
"""simple docstring"""




import math
import random
from typing import Any

from .hill_climbing import SearchProblem



def     __magic_name__		(					UpperCamelCase     :		Optional[int]							,		UpperCamelCase     :		bool = True							,		UpperCamelCase     :		float = math.inf							,		UpperCamelCase     :		float = -math.inf							,		UpperCamelCase     :		float = math.inf							,		UpperCamelCase     :		float = -math.inf							,		UpperCamelCase     :		bool = False							,		UpperCamelCase     :		float = 100							,		UpperCamelCase     :		float = 0.01							,		UpperCamelCase     :		float = 1							,		)     ->						Any:
    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(UpperCamelCase     )
        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(UpperCamelCase     ) - 1     )  # picking a random neighbor
            a__		=			neighbors.pop(UpperCamelCase     )
            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(UpperCamelCase     )							,		UpperCamelCase     )
        plt.xlabel('Iterations'     )
        plt.ylabel('Function values'     )
        plt.show()
    return best_state


if __name__ == "__main__":



    def     __magic_name__		(					UpperCamelCase     :		Optional[int]							,		UpperCamelCase     :		Tuple     )     ->						int:
        return (x**2) + (y**2)

    # starting the problem with initial coordinates (12, 47)
    a		:       Optional[Any]     						=       SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
    a		:       int     						=       simulated_annealing(
        prob, find_max=False, max_x=1_00, 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		:       str     						=       SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
    a		:       Union[str, Any]     						=       simulated_annealing(
        prob, find_max=True, max_x=1_00, 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     __magic_name__		(					UpperCamelCase     :		Optional[int]							,		UpperCamelCase     :		int     )     ->						Dict:
        return (3 * x**2) - (6 * y)

    a		:       int     						=       SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
    a		:       int     						=       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		:       Any     						=       SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
    a		:       Dict     						=       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"""




import operator



def     __magic_name__		(					UpperCamelCase     :		list							,		UpperCamelCase     :		bool = False							,		UpperCamelCase     :		list | None = None     )     ->						list:
    a__		=			operator.lt if reverse else operator.gt
    a__		=			solution or []

    if not arr:
        return solution

    a__		=			[arr.pop(0     )]
    for i, item in enumerate(UpperCamelCase     ):
        if _operator(UpperCamelCase							,		sublist[-1]     ):
            sublist.append(UpperCamelCase     )
            arr.pop(UpperCamelCase     )

    #  merging sublist into solution list
    if not solution:
        solution.extend(UpperCamelCase     )
    else:
        while sublist:
            a__		=			sublist.pop(0     )
            for i, xx in enumerate(UpperCamelCase     ):
                if not _operator(UpperCamelCase							,		UpperCamelCase     ):
                    solution.insert(UpperCamelCase							,		UpperCamelCase     )
                    break
            else:
                solution.append(UpperCamelCase     )

    strand_sort(UpperCamelCase							,		UpperCamelCase							,		UpperCamelCase     )
    return solution


if __name__ == "__main__":
    assert strand_sort([4, 3, 5, 1, 2]) == [1, 2, 3, 4, 5]
    assert strand_sort([4, 3, 5, 1, 2], reverse=True) == [5, 4, 3, 2, 1]


