def _go_to_child(self, parent, child_index):
if debug:
print "go_to @", self._stack_string()
move = naf.policy_index_point(self.game.turn, child_index)
subnode = parent.subnodes[child_index]
self.game.play(move)
parent.Ns[child_index] += 1
if subnode:
gc_index = self._pick_expand_index(subnode)
if gc_index == "wait":
parent.Ns[child_index] -= 1
r = 0
elif gc_index == "lose":
subnode.proven = True
subnode.winning_index = None
subnode.score = -1
self.finished_leves.append(subnode)
r = 1
else:
assert gc_index >= 0
r = self._go_to_child(subnode, gc_index)
else:
parent.subnodes[child_index] = self._expand_leaf(
parent, child_index)
r = 1
self.game.undo()
return r
def pick_move(self, game):
if self.use_swap and len(game.history) < 2:
if len(game.history) == 0:
self.report = "swapmodel"
return swapmodel.choose_first_move()
elif swapmodel.want_swap(game.history[0]):
self.report = "swapmodel"
return "swap"
# else didn't want swap so compute a regular move
N = self.nm.mcts(game, self.num_trials)
self.report = self.nm.report
# When a forcing win or forcing draw move is found, there's no policy
# array returned
if isinstance(N, (str, twixt.Point)):
return N
if self.temperature == 0.0:
mx = N.max()
weights = numpy.where(N == mx, 1.0, 0.0)
elif self.temperature == 1.0:
weights = N
elif self.temperature == 0.5:
weights = N**2
if self.verbosity >= 2:
print "weights=", weights
index = numpy.random.choice(numpy.arange(len(weights)),
p=weights / weights.sum())
return naf.policy_index_point(game, index), N
def _top_moves_str(self):
indices = numpy.argsort(self.root.P[self.root.LMnz])
pts = [
str(naf.policy_index_point(self.game, self.root.LMnz[0][index]))
for index in indices[-3:]
]
return ":" + ",".join(pts)
def show_game_with_p(game, P):
fig, ax = plt.subplots()
D = numpy.zeros((SIZE, SIZE))
for i, p in enumerate(P):
point = naf.policy_index_point(game, i)
print point, p
D[point.y, point.x] = p
ax.imshow(D)
game_to_axes(game, ax)
plt.show()
def _node_to_pv(self, node, color):
descend_index = None
if node.winning_index is not None:
descend_index = node.winning_index
else:
nmax = node.Nf.max()
if nmax < 2:
return []
Nfnz = (node.Nf == nmax).nonzero()
i1 = node.Q[Nfnz].argmax()
i2 = Nfnz[0][i1]
descend_index = i2
move = naf.policy_index_point(color, descend_index)
sub = self._node_to_pv(node.subnodes[descend_index], 1 - color)
sub.insert(0, str(move))
return sub
def mcts(self, game, trials):
""" Using the neural net, compute the move visit count vector """
self.compute_root(game)
if self.root == None:
self.root = self.expand_leaf(game)
self.history_at_root = list(game.history)
if self.verbosity >= 1:
top_ixs = numpy.argsort(self.root.P)[-5:]
print "eval=%.3f %s" % (self.root.score, " ".join([
"%s:%d" % (naf.policy_index_point(
game, ix), int(self.root.P[ix] * 10000 + 0.5))
for ix in top_ixs
]))
if not self.root.proven:
for i in range(trials):
assert not self.root.proven
self.visit_node(game, self.root, True, trials - i)
if self.root.proven:
break
if self.root.proven:
if self.root.winning_move:
self.report = "fwin" + self.top_moves_str(game)
return self.root.winning_move
elif self.root.drawing_move:
self.report = "fdraw" + self.top_moves_str(game)
return self.root.drawing_move
else:
self.report = "flose" + self.top_moves_str(game)
return "resign"
if self.verbosity >= 2:
print "N=", self.root.N
print "Q=", self.root.Q
self.report = "%6.3f" % (self.root.Q[numpy.argmax(
self.root.N)]) + self.top_moves_str(game)
return self.root.N
type=str,
nargs='+',
help='a file with selfplay binary logs')
args = parser.parse_args()
for p in args.positions:
colon = p.find(':')
if colon == -1:
raise Exception("Required position format <file>:<index>")
filename = p[:colon]
index = int(p[colon + 1:])
f = open(filename, "rb")
f.seek(index * naf.LearningState.NUM_BYTES)
b = f.read(naf.LearningState.NUM_BYTES)
ts = naf.LearningState.from_bytes(b)
print "evaluation: %d" % (ts.z),
for i in range(len(ts.N)):
if i % 8 == 0:
print
print "%3s:%-4d" % (naf.policy_index_point(twixt.Game.WHITE,
i), ts.N[i]),
print
if args.display:
import ui
tb = ui.TwixtBoardWindow(p)
tb.set_naf(ts.naf)
tb.win.getMouse()
def pick_move(self, game):
if len(game.history) == 0:
self.report = "swapmodel"
return swapmodel.choose_first_move()
elif len(game.history) == 1 and self.use_swap:
if swapmodel.want_swap(game.history[0]):
self.report = "swapmodel"
return "swap"
