





import math
import random
from typing import Any

from .hill_climbing import SearchProblem







def 			A__							(    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:
  __snake_case:						Dict   =						False
  __snake_case:						Optional[Any]   =						search_prob
  __snake_case:						Union[str, Any]   =						start_temperate
  __snake_case:						Dict   =						[]
  __snake_case:						Optional[int]   =						0
  __snake_case:						List[str]   =						None

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

  if visualization:
    from matplotlib import pyplot as plt

    plt.plot(range(__snake_case)					,					__snake_case)
    plt.xlabel("""Iterations""")
    plt.ylabel("""Function values""")
    plt.show()
  return best_state


if __name__ == "__main__":







      def 			A__							(    SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__)   ->    Optional[Any]:
        return (x**2) + (y**2)

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

      __UpperCAmelCase       :   Optional[Any]				       =							SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
      __UpperCAmelCase       :   Optional[Any]				       =							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()}'
      )

      __UpperCAmelCase       :   int				       =							SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
      __UpperCAmelCase       :   Optional[int]				       =							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()}'
      )










UpperCAmelCase__   	=							8.31_44_62  # Unit - J mol-1 K-1
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case						)     ->			float:







 """simple docstring"""


 if moles < 0 or kelvin < 0 or volume < 0:
  raise ValueError('''Invalid inputs. Enter positive value.'''						)
 return moles * kelvin * UNIVERSAL_GAS_CONSTANT / volume
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case						)     ->			float:







 """simple docstring"""


 if moles < 0 or kelvin < 0 or pressure < 0:
  raise ValueError('''Invalid inputs. Enter positive value.'''						)
 return moles * kelvin * UNIVERSAL_GAS_CONSTANT / pressure


if __name__ == "__main__":
  from doctest import testmod

  testmod()

