

import math
import random
from typing import Any

from .hill_climbing import SearchProblem


def       __UpperCamelCase     (lowerCAmelCase       :	Union[str, Any],     lowerCAmelCase       :	List[str] = True,     lowerCAmelCase       :	str = math.inf,     lowerCAmelCase       :	Optional[Any] = -math.inf,     lowerCAmelCase       :	Optional[int] = math.inf,     lowerCAmelCase       :	str = -math.inf,     lowerCAmelCase       :	int = False,     lowerCAmelCase       :	Any = 100,     lowerCAmelCase       :	Tuple = 0.01,     lowerCAmelCase       :	Optional[int] = 1,     )  ->	int:
      A 			=					False
      A 			=					search_prob
      A 			=					start_temperate
      A 			=					[]
      A 			=					0
      A 			=					None

      while not search_end:
            A 			=					current_state.score()
            if best_state is None or current_score > best_state.score():
                  A 			=					current_state
            scores.append(lowerCAmelCase  )
            iterations += 1
            A 			=					None
            A 			=					current_state.get_neighbors()
            while (
                next_state is None and neighbors
            ):  # till we do not find a neighbor that we can move to
                  A 			=					random.randint(0,     len(lowerCAmelCase  ) - 1  )  # picking a random neighbor
                  A 			=					neighbors.pop(lowerCAmelCase  )
                  A 			=					picked_neighbor.score() - current_score

                  if (
                      picked_neighbor.x > max_x
                      or picked_neighbor.x < min_x
                      or picked_neighbor.y > max_y
                      or picked_neighbor.y < min_y
                  ):
                        continue  # neighbor outside our bounds

                  if not find_max:
                        A 			=					change * -1  # in case we are finding minimum
                  if change > 0:  # improves the solution
                        A 			=					picked_neighbor
                  else:
                        A 			=					(math.e) ** (
                            change / current_temp
                        )  # probability generation function
                        if random.random() < probability:  # random number within probability
                              A 			=					picked_neighbor
            A 			=					current_temp - (current_temp * rate_of_decrease)

            if current_temp < threshold_temp or next_state is None:
                  # temperature below threshold, or could not find a suitable neighbor
                  A 			=					True
            else:
                  A 			=					next_state

      if visualization:
            from matplotlib import pyplot as plt

            plt.plot(range(lowerCAmelCase  ),     lowerCAmelCase  )
            plt.xlabel('Iterations'  )
            plt.ylabel('Function values'  )
            plt.show()
      return best_state


if __name__ == "__main__":


  def       __UpperCamelCase     (lowerCAmelCase       :	Tuple,     lowerCAmelCase       :	Any  )  ->	str:
        return (x**2) + (y**2)

  # starting the problem with initial coordinates (12, 47)
  _UpperCAmelCase     						=      SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
  _UpperCAmelCase     						=      simulated_annealing(
      prob, find_max=False, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
  )
  print(
      "The minimum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 "
      F'''and 50 > y > - 5 found via hill climbing: {local_min.score()}'''
  )

  # starting the problem with initial coordinates (12, 47)
  _UpperCAmelCase     						=      SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
  _UpperCAmelCase     						=      simulated_annealing(
      prob, find_max=True, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
  )
  print(
      "The maximum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 "
      F'''and 50 > y > - 5 found via hill climbing: {local_min.score()}'''
  )


  def       __UpperCamelCase     (lowerCAmelCase       :	Optional[Any],     lowerCAmelCase       :	int  )  ->	List[Any]:
        return (3 * x**2) - (6 * y)

  _UpperCAmelCase     						=      SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
  _UpperCAmelCase     						=      simulated_annealing(prob, find_max=False, visualization=True)
  print(
      "The minimum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: "
      F'''{local_min.score()}'''
  )

  _UpperCAmelCase     						=      SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
  _UpperCAmelCase     						=      simulated_annealing(prob, find_max=True, visualization=True)







  print(
      "The maximum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: "
      F'''{local_min.score()}'''
  )











from __future__ import annotations

def   __lowercase       (			snake_case,	snake_case			):







				"""simple docstring"""






				print(f'''Vertex\tShortest Distance from vertex {src}'''			)
				for i, d in enumerate(snake_case			):
								print(f'''{i}\t\t{d}'''			)

def   __lowercase       (			snake_case,	snake_case,	snake_case			):







				"""simple docstring"""






				for j in range(snake_case			):
								__magic_name__  ,					__magic_name__  ,					__magic_name__		:Tuple   =   (graph[j][k] for k in ['''src''', '''dst''', '''weight'''])
								if distance[u] != float('''inf'''			) and distance[u] + w < distance[v]:
												return True
				return False

def   __lowercase       (			snake_case,	snake_case,	snake_case,	snake_case			):







				"""simple docstring"""






				__magic_name__		:List[Any]   =   [float('''inf'''			)] * vertex_count
				__magic_name__		:Tuple   =   0.0

				for _ in range(vertex_count - 1			):
								for j in range(snake_case			):
												__magic_name__  ,					__magic_name__  ,					__magic_name__		:Dict   =   (graph[j][k] for k in ['''src''', '''dst''', '''weight'''])

												if distance[u] != float('''inf'''			) and distance[u] + w < distance[v]:
																__magic_name__		:Tuple   =   distance[u] + w

				__magic_name__		:Tuple   =   check_negative_cycle(snake_case,	snake_case,	snake_case			)
				if negative_cycle_exists:
								raise Exception('''Negative cycle found'''			)

				return distance


if __name__ == "__main__":
					import doctest

					doctest.testmod()

					SCREAMING_SNAKE_CASE__   :      Tuple				=		int(input("""Enter number of vertices: """).strip())
					SCREAMING_SNAKE_CASE__   :      Any				=		int(input("""Enter number of edges: """).strip())

					SCREAMING_SNAKE_CASE__   :      list[dict[str, int]]				=		[{} for _ in range(E)]

					for i in range(E):
										print("""Edge """, i + 1)
										SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__   :      Dict				=		(
										    int(x)
										    for x in input("""Enter source, destination, weight: """).strip().split(""" """)
										)
										SCREAMING_SNAKE_CASE__   :      Dict				=		{"""src""": src, """dst""": dest, """weight""": weight}

					SCREAMING_SNAKE_CASE__   :      List[Any]				=		int(input("""\nEnter shortest path source:""").strip())
					SCREAMING_SNAKE_CASE__   :      List[str]				=		bellman_ford(graph, V, E, source)
					print_distance(shortest_distance, 0)






