public RandomVariable Smooth(String perception)
{
evidenceFromSmoothedStepToPresent.Add(perception);
Matrix oT = 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 oTMinusD = hmm.SensorModel.AsMatrix(
evidenceFromSmoothedStepToPresent[0]);
B = oTMinusD.Inverse().Times(
transitionMatrix.Inverse().Times(
B.Times(transitionMatrix.Times(oT))));
}
else
{
B = B.Times(transitionMatrix.Times(oT));
}
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);
}
}
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 HMMAgent(HiddenMarkovModel hmm)
{
this.hmm = hmm;
this.Belief = hmm.Prior().Duplicate();
}