public void testGibbsAsk_compare()
{
// create two nodes: parent and child with an arc from parent to child
IRandomVariable rvParent = new RandVar("Parent", new BooleanDomain());
IRandomVariable rvChild = new RandVar("Child", new BooleanDomain());
FullCPTNode nodeParent = new FullCPTNode(rvParent, new double[] { 0.7, 0.3 });
new FullCPTNode(rvChild, new double[] { 0.8, 0.2, 0.2, 0.8 }, nodeParent);
// create net
BayesNet net = new BayesNet(nodeParent);
// query parent probability
IRandomVariable[] rvX = new IRandomVariable[] { rvParent };
// ...given child evidence (true)
AssignmentProposition[] propE = new AssignmentProposition[] { new AssignmentProposition(rvChild, true) };
// sample with LikelihoodWeighting
ICategoricalDistribution samplesLW = new LikelihoodWeighting().Ask(rvX, propE, net, 1000);
Assert.AreEqual(0.9, samplesLW.getValue(true), DELTA_THRESHOLD);
// sample with RejectionSampling
ICategoricalDistribution samplesRS = new RejectionSampling().Ask(rvX, propE, net, 1000);
Assert.AreEqual(0.9, samplesRS.getValue(true), DELTA_THRESHOLD);
// sample with GibbsAsk
ICategoricalDistribution samplesGibbs = new GibbsAsk().Ask(rvX, propE, net, 1000);
Assert.AreEqual(0.9, samplesGibbs.getValue(true), DELTA_THRESHOLD);
}
public void testGibbsAsk_basic()
{
IBayesianNetwork bn = BayesNetExampleFactory.constructCloudySprinklerRainWetGrassNetwork();
AssignmentProposition[] e = new AssignmentProposition[] { new AssignmentProposition(ExampleRV.SPRINKLER_RV, true) };
GibbsAsk ga = new GibbsAsk();
double[] estimate = ga.gibbsAsk(new IRandomVariable[] { ExampleRV.RAIN_RV }, e, bn, 1000).getValues();
assertArrayEquals(new double[] { 0.3, 0.7 }, estimate, DELTA_THRESHOLD);
}
