def train(configPath, name):
useGpu = os.environ.get('GNUMPY_USE_GPU', 'auto')
if useGpu == "no":
mode = "cpu"
else:
mode = "gpu"
print '========================================================'
print 'train %s' % name
print "the program is on %s" % mode
print '======================================================='
config = configparser.ConfigParser(
interpolation=configparser.ExtendedInterpolation())
config.read(configPath)
model_name = config.get(name, 'model')
if model_name == "ae":
from ae import AE
model = AE(config, name)
elif model_name == "lae":
from lae import LAE
model = LAE(config, name)
elif model_name == "pae":
from pae import PAE
model = PAE(config, name)
elif model_name == "sae":
from sae import SAE
model = SAE(config, name)
elif model_name == "msae":
from msae import MSAE
model = MSAE(config, name)
model.train()
def createsae(self, prefix, saeName):
if self.config.has_option(self.name, saeName):
saepath = self.readField(self.config, self.name, saeName)
sae = self.loadModel(self.config, saepath)
reset = self.readField(self.config, self.name, "reset_hyperparam")
if reset != "False":
for ae in sae.ae[1:]:
ae.resetHyperParam(self.config, reset)
return sae
else:
return SAE(self.config, self.name, prefix=prefix)
def __init__(self):
super(TrafficPrediction, self).__init__()
self.action_space = spaces.Box(low=-0.05, high=0.05, shape=(257,), dtype=np.float32)
self.observation_space = spaces.Tuple([
spaces.Box(low=0., high=1., shape=(6, 257), dtype=np.float32),
spaces.Box(low=0., high=1., shape=(257,), dtype=np.float32)])
#(last_state_point,)
self.delta = 1.
self.state = None
self.predictor = SAE() #SEKNN #self.load_sae() #KNN()
self.pointer = 0
self.load_sae()
self.link = 257
self.predstep = self.predictor.dp.predstep
self.maxv = np.asarray(self.predictor.dp.maxv)
self.valiX, self.valiY, self.valiY_nofilt = self.predictor.dp.get_data(data_type='vali')
self.testX, self.testY, self.testY_nofilt = self.predictor.dp.get_data(data_type='test')
self.set_predY = []
self.set_predY_ = []
self.set_realY = []
from sae import SAE
import torch
training_set = torch.load('./training_set.pkl')
test_set = torch.load('./test_set.pkl')
sae = SAE(3787, encoder_input=40, decoder_input=40)
sae.add_hiden_layer(20)
sae.add_dropout(0.2)
sae.add_hiden_layer(40)
sae.compile(optimizer='adam')
sae.fit(training_set, 5)
sae.perform(training_set, test_set)
torch.save(sae, 'model.pkl')
