def multi_astar(filename):
maze, start, goals = file_read(filename)
graph = state_representation(maze)
mouse = Mouse(start)
came_from = {start: (None, copy.deepcopy(goals))}
goals_obtained = {start: (None, [])}
open_list = [(start, 0, gs_heuristic(start, came_from[start][1]))]
closed_list = []
count = 0
while open_list:
position = min(open_list, key=lambda t: t[1] + t[2])[0]
position_cost = min(open_list, key=lambda t: t[1] + t[2])[1]
count += 1
if position in came_from[position][1]:
came_from[position][1].remove(position)
goals_obtained[position][1].append(position)
if len(came_from[position][1]) == 0:
if len(goals_obtained[position][1]) < 10:
for i in range(len(maze)):
for j in range(len(maze[i])):
for k in range(len(goals_obtained[position][1])):
if goals_obtained[position][1][k] == (i, j):
maze[i][j] = str(k + 1)
else:
for i in range(len(maze)):
for j in range(len(maze[i])):
for k in range(len(goals_obtained[position][1])):
if goals_obtained[position][1][k] == (i, j):
if k < 10:
maze[i][j] = str(k)
elif k >= 10:
maze[i][j] = chr(k + 87)
elif k >= 36:
maze[i][j] = chr(k + 29)
for i in maze:
print(''.join(i))
print("Total cost of the path: ", position_cost)
print("Total number of nodes explored: ", count)
mouse.position = position
mouse.goals_obtained = goals_obtained[position][1]
return mouse
open_list = [(node, g, h) for (node, g, h) in open_list
if node != position]
closed_list.append((position, came_from[position][1]))
for i in graph.neighbors(position):
if (i, came_from[position][1]) in closed_list:
continue
for j in open_list:
new_g = position_cost + 1
if len(came_from[j[0]][1]) > len(
came_from[position][1]) and j[1] < new_g:
open_list.remove(j)
closed_list.append((j[0], came_from[j[0]][1]))
continue
else:
g_val = position_cost + 1
h_val = gs_heuristic(position, came_from[position][1])
came_from[i] = (position,
copy.deepcopy(came_from[position][1]))
goals_obtained[i] = (position,
copy.deepcopy(
goals_obtained[position][1]))
open_list.append((i, g_val, h_val))
