def _update_prediction_trees(self):
i = 0
for prediction_tree in self.prediction_trees:
car = self.playfield.goldcars[i]
if car.pos is not None:
car_node = ai.AiNode_create(
GoldcarNodeState(car),
TrailNode(car.pos.tile, car.pos.get_in_direction()))
car_node.set_playfield(self.playfield)
car_node.set_other_trees(self._get_other_prediction_trees(car))
if prediction_tree.root_node is None:
prediction_tree.set_root(ai.Node(car_node))
elif prediction_tree.root_node.smartnode.nequals(car_node):
prediction_tree.set_root(ai.Node(car_node))
prediction_tree.root_node.smartnode.set_playfield(
self.playfield)
prediction_tree.root_node.smartnode.carstate = GoldcarNodeState(
car)
prediction_tree.update()
## if prediction_tree.root_node is not None:
## print prediction_tree.root_node._best_score,
i += 1
def bot_take_turn(level):
global SIDE
print("对手思考中……")
root = ai.Node(MAIN_BOARD.board, SIDE, 0)
tree = ai.MinimaxTreeSearch(root)
strategy = tree.next_move(level)
MAIN_BOARD.move(strategy[0], strategy[1], confirm=True)
SIDE = switch_side(SIDE)
def main():
with open(sys.argv[1]) as file:
data = json.load(file)
# TODO: find and print winning action sequence
print_utils.print_board(make_board_dict(data))
# make the initial state and the initial node
init_state = ai.State(make_state_dict(data, WHITE_PIECE), make_state_dict(data, BLACK_PIECE))
init_node = ai.Node(init_state)
# print the winning move sequence
print(ai.get_winning_sequence(init_node))
def main():
OUTPUT_FILE = "out/{}x{}_{}gems.csv".format(WIDTH, HEIGHT, len(GEMS))
AGENTS = ORIG_AGENTS.copy()
AGENT = AGENTS[0]
AGENTS.pop(0)
BOARD = [[random.choice(GEMS) for c in range(WIDTH)]
for r in range(HEIGHT)]
SCORE = MOVES = GEMS_CLEARED = CASCADES = 0
# clear matches
while get_matches(BOARD) != []:
for match in get_matches(BOARD):
for gem in match:
BOARD[gem[1]][gem[0]] = random.choice(GEMS)
# game loop
while True:
if PRINT_BOARDS:
print("\nscore: ", SCORE)
print_board(BOARD)
if not get_valid_swaps(BOARD):
print("GAME OVER")
print_board(BOARD)
print("agent: ", AGENT)
print("final score: ", SCORE)
print("moves: ", MOVES)
print("gems cleared: ", GEMS_CLEARED)
print("cascades: ", CASCADES)
gpm = float(GEMS_CLEARED) / float(MOVES)
print("avg gems/move: ", gpm)
spm = float(SCORE) / float(MOVES)
print("avg score/move: ", spm)
if OUTPUT_TO_FILE:
with open(OUTPUT_FILE, mode='a') as f:
f.write("{},{},{},{},{},{},{}\n".format(
AGENT, SCORE, MOVES, GEMS_CLEARED, CASCADES, gpm, spm))
if not AGENTS:
exit()
else:
AGENT = AGENTS[0]
AGENTS.pop(0)
BOARD = [[random.choice(GEMS) for c in range(WIDTH)]
for r in range(HEIGHT)]
SCORE = MOVES = GEMS_CLEARED = CASCADES = 0
if USER_INPUT:
print("enter pairs to swap: x1 y1 x2 y2")
coords = [int(val) for val in input().split(" ")]
else:
current_node = ai.Node(BOARD, SCORE, 0)
coords = current_node.get_next_swap(AGENT)
MOVES += 1
# make swap on prospective next board
next_board = copy.deepcopy(BOARD)
swap_gems(next_board, coords[0], coords[1], coords[2], coords[3])
# check that match is valid
matches = get_matches(next_board)
if matches == []: continue
BOARD = next_board
# track if we are cascading
settled_once = False
# clear matches and increment score until settled
while get_matches(BOARD) != []:
if settled_once: CASCADES += 1
for match in get_matches(BOARD):
for gem in match:
SCORE += (10 + (len(match) - 3) * 10)
BOARD[gem[1]][gem[0]] = " "
GEMS_CLEARED += 1
settled_once = True
drop_and_fill(BOARD)
def build_game_tree(self, depth=-1):
node = ai.Node()