ,      _snake_case  :Optional[Any]  ,      _snake_case  :List[Any]							)     ->    Tuple:
   _A						     =					np.dot(_snake_case  ,      data_x.transpose()							)
   prod -= data_y.transpose()
   _A						     =					np.sum(np.square(_snake_case							)							)
   _A						     =					sum_elem / (2 * len_data)
   return error

def   SCREAMING_SNAKE_CASE_       (  _snake_case  :Union[str, Any]  ,      _snake_case  :Union[str, Any]							)     ->    List[str]:
   _A						     =					100_000
   _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  ,      _snake_case							):
      _A						     =					run_steep_gradient_descent(_snake_case  ,      _snake_case  ,      _snake_case  ,      _snake_case  ,      _snake_case							)
      _A						     =					sum_of_square_error(_snake_case  ,      _snake_case  ,      _snake_case  ,      _snake_case							)
      print(F'''At Iteration {i + 1} - Error is {error:.5f}'''							)

   return theta

def   SCREAMING_SNAKE_CASE_       (  _snake_case  :List[Any]  ,      _snake_case  :Optional[int]							)     ->    Tuple:
   _A						     =					sum(abs(y - predicted_y[i]							) for i, y in enumerate(_snake_case							)							)
   return total / len(_snake_case							)

def   SCREAMING_SNAKE_CASE_       (  )     ->    Tuple:
   _A						     =					collect_dataset()

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

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


if __name__ == "__main__":
   main()



