public void testLikelihoodWeighting_AIMA3e_pg533()
{
// AIMA3e pg. 533
// <b>P</b>(Rain | Cloudy = true, WetGrass = true)
IBayesianNetwork bn = BayesNetExampleFactory.constructCloudySprinklerRainWetGrassNetwork();
AssignmentProposition[] e = new AssignmentProposition[] {
new AssignmentProposition(ExampleRV.CLOUDY_RV, true),
new AssignmentProposition(ExampleRV.WET_GRASS_RV, true)
};
// sample P(Sprinkler | Cloudy = true) = <0.1, 0.9>; suppose
// Sprinkler=false
// sample P(Rain | Cloudy = true) = <0.8, 0.2>; suppose Rain=true
MockRandomizer r = new MockRandomizer(new double[] { 0.5, 0.5 });
LikelihoodWeighting lw = new LikelihoodWeighting(r);
double[] estimate = lw.likelihoodWeighting(
new IRandomVariable[] { ExampleRV.RAIN_RV }, e, bn, 1)
.getValues();
// Here the event [true,false,true,true] should have weight 0.45,
// and this is tallied under Rain = true, which when normalized
// should be <1.0, 0.0>;
assertArrayEquals(new double[] { 1.0, 0.0 }, estimate, DELTA_THRESHOLD);
}
public void testLikelihoodWeighting_basic()
{
IBayesianNetwork bn = BayesNetExampleFactory.constructCloudySprinklerRainWetGrassNetwork();
AssignmentProposition[] e = new AssignmentProposition[] { new AssignmentProposition(ExampleRV.SPRINKLER_RV, true) };
MockRandomizer r = new MockRandomizer(new double[] { 0.5, 0.5, 0.5, 0.5 });
LikelihoodWeighting lw = new LikelihoodWeighting(r);
double[] estimate = lw.likelihoodWeighting(
new IRandomVariable[] { ExampleRV.RAIN_RV }, e, bn, 1000)
.getValues();
assertArrayEquals(new double[] { 1.0, 0.0 }, estimate, DELTA_THRESHOLD);
}
