





"""simple docstring"""



import math
import random
from typing import Any

from .hill_climbing import SearchProblem







def 						__lowercase	(  snake_case_    :    Optional[Any]							,snake_case_    :    bool = True							,snake_case_    :    float = math.inf							,snake_case_    :    float = -math.inf							,snake_case_    :    float = math.inf							,snake_case_    :    float = -math.inf							,snake_case_    :    bool = False							,snake_case_    :    float = 100							,snake_case_    :    float = 0.01							,snake_case_    :    float = 1							,)  ->Any:



    '''simple docstring'''







    __A       :		int							=	False
    __A       :		int							=	search_prob
    __A       :		List[str]							=	start_temperate
    __A       :		List[Any]							=	[]
    __A       :		Optional[Any]							=	0
    __A       :		Optional[Any]							=	None

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

    if visualization:
        from matplotlib import pyplot as plt

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


if __name__ == "__main__":







       def 						__lowercase	(  snake_case_    :    Any							,snake_case_    :    Optional[int]		)  ->Tuple:



           '''simple docstring'''







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

       # starting the problem with initial coordinates (12, 47)
       a_					=     SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
       a_					=     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)
       a_					=     SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
       a_					=     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 						__lowercase	(  snake_case_    :    Union[str, Any]							,snake_case_    :    List[str]		)  ->Union[str, Any]:



           '''simple docstring'''







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

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

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











"""simple docstring"""



def 						__lowercase	(  snake_case_    :    list		)  ->float:



    '''simple docstring'''







    __A       :		Tuple							=	0
    while len(snake_case_		) > 1:
        __A       :		List[Any]							=	0
        # Consider two files with minimum cost to be merged
        for _ in range(2		):
            __A       :		Dict							=	files.index(min(snake_case_		)		)
            temp += files[min_index]
            files.pop(snake_case_		)
        files.append(snake_case_		)
        optimal_merge_cost += temp
    return optimal_merge_cost


if __name__ == "__main__":
       import doctest

       doctest.testmod()




