





from random import randint, random


def     lowerCamelCase_	(      lowerCAmelCase:	int    ,							lowerCAmelCase:	int    ,							lowerCAmelCase:	int    ,							lowerCAmelCase:	bool = False    ,							lowerCAmelCase:	bool = False    ,							lowerCAmelCase:	int = 5    ,							)->		list:
  _snake_case   :				Dict														=	[[-1] * number_of_cells]  # Create a highway without any car
  _snake_case   :				List[str]														=	0
  _snake_case   :				List[str]														=	max(lowerCAmelCase    ,							0				)
  while i < number_of_cells:
    _snake_case   :				Optional[Any]														=	(
        randint(0    ,							lowerCAmelCase				) if random_speed else initial_speed
    )  # Place the cars
    i += (
        randint(1    ,							max_speed * 2				) if random_frequency else frequency
    )  # Arbitrary number, may need tuning
  return highway


def     lowerCamelCase_	(      lowerCAmelCase:	list    ,							lowerCAmelCase:	int				)->		int:
  _snake_case   :				Dict														=	0
  _snake_case   :				Optional[Any]														=	highway_now[car_index + 1 :]
  for cell in range(len(lowerCAmelCase				)				):  # May need a better name for this
    if cells[cell] != -1:  # If the cell is not empty then
      return distance  # we have the distance we wanted
    distance += 1
  # Here if the car is near the end of the highway
  return distance + get_distance(lowerCAmelCase    ,							-1				)


def     lowerCamelCase_	(      lowerCAmelCase:	list    ,							lowerCAmelCase:	float    ,							lowerCAmelCase:	int				)->		list:
  _snake_case   :				List[Any]														=	len(lowerCAmelCase				)
  # Beforce calculations, the highway is empty
  _snake_case   :				List[Any]														=	[-1] * number_of_cells

  for car_index in range(lowerCAmelCase				):
    if highway_now[car_index] != -1:
      # Add 1 to the current speed of the car and cap the speed
      _snake_case   :				int														=	min(highway_now[car_index] + 1    ,							lowerCAmelCase				)
      # Number of empty cell before the next car
      _snake_case   :				Tuple														=	get_distance(lowerCAmelCase    ,							lowerCAmelCase				) - 1
      # We can't have the car causing an accident
      _snake_case   :				Union[str, Any]														=	min(next_highway[car_index]    ,							lowerCAmelCase				)
      if random() < probability:
        # Randomly, a driver will slow down
        _snake_case   :				int														=	max(next_highway[car_index] - 1    ,							0				)
  return next_highway


def     lowerCamelCase_	(      lowerCAmelCase:	list    ,							lowerCAmelCase:	int    ,							lowerCAmelCase:	float    ,							lowerCAmelCase:	int				)->		list:
  _snake_case   :				Dict														=	len(highway[0]				)

  for i in range(lowerCAmelCase				):
    _snake_case   :				Any														=	update(highway[i]    ,							lowerCAmelCase    ,							lowerCAmelCase				)
    _snake_case   :				Tuple														=	[-1] * number_of_cells

    for car_index in range(lowerCAmelCase				):
      _snake_case   :				Union[str, Any]														=	next_speeds_calculated[car_index]
      if speed != -1:
        # Change the position based on the speed (with % to create the loop)
        _snake_case   :				Union[str, Any]														=	(car_index + speed) % number_of_cells
        # Commit the change of position
        _snake_case   :				Tuple														=	speed
    highway.append(lowerCAmelCase				)

  return highway


if __name__ == "__main__":
      import doctest

      doctest.testmod()











import math
import random
from typing import Any

from .hill_climbing import SearchProblem


def     lowerCamelCase_	(      lowerCAmelCase:	Tuple    ,							lowerCAmelCase:	bool = True    ,							lowerCAmelCase:	float = math.inf    ,							lowerCAmelCase:	float = -math.inf    ,							lowerCAmelCase:	float = math.inf    ,							lowerCAmelCase:	float = -math.inf    ,							lowerCAmelCase:	bool = False    ,							lowerCAmelCase:	float = 1_00    ,							lowerCAmelCase:	float = 0.0_1    ,							lowerCAmelCase:	float = 1    ,							)->		Any:
  _snake_case   :				int														=	False
  _snake_case   :				Any														=	search_prob
  _snake_case   :				Tuple														=	start_temperate
  _snake_case   :				Any														=	[]
  _snake_case   :				List[str]														=	0
  _snake_case   :				Optional[Any]														=	None

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

  if visualization:
    from matplotlib import pyplot as plt

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


if __name__ == "__main__":


      def     lowerCamelCase_	(      lowerCAmelCase:	Any    ,							lowerCAmelCase:	List[Any]				)->		List[Any]:
        return (x**2) + (y**2)

      # starting the problem with initial coordinates (12, 47)
      lowerCAmelCase_						   =    SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
      lowerCAmelCase_						   =    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_						   =    SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
      lowerCAmelCase_						   =    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     lowerCamelCase_	(      lowerCAmelCase:	Any    ,							lowerCAmelCase:	Dict				)->		Dict:
        return (3 * x**2) - (6 * y)

      lowerCAmelCase_						   =    SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
      lowerCAmelCase_						   =    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_						   =    SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
      lowerCAmelCase_						   =    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()}"""
      )




