# Plot the log likelihoods
plt.figure()
plt.plot(lls)
plt.xlabel('iteration')
plt.ylabel('training likelihood')
# Predict forward in time
T_given = 1800
T_predict = 200
given_data = data[:T_given]
given_inputs = inputs[:T_given]
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()
plt.show()
vlbs = [update(model) for _ in progprint_xrange(50)]
# Sample from the mean field posterior
model.resample_from_mf()
# Plot the log likelihoods
plt.figure()
plt.plot(vlbs)
plt.xlabel('iteration')
plt.ylabel('variational lower bound')
# Predict forward in time
given_data = data[:T_given]
preds = \
model.sample_predictions(given_data, Tpred=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()
plt.show()
model = DefaultLDS(n=2,p=data.shape[1]).add_data(data)
for _ in progprint_xrange(100):
model.resample_model()
vlbs = [update(model) for _ in progprint_xrange(50)]
plt.figure(figsize=(3,4))
plt.plot(vlbs)
plt.xlabel('iteration')
plt.ylabel('variational lower bound')
################
# predicting #
################
Npredict = 100
prediction_seed = data[:1700]
predictions = model.sample_predictions(prediction_seed, Npredict)
plt.figure()
plt.plot(data, 'b-')
plt.plot(prediction_seed.shape[0] + np.arange(Npredict), predictions, 'r--')
plt.xlabel('time index')
plt.ylabel('prediction')
plt.show()
model.resample_model()
return model.log_likelihood()
model = DefaultLDS(n=2, p=data.shape[1]).add_data(data)
vlbs = [update(model) for _ in progprint_xrange(100)]
plt.figure(figsize=(3, 4))
plt.plot(vlbs)
plt.xlabel('iteration')
plt.ylabel('variational lower bound')
################
# predicting #
################
Npredict = 100
prediction_seed = data[:1700]
predictions = model.sample_predictions(prediction_seed,
Npredict,
obs_noise=False)
plt.figure()
plt.plot(data, 'b-')
plt.plot(prediction_seed.shape[0] + np.arange(Npredict), predictions, 'r--')
plt.xlabel('time index')
plt.ylabel('prediction')
plt.show()
model.EM_step()
return model.log_likelihood()
model = DefaultLDS(n=2,p=data.shape[1]).add_data(data)
likes = [update(model) for _ in progprint_xrange(50)]
plt.figure(figsize=(3,4))
plt.plot(likes)
plt.xlabel('iteration')
plt.ylabel('training likelihood')
################
# predicting #
################
Npredict = 100
prediction_seed = data[:1700]
predictions = model.sample_predictions(
prediction_seed, Npredict, obs_noise=False)
plt.figure()
plt.plot(data, 'b-')
plt.plot(prediction_seed.shape[0] + np.arange(Npredict), predictions, 'r--')
plt.xlabel('time index')
plt.ylabel('prediction')
plt.show()
