/**
* Reset this instances persistent variables to be used between called to
* particleFiltering().
*
* @param N
* the number of samples to be maintained
* @param dbn
* a DBN with prior <b>P</b>(<b>X</b><sub>0</sub>), transition
* model <b>P</b>(<b>X</b><sub>1</sub> | <b>X</b><sub>0</sub>),
* sensor model <b>P</b>(<b>E</b><sub>1</sub> |
* <b>X</b><sub>1</sub>)
*/
public void initPersistent(int N, IDynamicBayesianNetwork dbn)
{
this.N = N;
this.dbn = dbn;
// persistent: S, a vector of samples of size N, initially generated
// from <b>P</b>(<b>X</b><sub>0</sub>)
S = new AssignmentProposition[N][];
S_tp1 = new AssignmentProposition[N][];
int[] indexes = new int[N];
for (int i = 0; i < N; ++i)
{
S[i] = new AssignmentProposition[this.dbn.GetX_0().Size()];
S_tp1[i] = new AssignmentProposition[this.dbn.GetX_0().Size()];
indexes[i] = i;
IMap <IRandomVariable, object> sample = priorSampler.priorSample(this.dbn.GetPriorNetwork());
int idx = 0;
foreach (var sa in sample)
{
S[i][idx] = new AssignmentProposition(this.dbn.GetX_0_to_X_1().Get(sa.GetKey()), sa.GetValue());
S_tp1[i][idx] = new AssignmentProposition(this.dbn.GetX_0_to_X_1().Get(sa.GetKey()), sa.GetValue());
idx++;
}
}
sensorModel = new FiniteBayesModel(dbn, new EliminationAsk());
sampleIndexes = new RandVar("SAMPLE_INDEXES", new FiniteIntegerDomain(indexes));
}
/**
* Construct a Particle Filtering instance.
*
* @param N
* the number of samples to be maintained
* @param dbn
* a DBN with prior <b>P</b>(<b>X</b><sub>0</sub>), transition
* model <b>P</b>(<b>X</b><sub>1</sub> | <b>X</b><sub>0</sub>),
* sensor model <b>P</b>(<b>E</b><sub>1</sub> |
* <b>X</b><sub>1</sub>)
* @param randomizer
* a IRandom to be used for sampling purposes.
*/
public ParticleFiltering(int N, IDynamicBayesianNetwork dbn, IRandom randomizer)
{
this.randomizer = randomizer;
this.priorSampler = new PriorSample(this.randomizer);
initPersistent(N, dbn);
}
/**
* Construct a Particle Filtering instance.
*
* @param N
* the number of samples to be maintained
* @param dbn
* a DBN with prior <b>P</b>(<b>X</b><sub>0</sub>), transition
* model <b>P</b>(<b>X</b><sub>1</sub> | <b>X</b><sub>0</sub>),
* sensor model <b>P</b>(<b>E</b><sub>1</sub> |
* <b>X</b><sub>1</sub>)
*/
public ParticleFiltering(int N, IDynamicBayesianNetwork dbn)
: this(N, dbn, CommonFactory.CreateRandom())
{
}
