private void sampleFromTransitionModel(int i)
{
// x <- an event initialized with S[i]
IMap <IRandomVariable, object> x = CollectionFactory.CreateInsertionOrderedMap <IRandomVariable, object>();
for (int n = 0; n < S[i].Length; n++)
{
AssignmentProposition x1 = S[i][n];
x.Put(this.dbn.GetX_1_to_X_0().Get(x1.getTermVariable()), x1.getValue());
}
// foreach variable X<sub>1<sub>i</sub></sub> in
// X<sub>1<sub>1</sub></sub>,...,X<sub>1<sub>n<</sub>/sub> do
foreach (IRandomVariable X1_i in dbn.GetX_1_VariablesInTopologicalOrder())
{
// x1[i] <- a random sample from
// <b>P</b>(X<sub>1<sub>i</sub></sub> |
// parents(X<sub>1<sub>i</sub></sub>))
x.Put(X1_i, ProbUtil.randomSample(dbn.GetNode(X1_i), x, randomizer));
}
// S[i] <- sample from <b>P</b>(<b>X</b><sub>1</sub> |
// <b>X</b><sub>0</sub> = S[i])
for (int n = 0; n < S_tp1[i].Length; n++)
{
AssignmentProposition x1 = S_tp1[i][n];
x1.setValue(x.Get(x1.getTermVariable()));
}
}
/**
* The population is re-sampled to generate a new population of N samples.
* Each new sample is selected from the current population; the probability
* that a particular sample is selected is proportional to its weight. The
* new samples are un-weighted.
*
* @param N
* the number of samples
* @param S
* a vector of samples of size N, where each sample is a vector
* of assignment propositions for the X_1 state variables, which
* is intended to represent the sample for time t
* @param W
* a vector of weights of size N
*
* @return a new vector of samples of size N sampled from S based on W
*/
private AssignmentProposition[][] weightedSampleWithReplacement(int N,
AssignmentProposition[][] S, double[] W)
{
AssignmentProposition[][] newS = new AssignmentProposition[N][];
double[] normalizedW = Util.normalize(W);
for (int i = 0; i < N; ++i)
{
newS[i] = new AssignmentProposition[this.dbn.GetX_0().Size()];
int sample = (int)ProbUtil.sample(randomizer.NextDouble(), sampleIndexes, normalizedW);
for (int idx = 0; idx < S_tp1[i].Length; idx++)
{
AssignmentProposition ap = S_tp1[sample][idx];
newS[i][idx] = new AssignmentProposition(ap.getTermVariable(), ap.getValue());
}
}
return(newS);
}
public void test_AIMA3e_Fig15_18()
{
IRandom mr = new MockRandomizer(new double[] {
// Prior Sample:
// 8 with Rain_t-1=true from prior distribution
0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,
// 2 with Rain_t-1=false from prior distribution
0.6, 0.6,
// (a) Propagate 6 samples Rain_t=true
0.7, 0.7, 0.7, 0.7, 0.7, 0.7,
// 4 samples Rain_t=false
0.71, 0.71, 0.31, 0.31,
// (b) Weight should be for first 6 samples:
// Rain_t-1=true, Rain_t=true, Umbrella_t=false = 0.1
// Next 2 samples:
// Rain_t-1=true, Rain_t=false, Umbrealla_t=false= 0.8
// Final 2 samples:
// Rain_t-1=false, Rain_t=false, Umbrella_t=false = 0.8
// gives W[] =
// [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.8, 0.8, 0.8, 0.8]
// normalized =
// [0.026, ...., 0.211, ....] is approx. 0.156 = true
// the remainder is false
// (c) Resample 2 Rain_t=true, 8 Rain_t=false
0.15, 0.15, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2,
//
// Next Sample:
// (a) Propagate 1 samples Rain_t=true
0.7,
// 9 samples Rain_t=false
0.71, 0.31, 0.31, 0.31, 0.31, 0.31, 0.31, 0.31, 0.31,
// (c) resample 1 Rain_t=true, 9 Rain_t=false
0.0001, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2
});
int N = 10;
ParticleFiltering pf = new ParticleFiltering(N, DynamicBayesNetExampleFactory.getUmbrellaWorldNetwork(), mr);
AssignmentProposition[] e = new AssignmentProposition[] { new AssignmentProposition(ExampleRV.UMBREALLA_t_RV, false) };
AssignmentProposition[][] S = pf.particleFiltering(e);
Assert.AreEqual(N, S.Length);
for (int i = 0; i < N; ++i)
{
Assert.AreEqual(1, S[i].Length);
AssignmentProposition ap = S[i][0];
Assert.AreEqual(ExampleRV.RAIN_t_RV, ap.getTermVariable());
if (i < 2)
{
Assert.AreEqual(true, ap.getValue());
}
else
{
Assert.AreEqual(false, ap.getValue());
}
}
// Generate next sample to ensure everything roles forward ok
// in this case with prefixed probabilities only expect 1 Rain_t=true
S = pf.particleFiltering(e);
Assert.AreEqual(N, S.Length);
for (int i = 0; i < N; ++i)
{
Assert.AreEqual(1, S[i].Length);
AssignmentProposition ap = S[i][0];
Assert.AreEqual(ExampleRV.RAIN_t_RV, ap.getTermVariable());
if (i < 1)
{
Assert.AreEqual(true, ap.getValue());
}
else
{
Assert.AreEqual(false, ap.getValue());
}
}
}