def __init__(self, sign, mod, name, *keys):
self.sign = sign
self.mod = mod
self.name = name
Difficulte.MAP[util.snorm(name)] = self
for k in keys:
Difficulte.MAP[util.snorm(k)] = self
def __init__(self, name, degats, aptitude, *keys):
self.name = name
self.degats = degats
self.aptitude = aptitude
self.parsed_degats = regles.parse_dice(degats)
Arme.TOUTES[util.snorm(name)] = self
for k in keys:
Arme.TOUTES[util.snorm(k)] = self
self.keys = keys
def _junk(self, raw):
if util.snorm(raw) in regles.Difficulte.MAP:
self.difficulte(raw, regles.Difficulte.MAP[raw])
elif self.client.has_perso(raw):
self.persos.append((self.client.get_perso(raw), None))
else:
le_perso, _uid = self.le_perso()
if le_perso is not None:
norm = util.snorm(raw)
if norm in le_perso.ref_map:
self.score(raw, le_perso.ref_map[raw])
elif norm in le_perso.avantages.value:
self.bonus(raw)
elif norm in le_perso.desavantages.value:
self.malus(raw)
else:
self.junk(raw)
else:
self.junk(raw)
def parse_line(self, line):
m = Perso.SHORT_PATTERN.match(line)
if m is not None:
for k, v in m.groupdict().items():
self.setv(k, v)
return
m = Perso.LINE_PATTERN.match(line)
if m is not None:
k = util.snorm(m.group('k'))
v = m.group('v').strip()
self.setv(k, v)
async def get_reply(self, message):
if not message.content.startswith('reload'):
return ()
update_pj = False
update_pnj = False
for t in message.content[6:].split(' '):
t = util.snorm(t)
if t == 'pj':
update_pj = True
if t == 'pnj':
update_pnj = True
npj, npnj = client.load_data(update_pj, update_pnj)
return ('%d PJ, %d PNJ' % (npj, npnj),)
def load_data(self, update_pj=False, update_pnj=False):
with open(self.mj_file) as f:
self.mj_userid = int(f.read().strip())
npj = 0
for pj, path in perso.load(self.pj_path):
pj.niveau.value = 'pj'
userid = int(os.path.basename(path)[:-4])
if userid not in self.pj_par_userid or update_pj:
self.pj_par_userid[userid] = pj
self.add_perso(pj)
npj += 1
npnj = 0
for pnj, path in perso.load(self.pnj_path):
if util.snorm(pnj.nom.value) not in self.persos_par_nom or update_pnj:
self.add_perso(pnj)
npnj += 1
return npj, npnj
checkpoint_file = 'segm.ckpt'
if validation_path:
validation_data = util.get_validation_data(validation_path, char_dic,
vocab_size, n_steps, padd)
seq = 0
while seq < training_iters:
c_istate = np.zeros((batch_size, 2 * n_hidden))
i = 0
fid = util.open_file(train_path, 'r')
for line in fid:
line = line.strip()
if line == "": continue
line = line.decode('utf-8')
sentence = util.snorm(line)
pos = 0
while pos != -1:
batch_xs, batch_ys, next_pos, count = util.next_batch(
sentence, pos, char_dic, vocab_size, n_steps, padd)
'''
print 'window : ' + sentence[pos:pos+n_steps].encode('utf-8')
print 'count : ' + str(count)
print 'next_pos : ' + str(next_pos)
print batch_ys
print batch_xs
'''
feed = {
x: batch_xs,
y_: batch_ys,
istate: c_istate,
def has_perso(self, nom):
return util.snorm(nom) in self.persos_par_nom
def get_perso(self, nom):
return self.persos_par_nom[util.snorm(nom)]
def add_perso(self, p):
self.persos_par_nom[util.snorm(p.nom.value)] = p
saver = tf.train.Saver() # save all variables
checkpoint_dir = model_dir
checkpoint_file = 'segm.ckpt'
validation_data = util.get_validation_data_emb(validation_path, char_dic, id2emb, n_steps, padd)
seq = 0
while seq < training_iters :
c_istate = np.zeros((batch_size, 2*n_hidden))
i = 0
fid = util.open_file(train_path, 'r')
for line in fid :
line = line.strip()
if line == "" : continue
line = line.decode('utf-8')
sentence = util.snorm(line)
pos = 0
while pos != -1 :
batch_xs, batch_ys, next_pos, count = util.next_batch_emb(sentence, pos, char_dic, id2emb, n_steps, padd)
'''
print 'window : ' + sentence[pos:pos+n_steps].encode('utf-8')
print 'count : ' + str(count)
print 'next_pos : ' + str(next_pos)
print batch_ys
print batch_xs
'''
feed={x: batch_xs, y_: batch_ys, istate: c_istate, early_stop:count}
sess.run(optimizer, feed_dict=feed)
pos = next_pos
sys.stderr.write('%s th sentence in %s th iterations ... done\n' % (i, seq))
i += 1
