def convert_to_cheating_data(data):
"""
:param data: format is SelfPlayWorker.buffer
:return:
"""
state_list = []
policy_list = []
value_list = []
env = ChessEnv().reset()
for state_fen, policy, value in data:
move_number = int(state_fen.split(' ')[5])
# f2 = maybe_flip_fen(maybe_flip_fen(state_fen,True),True)
# assert state_fen == f2
next_move = env.deltamove(state_fen)
if next_move == None: # new game!
assert state_fen == chess.STARTING_FEN
env.reset()
else:
env.step(next_move, False)
state_planes = env.canonical_input_planes()
# assert env.check_current_planes(state_planes)
side_to_move = state_fen.split(" ")[1]
if side_to_move == 'b':
#assert np.sum(policy) == 0
policy = Config.flip_policy(policy)
else:
#assert abs(np.sum(policy) - 1) < 1e-8
pass
# if np.sum(policy) != 0:
# policy /= np.sum(policy)
#assert abs(np.sum(policy) - 1) < 1e-8
assert len(policy) == 1968
assert state_planes.dtype == np.float32
value_certainty = min(
15, move_number
) / 15 # reduces the noise of the opening... plz train faster
SL_value = value * value_certainty + env.testeval() * (1 -
value_certainty)
state_list.append(state_planes)
policy_list.append(policy)
value_list.append(SL_value)
return np.array(state_list, dtype=np.float32), np.array(
policy_list, dtype=np.float32), np.array(value_list, dtype=np.float32)
def start(config: Config):
PlayWithHumanConfig().update_play_config(config.play)
me_player = None
env = ChessEnv().reset()
while True:
line = input()
words = line.rstrip().split(" ",1)
if words[0] == "uci":
print("id name ChessZero")
print("id author ChessZero")
print("uciok")
elif words[0] == "isready":
if not me_player:
me_player = get_player(config)
print("readyok")
elif words[0] == "ucinewgame":
env.reset()
elif words[0] == "position":
words = words[1].split(" ",1)
if words[0] == "startpos":
env.reset()
else:
if words[0] == "fen": # skip extraneous word
words = words[1].split(' ',1)
fen = words[0]
for _ in range(5):
words = words[1].split(' ',1)
fen += " " + words[0]
env.update(fen)
if len(words) > 1:
words = words[1].split(" ",1)
if words[0] == "moves":
for w in words[1].split(" "):
env.step(w, False)
elif words[0] == "go":
if not me_player:
me_player = get_player(config)
action = me_player.action(env, False)
print(f"bestmove {action}")
elif words[0] == "stop":
pass
elif words[0] == "quit":
break
def start(config: Config):
PlayWithHumanConfig().update_play_config(config.play)
me_player = None
env = ChessEnv().reset()
while True:
line = input()
words = line.rstrip().split(" ", 1)
if words[0] == "uci":
print("id name ChessZero")
print("id author ChessZero")
print("uciok")
elif words[0] == "isready":
if not me_player:
me_player = get_player(config)
print("readyok")
elif words[0] == "ucinewgame":
env.reset()
elif words[0] == "position":
words = words[1].split(" ", 1)
if words[0] == "startpos":
env.reset()
else:
if words[0] == "fen": # skip extraneous word
words = words[1].split(' ', 1)
fen = words[0]
for _ in range(5):
words = words[1].split(' ', 1)
fen += " " + words[0]
env.update(fen)
if len(words) > 1:
words = words[1].split(" ", 1)
if words[0] == "moves":
for w in words[1].split(" "):
env.step(w, False)
elif words[0] == "go":
if not me_player:
me_player = get_player(config)
action = me_player.action(env, False)
print(f"bestmove {action}")
elif words[0] == "stop":
pass
elif words[0] == "quit":
break
def start(config: Config):
PlayWithHumanConfig().update_play_config(config.play)
config.play.thinking_loop = 1
chess_model = None
env = ChessEnv().reset()
while True:
line = input()
words = line.rstrip().split(" ", 1)
if words[0] == "uci":
print("id name ChessZero")
print("id author ChessZero")
print("uciok")
elif words[0] == "isready":
if chess_model is None:
chess_model = PlayWithHuman(config)
print("readyok")
elif words[0] == "ucinewgame":
env.reset()
elif words[0] == "position":
words = words[1].split(" ", 1)
if words[0] == "startpos":
env.reset()
else:
fen = words[0]
for _ in range(5):
words = words[1].split(' ', 1)
fen += " "+words[0]
env.update(fen)
if len(words) > 1:
words = words[1].split(" ", 1)
if words[0] == "moves":
for w in words[1].split(" "):
env.step(w, False)
elif words[0] == "go":
action = chess_model.move_by_ai(env)
print(f"bestmove {action}")
elif words[0] == "stop":
pass
elif words[0] == "quit":
break
