preds = \
model.sample_predictions(
given_data, inputs=given_inputs,
Tpred=T_predict,
inputs_pred=inputs[T_given:T_given + T_predict])
# Plot the predictions
plt.figure()
plt.plot(np.arange(T), data, 'b-', label="true")
plt.plot(T_given + np.arange(T_predict), preds, 'r--', label="prediction")
ylim = plt.ylim()
plt.plot([T_given, T_given], ylim, '-k')
plt.xlabel('time index')
plt.xlim(max(0, T_given - 200), T)
plt.ylabel('prediction')
plt.ylim(ylim)
plt.legend()
# Smooth the data
ys = model.smooth(data, inputs)
plt.figure()
plt.plot(data, 'b-', label="true")
plt.plot(ys, 'r-', lw=2, label="smoothed")
plt.xlabel("Time")
plt.xlim(max(0, T_given - 200), T)
plt.ylabel("Smoothed Data")
plt.legend()
plt.show()
lls = [update(test_model) for _ in progprint_xrange(N_samples)]
# Plot the log likelihoods
plt.figure(figsize=(5, 3))
plt.plot([0, N_samples],
true_model.log_likelihood() * np.ones(2),
'--k',
label="true")
plt.plot(np.arange(N_samples), lls, color=colors[0], label="test")
plt.xlabel('iteration')
plt.ylabel('training likelihood')
plt.legend(loc="lower right")
plt.tight_layout()
plt.savefig("aux/demo_ll.png")
# Smooth the data
smoothed_data = test_model.smooth(data, inputs)
plt.figure(figsize=(5, 3))
plt.plot(data, color=colors[0], lw=2, label="observed")
plt.plot(smoothed_data, color=colors[1], lw=1, label="smoothed")
plt.xlabel("Time")
plt.xlim(0, min(T, 500))
plt.ylabel("Smoothed Data")
plt.ylim(1.2 * np.array(plt.ylim()))
plt.legend(loc="upper center", ncol=2)
plt.tight_layout()
plt.savefig("aux/demo_smooth.png")
plt.show()
