


'''simple docstring'''







from __future__ import annotations




def        __lowercase						(      __SCREAMING_SNAKE_CASE		)				->					bool:
					"""simple docstring"""




					return len(set(a__		)		) == len(a__		)


if __name__ == "__main__":
	import doctest

	doctest.testmod()










'''simple docstring'''







import math
import random
from typing import Any

from .hill_climbing import SearchProblem




def        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE = True				,  __SCREAMING_SNAKE_CASE = math.inf				,  __SCREAMING_SNAKE_CASE = -math.inf				,  __SCREAMING_SNAKE_CASE = math.inf				,  __SCREAMING_SNAKE_CASE = -math.inf				,  __SCREAMING_SNAKE_CASE = False				,  __SCREAMING_SNAKE_CASE = 100				,  __SCREAMING_SNAKE_CASE = 0.01				,  __SCREAMING_SNAKE_CASE = 1				,  )				->					Any:
     """simple docstring"""




     __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(__SCREAMING_SNAKE_CASE		)
          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(__SCREAMING_SNAKE_CASE		) - 1		)  # picking a random neighbor
               __a       =   neighbors.pop(__SCREAMING_SNAKE_CASE		)
               __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(__SCREAMING_SNAKE_CASE		)				,  __SCREAMING_SNAKE_CASE		)
          plt.xlabel("""Iterations"""		)
          plt.ylabel("""Function values"""		)
          plt.show()
     return best_state


if __name__ == "__main__":




 def        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)				->					int:
      """simple docstring"""




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

 # starting the problem with initial coordinates (12, 47)
 SCREAMING_SNAKE_CASE_      =			SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
 SCREAMING_SNAKE_CASE_      =			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)
 SCREAMING_SNAKE_CASE_      =			SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
 SCREAMING_SNAKE_CASE_      =			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        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)				->					int:
      """simple docstring"""




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

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

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






