



'''simple docstring'''





import math
import random
from typing import Any

from .hill_climbing import SearchProblem
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase = True							,    UpperCamelCase = math.inf							,    UpperCamelCase = -math.inf							,    UpperCamelCase = math.inf							,    UpperCamelCase = -math.inf							,    UpperCamelCase = False							,    UpperCamelCase = 100							,    UpperCamelCase = 0.01							,    UpperCamelCase = 1							,    ):



    """simple docstring"""





    lowerCAmelCase__ :  Dict							      =					False
    lowerCAmelCase__ :  str							      =					search_prob
    lowerCAmelCase__ :  str							      =					start_temperate
    lowerCAmelCase__ :  Tuple							      =					[]
    lowerCAmelCase__ :  str							      =					0
    lowerCAmelCase__ :  List[str]							      =					None

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



     """simple docstring"""





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

 # starting the problem with initial coordinates (12, 47)
 _lowerCAmelCase      						=	SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
 _lowerCAmelCase      						=	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)
 _lowerCAmelCase      						=	SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
 _lowerCAmelCase      						=	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    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase			):



     """simple docstring"""





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

 _lowerCAmelCase      						=	SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
 _lowerCAmelCase      						=	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()}"""
 )

 _lowerCAmelCase      						=	SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
 _lowerCAmelCase      						=	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()}"""
 )










import numpy as np







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase			) ->	np.array:
     return 1 / (1 + np.exp(-vector			))







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase			) ->	np.array:
     return vector * sigmoid(1.7_0_2 * vector			)


if __name__ == "__main__":
 import doctest

 doctest.testmod()