def get_my_move(self): # -> Tuple[MainBoardCoords, SubBoardCoords]
new_board = parse(self.main_board)
last_turn = self.main_board.sub_board_next_player_must_play
mcts = MCTS(board=new_board,
last_turn=reverse_moves_mapper[(last_turn.row,
last_turn.col)]
if last_turn is not None else None)
sub_board, pos = mcts.run()
return moves_mapper[sub_board], moves_mapper[pos]
def get_my_move(self): # -> Tuple[MainBoardCoords, SubBoardCoords]
current_board = parse(self.main_board)
last_turn = self.main_board.sub_board_next_player_must_play
features = [] # so you can map
all_moves = [] # these two by indexes
if last_turn is None:
moves = self.main_board.get_playable_coords()
for move in moves:
sub_board_parsed = reverse_moves_mapper[(move.row, move.col)]
sub_moves = self.main_board.get_sub_board(
move).get_playable_coords()
for sub_move in sub_moves:
next_board = deepcopy(current_board)
sub_move_parsed = reverse_moves_mapper[(sub_move.row,
sub_move.col)]
next_board[sub_board_parsed][sub_move_parsed] = 1
new_board = np.concatenate(
(current_board.flatten(), next_board.flatten()),
axis=0)
features.append(new_board.reshape((18, 9, 1)))
all_moves.append((move, sub_move))
else:
sub_moves = self.main_board.get_sub_board(
last_turn).get_playable_coords()
sub_board_parsed = reverse_moves_mapper[(last_turn.row,
last_turn.col)]
for sub_move in sub_moves:
sub_move_parsed = reverse_moves_mapper[(sub_move.row,
sub_move.col)]
next_board = deepcopy(current_board)
next_board[sub_board_parsed][sub_move_parsed] = 1
new_board = np.concatenate(
(current_board.flatten(), next_board.flatten()), axis=0)
features.append(new_board.reshape((18, 9, 1)))
all_moves.append((last_turn, sub_move))
features = np.array(features)
scores = self.nn.predict(features)
# print(scores)
max_score_index = 0
max_score = -1 # gonna lose completely
least_o_score = -1
least_o_index = 0
for i, score in enumerate(scores):
if score[0] > max_score:
max_score = score[0]
max_score_index = i
if score[1] > least_o_score:
least_o_score = score[1]
least_o_index = i
if max_score < 0:
move = all_moves[least_o_index]
return move[0], move[1]
else:
move = all_moves[max_score_index]
return move[0], move[1]