class Play(object):
def __init__(self):
# global bp, wp, board, black_prompt, white_prompt, features, labels, reversi_model # use occupied to update board history
# json style to input
black_prompt = '{"requests":[{"x":-1,"y":-1}],"responses":[]}'
white_prompt = '{"requests":[],"responses":[]}'
self.bp = json.loads(black_prompt)
self.wp = json.loads(white_prompt)
self.board = Board()
def play(self, turn):
"""the black side first to place a disc, add output to black_prompt responses and to white_prompt requests, vice versa"""
# global black_prompt, white_prompt, res, pi, board
if turn % 2 == 0:
prompt, requests_add, responses_add, color_to_play = self.bp, self.bp, self.wp, BLACK
print("pure")
res = pure_MCTS.UCTAlg(json=prompt).run(time_limit=1)
else:
prompt, requests_add, responses_add, color_to_play = self.wp, self.wp, self.bp, WHITE
print("alpha")
res = mcts.uctAlg.UCTAlg(predict_model=player,
json=prompt,
mode='comp').run(time_limit=1)[0]
print(res)
self.board.disc_place(color_to_play, res[0],
res[1]) # record steps to board
dct = {'x': res[0], 'y': res[1]}
requests_add["responses"].append(dct)
responses_add["requests"].append(dct)
class Play(object):
def __init__(self):
# global bp, wp, board, black_prompt, white_prompt, features, labels, reversi_model # use occupied to update board history
# json style to input
black_prompt = '{"requests":[{"x":-1,"y":-1}],"responses":[]}'
white_prompt = '{"requests":[],"responses":[]}'
self.bp = json.loads(black_prompt)
self.wp = json.loads(white_prompt)
self.board = Board()
self.labels = np.reshape(
[], (-1, BOARD_SIZE**2)) # one turn has (8,8,3) -> 64 * 3
self.features = np.reshape(
[], (-1, BOARD_SIZE**2 * channel)) # one turn has 64 + 1
# all_objects = muppy.get_objects()
# sum1 = summary.summarize(all_objects)
# summary.print_(sum1)
def record_steps(self, color_to_play):
"""record board state in selfplay.py, which maintains a Board instance"""
self.calc_features(
color_to_play) # produce input data before change state
def record_possibilities(self, pi):
self.labels = np.vstack((self.labels, pi))
if DEBUG:
print('the labels...', self.labels.shape)
def add_winner_and_writeIO(self, winner, turn):
"""1 if winner is current player which can be calculated by turn else 0 for tie or -1 for lost"""
# transpose and rotate
self.transform_history_matrix()
winner_stack = []
# turn = labels.shape[0]
# winner = board.judge()
for i in range(turn):
current_player = BLACK if i % 2 == 0 else WHITE
player_reward = 1 if current_player == winner else (
0 if winner == TIE else -1)
winner_stack.append(player_reward)
winner_stack = np.asarray(winner_stack).reshape((turn, 1))
x = winner_stack
for i in range(7): # because of having added the transformed matrix
winner_stack = np.vstack((winner_stack, x))
print(self.labels.shape, winner_stack.shape)
self.labels = np.hstack((self.labels, winner_stack))
if DEBUG:
print('label shape...', self.labels.shape)
print('final labels...', self.labels)
data_lock.acquire()
try:
with open(label_path, "a+") as f:
np.savetxt(f, self.labels, fmt='%f')
with open(features_path, "a+") as f:
np.savetxt(f, self.features,
fmt='%i') # (tup) can save the array row wise
except Exception as e:
print(e)
finally:
data_lock.release()
def play(self, player1, player2, turn, p1isp2):
"""the black side first to place a disc, add output to black_prompt responses and to white_prompt requests, vice versa"""
# global black_prompt, white_prompt, res, pi, board
if turn % 2 == 0:
prompt, requests_add, responses_add, color_to_play = self.bp, self.bp, self.wp, BLACK
current2play = player1
else:
prompt, requests_add, responses_add, color_to_play = self.wp, self.wp, self.bp, WHITE
current2play = player2
if p1isp2: # self play, use stochastic policy
results = UCTAlg(predict_model=current2play,
json=prompt,
mode='stoch').run(time_limit=selfplay_timelimit)
else: # eval, use deterministic policy
results = UCTAlg(predict_model=current2play,
json=prompt,
mode='comp').run(time_limit=eval_timelimit)
res, pi = results[0], results[1] # (3, 2)
if p1isp2:
self.record_steps(color_to_play)
self.record_possibilities(pi)
self.board.disc_place(color_to_play, res[0],
res[1]) # record steps to board
dct = {'x': res[0], 'y': res[1]}
requests_add["responses"].append(dct)
responses_add["requests"].append(dct)
if DEBUG:
print('The subprocess responses ...', res)
print('the possibility...', pi)
print('%s round has played' %
('black' if color_to_play == BLACK else 'white'))
def calc_features(self, color_to_play):
one_piece = [0 for _ in range(channel)]
one_feature = []
bd = self.board.board
for j in range(BOARD_SIZE):
for i in range(BOARD_SIZE):
e = bd[i][j]
if e == color_to_play:
one_piece[0], one_piece[1] = 1, 0
elif e == EMPTY:
one_piece[0], one_piece[1] = 0, 0
else:
one_piece[0], one_piece[1] = 0, 1
one_piece[-1] = 1 if color_to_play == BLACK else 0
one_feature = np.hstack((one_feature, one_piece))
# print(one_feature)
self.features = np.vstack((self.features, one_feature))
# print(self.features.shape)
# print(data.shape)
def transform_history_matrix(self):
"""a board state can be transformed by the rotation and transposition to accelerate the data-generation"""
# features
origin = self.features.reshape(-1, BOARD_SIZE, BOARD_SIZE, channel)
tsp = origin.transpose((0, 2, 1, 3))
#print(tsp.shape, self.features.shape)
sp = self.features.shape
self.features = np.vstack((self.features, tsp.reshape(sp)))
#print(self.features.shape)
for i in range(3):
origin = np.rot90(origin, axes=(1, 2))
tsp = np.rot90(tsp, axes=(1, 2))
self.features = np.vstack((self.features, origin.reshape(sp)))
self.features = np.vstack((self.features, tsp.reshape(sp)))
# labels
origin = self.labels.reshape(-1, BOARD_SIZE, BOARD_SIZE)
sp = self.labels.shape
tsp = origin.transpose((0, 2, 1))
self.labels = np.vstack((self.labels, tsp.reshape(sp)))
for i in range(3):
origin = np.rot90(origin, axes=(1, 2))
tsp = np.rot90(tsp, axes=(1, 2))
self.labels = np.vstack((self.labels, origin.reshape(sp)))
self.labels = np.vstack((self.labels, tsp.reshape(sp)))
