class NaiveBayesClassifier(object):
def __init__(self):
self.label_prob = ProbDist()
self.label_feat_prob = defaultdict(ProbDist)
self._fmap = FeatureMap()
def print_features(self, label):
for fid in self._fmap.keys():
if (label, fid) in self.label_feat_prob:
pdist = self.label_feat_prob[label, fid]
for val in pdist.keys():
print "{0} {1} {2} {3}".format(label, fid, val,
pdist.prob(val))
def train(self, label_features_set):
label_freq_map = FrequencyMap()
label_fid_freq_map = defaultdict(FrequencyMap)
for label, features in label_features_set:
# Count how often the label occurs.
label_freq_map.inc(label)
# Count how often a value for a given feature and label occurs.
for f, val in features.iteritems():
label_fid_freq_map[label, f].inc(val)
# Record that we have seen that feature (for some label).
self._fmap.get(f)
for label in label_freq_map.keys():
num_count = label_freq_map.freq(label)
for f in self._fmap.keys():
count = label_fid_freq_map[label, f].total()
label_fid_freq_map[label, f].inc(None, num_count - count)
# Compute the probabilities.
for label in label_freq_map.keys():
# P(label)
probability = float(label_freq_map.freq(label))/label_freq_map.total()
self.label_prob.set(label, probability)
for ((label, f), freqmap) in label_fid_freq_map.items():
for val in freqmap.keys():
p = float(freqmap.freq(val)) / float(freqmap.total())
self.label_feat_prob[label, f].set(val, p)
def classify(self, features):
features_copy = features.copy()
for f in features_copy.keys():
if f not in self._fmap.keys():
del features_copy[f]
prob_dist = ProbDist()
for label in self.label_prob.keys():
prob_dist.set(label, self.label_prob.logprob(label))
for feat, val in features_copy.iteritems():
if (label, feat) not in self.label_feat_prob:
print "ERROR"
p_dist = self.label_feat_prob[label, feat]
prob_dist.inc(label, p_dist.logprob(val))
return prob_dist
def load_dataset():
print "start loading go dataset files"
# sgf에서 plays 추출
file_names = get_file_names(GO_FOLDER, POSTFIX)
for name in file_names:
plays = Plays()
plays.load_from_sgf(GO_FOLDER + "/" + name)
splitted_name = name.split("_")
play_num = int(splitted_name[1])
features = FeatureMap(plays, play_num)
f = open(
OTHER_TRAIN_FOLDER + "/" + name + "_" + str(play_num) + "_x" +
".csv", 'w')
txt = ""
for feature in features.input_planes_valuenet:
for rows in feature:
for r in range(0, len(rows)):
if r == len(rows) - 1:
txt += str(rows[r])
else:
txt += str(rows[r]) + ","
txt += "\n"
txt += "\n"
f.write(txt)
f.close()
f = open(
OTHER_TRAIN_FOLDER + "/" + name + "_" + str(play_num) + "_y" +
".csv", 'w')
cur_label = splitted_name[2][0]
f.write(cur_label)
f.close()
print "finish.."
