


'''simple docstring'''
import math
import random
from typing import Any

from .hill_climbing import SearchProblem


def 			a      (			__a ,					__a = True ,					__a = math.inf ,					__a = -math.inf ,					__a = math.inf ,					__a = -math.inf ,					__a = False ,					__a = 100 ,					__a = 0.0_1 ,					__a = 1 ,					)  ->	Any:



					'''simple docstring'''






					UpperCamelCase__   :Optional[int]												= False
					UpperCamelCase__   :Optional[int]												= search_prob
					UpperCamelCase__   :List[str]												= start_temperate
					UpperCamelCase__   :List[str]												= []
					UpperCamelCase__   :Optional[int]												= 0
					UpperCamelCase__   :Dict												= None

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

					if visualization:
										from matplotlib import pyplot as plt

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


if __name__ == "__main__":


		def 			a      (			__a ,					__a   )  ->	Tuple:



							'''simple docstring'''






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

		# starting the problem with initial coordinates (12, 47)
		__snake_case							      =     SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
		__snake_case							      =     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							      =     SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
		__snake_case							      =     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 			a      (			__a ,					__a   )  ->	Any:



							'''simple docstring'''






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

		__snake_case							      =     SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
		__snake_case							      =     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							      =     SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
		__snake_case							      =     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 			a      (			__a ,					__a   )  ->	int:



					'''simple docstring'''






					if len(__a   ) != len(__a   ):
										raise ValueError('''String lengths must match!'''   )

					UpperCamelCase__   :Union[str, Any]												= 0

					for chara, chara in zip(__a ,					__a   ):
										if chara != chara:
															count += 1

					return count


if __name__ == "__main__":
		import doctest

		doctest.testmod()