



import math
import random
from typing import Any

from .hill_climbing import SearchProblem





def      __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   , ):
    __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      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase				):
     return (x**2) + (y**2)

 # starting the problem with initial coordinates (12, 47)
 __snake_case						:List[str]      		=     SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
 __snake_case						:int      		=     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)
 __snake_case						:str      		=     SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
 __snake_case						:Any      		=     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      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase				):
     return (3 * x**2) - (6 * y)

 __snake_case						:List[Any]      		=     SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
 __snake_case						:List[str]      		=     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()}'
 )

 __snake_case						:str      		=     SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
 __snake_case						:Union[str, Any]      		=     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 math import asin, atan, cos, radians, sin, sqrt, tan

A__	:			Optional[int]			   = 637_8137.0
A__	:			List[str]			   = 635_6752.31_4245
A__	:			Union[str, Any]			   = 6_37_81_37






def   a			(						lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_				):






				'''simple docstring'''
				lowercase__    =			(AXIS_A - AXIS_B) / AXIS_A
				lowercase__    =			atan((1 - flattening) * tan(radians(lowerCamelCase_				)				)				)
				lowercase__    =			atan((1 - flattening) * tan(radians(lowerCamelCase_				)				)				)
				lowercase__    =			radians(lowerCamelCase_				)
				lowercase__    =			radians(lowerCamelCase_				)
				# Equation
				lowercase__    =			sin((phi_a - phi_a) / 2				)
				lowercase__    =			sin((lambda_a - lambda_a) / 2				)
				# Square both values
				sin_sq_phi *= sin_sq_phi
				sin_sq_lambda *= sin_sq_lambda
				lowercase__    =			sqrt(sin_sq_phi + (cos(lowerCamelCase_				) * cos(lowerCamelCase_				) * sin_sq_lambda)				)
				return 2 * RADIUS * asin(lowerCamelCase_				)


if __name__ == "__main__":
							import doctest

							doctest.testmod()




