batchdata = numpy.asarray(test_data, dtype=theano.config.floatX)
delay = 0
numpy_rng = numpy.random.RandomState(123)
n_dim = [test_data.shape[1]]
dbn_tadbn = TADBN(numpy_rng=numpy_rng, n_ins=[n_dim],
hidden_layers_sizes=[100],
sparse=0.0, delay=delay, learning_rate=0.01)
dbn_tadbn.pretrain(batchdata, plot_interval=5, static_epochs=50,
save_interval=10, ae_epochs=0, all_epochs=0,
batch_size=5)
up = dbn_tadbn.propup(batchdata, static=True)
up = np.array(up)
print up
generated_series = dbn_tadbn.generate(batchdata, n_samples=40)
plt.figure()
plt.subplot(211)
plt.plot(test_data[:generated_series.shape[1]])
plt.subplot(212)
plt.plot(generated_series[0])
plt.show()
import sys
sys.path.append('..')
from models.tadbn import TADBN
import numpy
import numpy as np
import theano
test_data = np.array([np.sin(np.arange(400) * 0.2),
np.sin(np.arange(400) * 0.4)]).T
batchdata = numpy.asarray(test_data, dtype=theano.config.floatX)
delay = 3
numpy_rng = numpy.random.RandomState(123)
n_dim = [test_data.shape[1]]
dbn_tadbn = TADBN(numpy_rng=numpy_rng, n_ins=[n_dim],
hidden_layers_sizes=[10],
sparse=0.0, delay=delay, learning_rate=0.01)
up = dbn_tadbn.propup(batchdata, static=False)
up = np.array(up)
print up.shape, batchdata.shape
