public void testRobotHMMInitialization()
{
Assert.AreEqual(0.5, robotHmm.prior().getProbabilityOf(
HmmConstants.DOOR_OPEN), 0.001);
Assert.AreEqual(0.5, robotHmm.prior().getProbabilityOf(
HmmConstants.DOOR_CLOSED), 0.001);
}
public void testFilteringWithParticleSetsWorksForRainmanHmm()
{
Randomizer r = new MockRandomizer(new double[] { 0.1, 0.2, 0.3, 0.4,
0.5, 0.6, 0.7, 0.8, 0.9 });
ParticleSet starting = rainman.prior().toParticleSet(rainman, r, 100);
Assert.AreEqual(56, starting
.numberOfParticlesWithState(HmmConstants.RAINING));
Assert.AreEqual(44, starting
.numberOfParticlesWithState(HmmConstants.NOT_RAINING));
ParticleSet afterSeeingUmbrella = starting.filter(
HmmConstants.SEE_UMBRELLA, r);
Assert.AreEqual(84, afterSeeingUmbrella
.numberOfParticlesWithState(HmmConstants.RAINING));
Assert.AreEqual(16, afterSeeingUmbrella
.numberOfParticlesWithState(HmmConstants.NOT_RAINING));
// or alternatively
ParticleSet afterNotSeeingUmbrella = starting.filter(
HmmConstants.SEE_NO_UMBRELLA, r);
Assert.AreEqual(12, afterNotSeeingUmbrella
.numberOfParticlesWithState(HmmConstants.RAINING));
Assert.AreEqual(88, afterNotSeeingUmbrella
.numberOfParticlesWithState(HmmConstants.NOT_RAINING));
}
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);
}
}
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();
}
