int(input("Please input the depth you want: "))))
if (game_mode) == 2:
oh.A_star_solve(
manual_scramble(),
int(input("Please input number of steps you want: ")),
int(input("Please input the depth you want: ")))
#input parameters
if (game_mode) == 3:
state = manual_scramble()
scramble_depth = int(
input("\nPlease input the module you want to load: "))
depth = int(input("\nPlease input the number of depth: "))
steps = int(input("\nPlease input the number of steps: "))
node = gr.Node(state, depth, steps)
#import trained nn
net = nn.RubikNet()
net.load_state_dict(tr.load("model_%d.pth" % scramble_depth))
for step in range(steps):
print("Turn %d" % step)
#check goal
if node.match == 27:
print("\nSolve")
print("Path is :")
print(node.path)
#node.state.disp()
break
#check if have goal in the pool of children, in-other word, check the size of heuristic, it has the goal or not
#search if there is a result in the heuristic
r_A_nn = list()
for game in range(num_games):
scramble = gr.scramble(scramble_depth)
#random choice
print("\nCalculate random process game: %d" % game)
r_rand.append(oh.random_process_score(scramble, (depth * steps)))
#r_rand.append(oh.random_process_score(gr.scramble(scramble_depth), (depth*steps)))
#A* choice
print("\nCalculate A* game: %d" % game)
r_A.append(oh.A_star_solve_score(scramble, steps, depth))
#r_A.append(oh.A_star_solve_score(gr.scramble(scramble_depth), steps, depth))
#A*_NN choice
print("\nCalculate A*_NN game: %d" % game)
node = gr.Node(scramble, depth, steps)
#node = gr.Node(gr.scramble(scramble_depth), depth, steps)
for step in range(steps):
#check goal
if node.match == 8:
break
#check if have goal in the pool of children, in-other word, check the size of heuristic, it has the goal or not
#search if there is a result in the heuristic
ind = gr.make_choice(node)
#check goal in last step
if node.children()[ind].match == 8:
node = node.children()[ind]
break
#no goal in the poll of children
#nn_searching