def __import_beacons(self, lines):
"""
Importe les donnees relative aux balises a partir d un fichier texte
passe en entree et les stocke dans la BDD
:param lines: fichier texte contenant les balises a importer
:return: None
"""
logging.debug("Importation des balises")
l_beacons = self.__line_search(lines, "NBeacons:", "########## Layers")
if l_beacons == []:
print("Aucune balise dans le fichier")
for beacon in l_beacons:
# Attention: la position est une str ici, pas un entier
(b_name, b_x_pos, b_y_pos) = beacon.split()[0:3]
### chercher du becon
r_beacon = self.session.query(mod.Beacon)\
.filter(mod.Beacon.name==b_name)\
.first()
if r_beacon is None:
# Ajout de la balise a la bdd si elle n'y est pas deja
r_beacon = mod.Beacon(name = b_name,
pos_x = int(b_x_pos)/8.0,
pos_y = int(b_y_pos)/8.0)
else:
# Cas ou une balise portant le meme nom est dans la bdd
r_beacon.pos_x = int(b_x_pos)/8.0
r_beacon.pos_y = int(b_y_pos)/8.0
self.session.add(r_beacon)
logging.debug("%d balises ont été importées" % len(l_beacons))
def initdb(username, password):
db.create_all()
import binascii
u = models.User(email=username, password_hash=bcrypt.generate_password_hash(binascii.hexlify(password)), role=0, status=1)
db.session.add(u)
db.session.commit()
print 'Database initialized.'
# remove below for production
t = models.Target(name='demo', guid='aedc4c63-8d13-4a22-81c5-d52d32293867')
db.session.add(t)
db.session.commit()
b = models.Beacon(target_guid='aedc4c63-8d13-4a22-81c5-d52d32293867', agent='HTML', ip='1.2.3.4', port='80', useragent='Mac OS X', comment='this is a comment.', lat='38.2531419', lng='-85.7564855', acc='5')
db.session.add(b)
db.session.commit()
b = models.Beacon(target_guid='aedc4c63-8d13-4a22-81c5-d52d32293867', agent='HTML', ip='5.6.7.8', port='80', useragent='Mac OS X', comment='this is a comment.', lat='34.855117', lng='-82.114192', acc='1')
db.session.add(b)
db.session.commit()
train_generator = utils.seq_batch_generator(training_instances,
item_dict,
config.batch_size)
validate_generator = utils.seq_batch_generator(validate_instances,
item_dict,
config.batch_size,
False)
test_generator = utils.seq_batch_generator(testing_instances,
item_dict,
config.batch_size, False)
# Initialize the network
print(" + Initialize the network")
net = models.Beacon(sess, config.emb_dim, config.rnn_unit,
config.alpha, MAX_SEQ_LENGTH, item_probs,
adj_matrix, config.top_k, config.batch_size,
config.rnn_cell_type, config.dropout_rate,
config.seed, config.learning_rate, config.pooling)
print(" + Initialize parameters")
sess.run(tf.global_variables_initializer())
print("================== TRAINING ====================")
print("@Start training")
procedure.train_network(sess, net, train_generator, validate_generator,
config.nb_epoch, total_train_batches,
total_validate_batches, config.display_step,
config.early_stopping_k, config.epsilon,
tensorboard_dir, model_dir, test_generator,
total_test_batches)