def getargumenttopredict(self):
#print 'getargumenttopredict'
#print 'getargumenttopredict : len(self.move_history) : ' + str(len(self.move_history))
if len(self.move_history) == 0:
#print 'len(self.move_history) == 0'
return self.first_state()
s = ""
i = 1
playlist = []
for move in self.move_history:
if i % 2 == 1:
playlist.append(('b', (move[0] - 1, move[1] - 1)))
else:
playlist.append(('w', (move[0] - 1, move[1] - 1)))
i = i + 1
s = None
try:
plays = Plays(playlist)
features = FeatureMap(plays, len(plays))
#s = features.input_planes # <-- use this value
s = features.input_planes_policynet # <-- use this value
except Exception as e:
print "Unexpected error in getargumenttopredict : ", sys.exc_info(
)[0]
print e
pass
return s
def load_dataset():
print "start"
num_saves = 0
file_names = get_file_names(SGF_FOLDER, POSTFIX)
for name in file_names:
plays = Plays()
plays.load_from_sgf(SGF_FOLDER+"/"+name)
if plays.total_nonpass_plays < 10:
continue
for play_num in range(0, plays.total_plays+1):
features = FeatureMap(plays, play_num)
if type(features) is NoneType:
continue
f = open(SAVE_FOLDER+"/"+name+"_"+str(play_num)+"_x"+".csv", 'w')
txt = ""
inputs = features.input_planes_policynet
for feature in inputs:
for rows in feature:
for r in range(0, len(rows)):
if r == len(rows)-1:
txt += str(rows[r])
else:
txt += str(rows[r]) + ","
txt+="\n"
txt+="\n"
f.write(txt)
f.close()
txt = ""
f = open(SAVE_FOLDER+"/"+name+"_"+str(play_num)+"_y"+".csv", 'w')
for rows in features.label:
for r in range(0, len(rows)):
r_value = rows[r]
if r == len(rows)-1:
txt += str(r_value)
else:
txt += str(r_value) + ","
txt+="\n"
f.write(txt)
f.close()
num_saves += 1
if num_saves % 100 == 0:
print "finish "+str(num_saves)+" sgf files.."
print "finish all.."
def getargumenttopredictvalue(self):
#print 'getargumenttopredictvalue'
#print 'getargumenttopredictvalue : len(self.move_history) : ' + str(len(self.move_history))
if len(self.move_history) == 0:
#print 'len(self.move_history) == 0'
return self.first_statevalue()
#print self.move_history
s = ""
i = 1
playlist = []
for move in self.move_history:
if i % 2 == 1:
playlist.append(('b', (move[0] - 1, move[1] - 1)))
else:
playlist.append(('w', (move[0] - 1, move[1] - 1)))
i = i + 1
s = None
try:
#print 'ZZ01'
plays = Plays(playlist)
#print 'ZZ02'
features = FeatureMap(plays, len(plays))
#print 'ZZ03'
s = features.input_planes_valuenet # <-- use this value
#print 'ZZ04'
#print_features(s)
#pprint.pprint(s)
#print 'ZZ05'
except Exception as e:
print "Unexpected error in getargumenttopredictvalue : ", sys.exc_info(
)[0]
print e
#print "Unexpected error in getargumenttopredictvalue : ", sys.exc_info()[0]
pass
return s
def __init__(self):
self.label_prob = ProbDist()
self.label_feat_prob = defaultdict(ProbDist)
self._fmap = FeatureMap()
game.make_move((2, 2))
print str(game.current_board)
#make argument to call model.predict
i = 1
playlist = []
for move in game.move_history:
if i%2 == 1:
playlist.append(('b', (move[0]-1, move[1]-1)))
else:
playlist.append(('w', (move[0]-1, move[1]-1)))
i = i + 1
plays = Plays(playlist)
features = FeatureMap(plays, len(plays))
print "first : "
print features.input_planes # <-- use this value
#another way
print "second : "
print game.getargumenttopredict()
#mcts test. reference only interface. inner logic will be modified.
root = StateNode(None, GoState(game))
mcts = MCTS(tree_policy=UCB1(c=1.41),
default_policy=evaluation,
backup=monte_carlo)
best_action = mcts(root)
start = True
m = 0
while m < rand_move_number:
#make argument to call model.predict
playlist = []
n = 1
for move in game.move_history:
if n % 2 == 1:
playlist.append(('b', (move[0] - 1, move[1] - 1)))
else:
playlist.append(('w', (move[0] - 1, move[1] - 1)))
n += 1
plays = Plays(playlist)
features = FeatureMap(plays, len(playlist))
X = features.input_planes_policynet
pred_pos = sl_policy.predict(np.asarray([X], dtype=np.float))
tmp_pred_pos = np.argmax(pred_pos)
col, row = get_state(tmp_pred_pos)
while not game.legal_move((col, row)):
pred_pos = np.delete(pred_pos, tmp_pred_pos)
if len(pred_pos) == 0:
start = False
break
tmp_pred_pos = np.argmax(pred_pos)
col, row = get_state(tmp_pred_pos)