public ParticleSet perceptionUpdate(String perception, Randomizer r)
{
// compute Particle Weight
foreach (Particle p in particles)
{
double particleWeight = hmm.sensorModel().get(p.getState(),
perception);
p.setWeight(particleWeight);
}
// weighted sample to create new ParticleSet
ParticleSet result = new ParticleSet(hmm);
while (result.size() != size())
{
foreach (Particle p in particles)
{
double probability = r.nextDouble();
if (probability <= p.getWeight())
{
if (result.size() < size())
{
result.add(new Particle(p.getState(), p.getWeight()));
}
}
}
}
return(result);
}
public RandomVariable smooth(String perception)
{
evidenceFromSmoothedStepToPresent.Add(perception);
Matrix O_t = hmm.sensorModel().asMatrix(perception);
Matrix transitionMatrix = hmm.transitionModel().asMatrix();
if (time > timelag)
{
forwardMessage = hmm.forward(forwardMessage, perception); // This
// seems
// WRONG
// I think this should be
// forwardMessage = hmm.forward(forwardMessage,
// evidenceFromSmoothedStepToPresent.get(0));
// this the perception at t-d. the book's algorithm
// uses the latest perception.
evidenceFromSmoothedStepToPresent.RemoveAt(0);
Matrix O_t_minus_d = hmm.sensorModel().asMatrix(
evidenceFromSmoothedStepToPresent[0]);
B = O_t_minus_d.inverse().times(
transitionMatrix.inverse().times(
B.times(transitionMatrix.times(O_t))));
}
else
{
B = B.times(transitionMatrix.times(O_t));
}
time += 1;
if (time > timelag)
{
Matrix one = hmm.prior().createUnitBelief().asMatrix();
Matrix forwardMatrix = forwardMessage.asMatrix();
RandomVariable result = hmm.prior().duplicate();
Matrix backwardMessage = (B.times(one));
result.updateFrom(forwardMatrix.arrayTimes(backwardMessage));
result.normalize();
return(result);
}
else
{
return(null);
}
}
