




import math
import random
from typing import Any

from .hill_climbing import SearchProblem







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_ = True				,       lowercase_ = math.inf				,       lowercase_ = -math.inf				,       lowercase_ = math.inf				,       lowercase_ = -math.inf				,       lowercase_ = False				,       lowercase_ = 100				,       lowercase_ = 0.01				,       lowercase_ = 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(lowercase_		)
              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(lowercase_		) - 1		)  # picking a random neighbor
                     A__   				=					neighbors.pop(lowercase_		)
                     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(lowercase_		)				,       lowercase_		)
              plt.xlabel('''Iterations'''		)
              plt.ylabel('''Function values'''		)
              plt.show()
       return best_state


if __name__ == "__main__":







  def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_		)     -> Any:



         """simple docstring"""

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

  # starting the problem with initial coordinates (12, 47)
  _lowerCamelCase			:	str							 =     SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
  _lowerCamelCase			:	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)
  _lowerCamelCase			:	str							 =     SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
  _lowerCamelCase			:	Dict							 =     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 SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_		)     -> Optional[Any]:



         """simple docstring"""

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

  _lowerCamelCase			:	int							 =     SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
  _lowerCamelCase			:	Union[str, 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()}'''
  )

  _lowerCamelCase			:	Union[str, Any]							 =     SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
  _lowerCamelCase			:	Tuple							 =     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 __future__ import annotations

import numpy as np







def SCREAMING_SNAKE_CASE       ( lowercase_		)     -> List[Any]:



       """simple docstring"""

       return np.maximum(0				,       lowercase_		)


if __name__ == "__main__":
  print(np.array(relu([-1, 0, 5])))  # --> [0, 0, 5]


