def solve(self, starting_board: Board) -> any:
self.open_queue: PriorityQueue = PriorityQueue()
self.open_dict = dict()
self.closed_list = []
self.closed_dict = dict()
start_time = time.time()
current_board = starting_board
current_board.parent = None
h = self.heuristic_func(current_board)
g = current_board.g
current_f = g + h
current_board.h = h
current_board.f = current_f
self.open_queue.put((current_f, current_board))
self.open_dict[current_board] = (g, h)
goal_1 = get_goal_1(rows=starting_board.rows,
columns=starting_board.columns)
goal_2 = get_goal_2(rows=starting_board.rows,
columns=starting_board.columns)
while not (current_board.equals(goal_1)
or current_board.equals(goal_2)):
if (time.time() - start_time) > self.time_out:
return None
current_board = self.search(current_board)
if current_board is None:
return None
current_board.open_length_solved = len(self.open_dict)
current_board.closed_length_solved = len(self.closed_list)
return current_board
def solve(self, starting_board: Board) -> any:
self.open_queue: PriorityQueue = PriorityQueue()
self.open_set = set()
self.closed_list = []
self.closed_set = set()
start_time = time.time()
current_board = starting_board
current_board.parent = None
h = self.heuristic_func(current_board)
current_board.h = h
self.open_queue.put((h, time.time(), current_board))
self.open_set.add(current_board)
# if h(n) are equal, arbitrary selection instead of comparing
# cost (here, we use time as that arbitrary comparator)
goal_1 = get_goal_1(rows=starting_board.rows,
columns=starting_board.columns)
goal_2 = get_goal_2(rows=starting_board.rows,
columns=starting_board.columns)
while not (current_board.equals(goal_1)
or current_board.equals(goal_2)):
if (time.time() - start_time) > self.time_out:
return None
current_board = self.search(current_board)
if current_board is None:
return None
current_board.open_length_solved = len(self.open_set)
current_board.closed_length_solved = len(self.closed_list)
return current_board
def h2(board: Board) -> int:
manhattan_distance_goal_1 = modified_manhattan_distance(
board, get_goal_1(rows=board.rows, columns=board.columns))
manhattan_distance_goal_2 = modified_manhattan_distance(
board, get_goal_2(rows=board.rows, columns=board.columns))
if manhattan_distance_goal_1 <= manhattan_distance_goal_2:
return manhattan_distance_goal_1
return manhattan_distance_goal_2
def h1(board: Board) -> int:
hamming_distance_goal_1 = hamming_distance(
board, get_goal_1(rows=board.rows, columns=board.columns))
hamming_distance_goal_2 = hamming_distance(
board, get_goal_2(rows=board.rows, columns=board.columns))
if hamming_distance_goal_1 <= hamming_distance_goal_2:
return hamming_distance_goal_1
return hamming_distance_goal_2