public FixedLagSmoothing(HiddenMarkovModel hmm, int timelag)
{
this.hmm = hmm;
this.timelag = timelag;
this.evidenceFromSmoothedStepToPresent = new List<String>();
this.time = 1;
this.forwardMessage = hmm.prior();
this.B = hmm.transitionModel().unitMatrix();
}
public FixedLagSmoothing(HiddenMarkovModel hmm, int timelag)
{
this.hmm = hmm;
this.timelag = timelag;
this.evidenceFromSmoothedStepToPresent = new List <String>();
this.time = 1;
this.forwardMessage = hmm.prior();
this.B = hmm.transitionModel().unitMatrix();
}
public void testTransitionModelGeneratesNewStateWhenGivenOldStateAndProbability()
{
TransitionModel tm = rainman.transitionModel();
String oldState = HmmConstants.RAINING;
String state1 = tm.getStateForProbability(oldState, randomizer
.nextDouble());
String state2 = tm.getStateForProbability(oldState, randomizer
.nextDouble());
Assert.AreEqual(state1, HmmConstants.RAINING);
Assert.AreEqual(state2, HmmConstants.NOT_RAINING);
}
public ParticleSet generateParticleSetForPredictedState(String action,
Randomizer randomizer)
{
ParticleSet predictedParticleSet = new ParticleSet(this.hmm);
foreach (Particle p in particles)
{
String newState = hmm.transitionModel().getStateForProbability(
p.getState(), action, randomizer.nextDouble());
Particle generatedParticle = new Particle(newState);
predictedParticleSet.add(generatedParticle);
}
return(predictedParticleSet);
}
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);
}
}