


import math
import random
from typing import Any

from .hill_climbing import SearchProblem

def 	_a     ( SCREAMING_SNAKE_CASE		: Union[str, Any]			,       SCREAMING_SNAKE_CASE		: Union[str, Any] = True			,       SCREAMING_SNAKE_CASE		: Any = math.inf			,       SCREAMING_SNAKE_CASE		: str = -math.inf			,       SCREAMING_SNAKE_CASE		: str = math.inf			,       SCREAMING_SNAKE_CASE		: Any = -math.inf			,       SCREAMING_SNAKE_CASE		: str = False			,       SCREAMING_SNAKE_CASE		: Optional[int] = 100			,       SCREAMING_SNAKE_CASE		: List[str] = 0.01			,       SCREAMING_SNAKE_CASE		: Union[str, Any] = 1			,       ):







 """simple docstring"""



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

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







        """simple docstring"""



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

       # 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=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				:				Optional[int]  				= 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		: Optional[int]			,       SCREAMING_SNAKE_CASE		: Union[str, Any]   ):







        """simple docstring"""



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

       __UpperCamelCase				:				Dict  				= 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				:				Optional[Any]  				= SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
       __UpperCamelCase				:				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()}"
       )



'''simple docstring'''

def 			__UpperCamelCase       (   UpperCAmelCase = 1		,     UpperCAmelCase = 1000     ):
 lowercase__				:    Dict  =					1
 lowercase__				:    Dict  =					0

 for divide_by_number in range(UpperCAmelCase		,     digit + 1     ):
  lowercase__				:    list[int]  =					[]
  lowercase__				:    Union[str, Any]  =					numerator
  for _ in range(1		,     digit + 1     ):
   if now_divide in has_been_divided:
    if longest_list_length < len(UpperCAmelCase     ):
     lowercase__				:    Dict  =					len(UpperCAmelCase     )
     lowercase__				:    Optional[Any]  =					divide_by_number
   else:
    has_been_divided.append(UpperCAmelCase     )
    lowercase__				:    int  =					now_divide * 10 % divide_by_number

 return the_digit


# Tests
if __name__ == "__main__":
  import doctest

  doctest.testmod()