# If we get here, it is move 2+ and either we cannot or do not swap.
self.game = game
self._preload_root()
self.finished_leaves = []
self.leaves_waiting = 0
self.num_evals = 0
if self.root == None:
self.root = self._expand_leaf(None, None)
self.history_at_root = list(self.game.history)
self._block_until_reply()
num_evals = 1
# Let's start thinkin'!
request_block = False
while not self.root.proven and self.num_evals < self.trials:
self._process_incoming_data(request_block)
request_block = False
self._process_finished_leaves()
if self.root.proven:
break
ei = self._pick_root_expand_index(self.trials - self.num_evals)
if ei >= 0:
r = self._go_to_child(self.root, ei)
if r == 0:
assert self.leaves_waiting > 0
request_block = True
elif self.leaves_waiting == 0:
# no moves worth evaluating; means we lost.
self.root.proven = True
else:
request_block = True
# Okay, done thinkin'. flush out any waiting kids
while self.leaves_waiting > 0:
self._block_until_reply()
self._process_finished_leaves()
if self.root.proven:
if self.root.winning_index is not None:
self.report = "fwin"
return naf.policy_index_point(game, self.root.winning_index)
else:
self.report = "flose"
return "resign"
assert numpy.array_equal(self.root.Nf,
self.root.Ns), (self.root.Nf.sum(),
self.root.Ns.sum())
good_moves = numpy.where(self.root.Nf == self.root.Nf.max())
index = random.choice(good_moves[0])
move = naf.policy_index_point(game, index)
self.report = "%6.3f %s" % (self.root.Q[index],
self._principal_var_str())
return move
nips.rotate(ROT)
pw, ml = ne.eval_one(nips)
ml = naf.rotate_policy_array(ml[0,:], ROT)
print "pwin=",pw
LM = naf.legal_move_policy_array(game)
LMnz = LM.nonzero()
max_ml = ml[LMnz].max()
el = numpy.exp(ml - max_ml)
divisor = el[LMnz].sum()
P = el / divisor
# print P
P[(1-LM).nonzero()] = 0
inds = numpy.argsort(P)
for idx, i in enumerate(reversed(inds[-40:])):
coord = naf.policy_index_point(game, i)
possible_moves.append(coord)
if idx<10:
print "%3s %6.2f" % (str(coord), P[i]*100.0)
#import mpui
#mpui.show_game_with_p(game, P)
if args.thinker:
thinker = twixt.get_thinker(args.thinker, resources)
tup = thinker.pick_move(game)
if type(tup) == tuple:
m, n = thinker.pick_move(game)
else :
m = tup
def visit_node(self, game, node, top=False, trials_left=-1):
""" Visit a node, return the evaluation from the
point of view of the player currently to play. """
assert not game.just_won()
if not node.LM.any():
self.proven = True
if node.drawing_move:
self.score = 0
else:
# all moves lose. very sad.
self.score = -1
return self.score
if top and self.smart_root:
maxn = node.N[node.LMnz].max()
winnables = (node.N > maxn - trials_left) & node.LM
num_winnables = numpy.count_nonzero(winnables)
assert num_winnables > 0, (maxn, trials_left,
numpy.array_str(node.N),
numpy.array_str(node.LM))
if num_winnables == 1:
index = winnables.argmax()
else:
maxes = (node.N == maxn)
num_maxes = numpy.count_nonzero(maxes)
assert num_maxes > 0
if num_maxes == 1:
winnables[maxes.argmax()] = 0
nsum = node.N.sum() # don't need to filter since all are 0
stv = math.sqrt(nsum + 1.0)
U = node.Q + node.P * stv / (1.0 + node.N)
wnz = numpy.nonzero(winnables)
nz_index = U[wnz].argmax()
index = wnz[0][nz_index]
else:
# At least one node worth visiting. Figure out which one to visit...
nsum = node.N.sum() # don't need to filter since all are 0
stv = math.sqrt(nsum + 1.0)
U = node.Q + node.P * stv / (1.0 + node.N)
nz_index = U[node.LMnz].argmax()
index = node.LMnz[0][nz_index]
move = naf.policy_index_point(game.turn, index)
if top and self.verbosity >= 3:
for i in range(len(U)):
m = naf.policy_index_point(game.turn, i)
if top and self.verbosity > 1:
print "selecting index=%d move=%s Q=%.3f P=%.5f N=%d" % (
index, str(move), node.Q[index], node.P[index], node.N[index])
subnode = node.subnodes[index]
game.play(move)
if subnode:
subscore = -self.visit_node(game, subnode)
else:
subnode = self.expand_leaf(game)
node.subnodes[index] = subnode
subscore = -subnode.score
game.undo()
node.N[index] += 1
if subnode.proven:
node.Q[index] = subscore
node.LM[index] = 0
node.LMnz = node.LM.nonzero()
if subscore == 1:
node.proven = True
node.winning_move = move
elif subscore == 0:
node.drawing_move = move
else:
oldq = node.Q[index]
node.Q[index] += (subscore - node.Q[index]) / node.N[index]
return subscore