/**
* 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));
}
// function REJECTION-SAMPLING(X, e, bn, N) returns an estimate of
// <b>P</b>(X|e)
/**
* The REJECTION-SAMPLING algorithm in Figure 14.14. For answering queries
* given evidence in a Bayesian Network.
*
* @param X
* the query variables
* @param e
* observed values for variables E
* @param bn
* a Bayesian network
* @param Nsamples
* the total number of samples to be generated
* @return an estimate of <b>P</b>(X|e)
*/
public ICategoricalDistribution rejectionSampling(IRandomVariable[] X, AssignmentProposition[] e, IBayesianNetwork bn, int Nsamples)
{
// local variables: <b>N</b>, a vector of counts for each value of X,
// initially zero
double[] N = new double[ProbUtil.expectedSizeOfCategoricalDistribution(X)];
// for j = 1 to N do
for (int j = 0; j < Nsamples; j++)
{
// <b>x</b> <- PRIOR-SAMPLE(bn)
IMap <IRandomVariable, object> x = ps.priorSample(bn);
// if <b>x</b> is consistent with e then
if (isConsistent(x, e))
{
// <b>N</b>[x] <- <b>N</b>[x] + 1
// where x is the value of X in <b>x</b>
N[ProbUtil.indexOf(X, x)] += 1.0;
}
}
// return NORMALIZE(<b>N</b>)
return(new ProbabilityTable(N, X).normalize());
}
