import math
import random
from typing import Any

from .hill_climbing import SearchProblem



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE = True  ,      SCREAMING_SNAKE_CASE = math.inf  ,      SCREAMING_SNAKE_CASE = -math.inf  ,      SCREAMING_SNAKE_CASE = math.inf  ,      SCREAMING_SNAKE_CASE = -math.inf  ,      SCREAMING_SNAKE_CASE = False  ,      SCREAMING_SNAKE_CASE = 100  ,      SCREAMING_SNAKE_CASE = 0.01  ,      SCREAMING_SNAKE_CASE = 1  ,      ):
    '''simple docstring'''







    __UpperCamelCase					:Optional[int]        =		False
    __UpperCamelCase					:Tuple        =		search_prob
    __UpperCamelCase					:int        =		start_temperate
    __UpperCamelCase					:Any        =		[]
    __UpperCamelCase					:Union[str, Any]        =		0
    __UpperCamelCase					:Tuple        =		None

    while not search_end:
        __UpperCamelCase					:Any        =		current_state.score()
        if best_state is None or current_score > best_state.score():
            __UpperCamelCase					:str        =		current_state
        scores.append(SCREAMING_SNAKE_CASE     )
        iterations += 1
        __UpperCamelCase					:Dict        =		None
        __UpperCamelCase					:Union[str, Any]        =		current_state.get_neighbors()
        while (
            next_state is None and neighbors
        ):  # till we do not find a neighbor that we can move to
            __UpperCamelCase					:Optional[int]        =		random.randint(0  ,      len(SCREAMING_SNAKE_CASE     ) - 1     )  # picking a random neighbor
            __UpperCamelCase					:Optional[Any]        =		neighbors.pop(SCREAMING_SNAKE_CASE     )
            __UpperCamelCase					:List[Any]        =		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:
                __UpperCamelCase					:Any        =		change * -1  # in case we are finding minimum
            if change > 0:  # improves the solution
                __UpperCamelCase					:Dict        =		picked_neighbor
            else:
                __UpperCamelCase					:Tuple        =		(math.e) ** (
                    change / current_temp
                )  # probability generation function
                if random.random() < probability:  # random number within probability
                    __UpperCamelCase					:Dict        =		picked_neighbor
        __UpperCamelCase					:Tuple        =		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
            __UpperCamelCase					:Tuple        =		True
        else:
            __UpperCamelCase					:int        =		next_state

    if visualization:
        from matplotlib import pyplot as plt

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


if __name__ == "__main__":



    def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
        '''simple docstring'''







        return (x**2) + (y**2)

    # starting the problem with initial coordinates (12, 47)
    __lowercase										= SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
    __lowercase										= 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)
    __lowercase										= SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
    __lowercase										= 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 lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
        '''simple docstring'''







        return (3 * x**2) - (6 * y)

    __lowercase										= SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
    __lowercase										= 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()}'
    )

    __lowercase										= SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
    __lowercase										= 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 lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    print(f"""Vertex\tShortest Distance from vertex {src}"""     )
    for i, d in enumerate(SCREAMING_SNAKE_CASE     ):
        print(f"""{i}\t\t{d}"""     )



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    for j in range(SCREAMING_SNAKE_CASE     ):
        __UpperCamelCase  ,	__UpperCamelCase  ,	__UpperCamelCase					:int        =		(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 lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    __UpperCamelCase					:List[Any]        =		[float('''inf'''     )] * vertex_count
    __UpperCamelCase					:str        =		0.0

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

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

    __UpperCamelCase					:Tuple        =		check_negative_cycle(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     )
    if negative_cycle_exists:
        raise Exception('''Negative cycle found'''     )

    return distance


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    __lowercase										= int(input('''Enter number of vertices: ''').strip())
    __lowercase										= int(input('''Enter number of edges: ''').strip())

    __lowercase										= [{} for _ in range(E)]

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

    __lowercase										= int(input('''\nEnter shortest path source:''').strip())
    __lowercase										= bellman_ford(graph, V, E, source)
    print_distance(shortest_distance, 0)





