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 testPriorSample_basic()
{
IBayesianNetwork bn = BayesNetExampleFactory.constructCloudySprinklerRainWetGrassNetwork();
AssignmentProposition[] e = new AssignmentProposition[] { new AssignmentProposition(ExampleRV.SPRINKLER_RV, true) };
MockRandomizer r = new MockRandomizer(new double[] { 0.1 });
RejectionSampling rs = new RejectionSampling(new PriorSample(r));
double[] estimate = rs.rejectionSampling(new IRandomVariable[] { ExampleRV.RAIN_RV }, e, bn, 100).getValues();
assertArrayEquals(new double[] { 1.0, 0.0 }, estimate, 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);
}
public void testRejectionSampling_AIMA3e_pg532()
{
// AIMA3e pg. 532
IBayesianNetwork bn = BayesNetExampleFactory.constructCloudySprinklerRainWetGrassNetwork();
AssignmentProposition[] e = new AssignmentProposition[] { new AssignmentProposition(ExampleRV.SPRINKLER_RV, true) };
// 400 required as 4 variables and 100 samples planned
double[] ma = new double[400];
for (int i = 0; i < ma.Length; i += 4)
{
// Of the 100 that we generate, suppose
// that 73 have Sprinkler = false and are rejected,
if (i < (73 * 4))
{
ma[i] = 0.5; // i.e Cloudy=true
ma[i + 1] = 0.2; // i.e. Sprinkler=false
ma[i + 2] = 0.5; // i.e. Rain=true
ma[i + 3] = 0.1; // i.e. WetGrass=true
}
else
{
ma[i] = 0.5; // i.e Cloudy=true
ma[i + 1] = 0.09; // i.e. Sprinkler=true
// while 27 have Sprinkler = true; of the 27,
// 8 have Rain = true
if (i < ((73 + 8) * 4))
{
ma[i + 2] = 0.5; // i.e. Rain=true
}
else
{
// and 19 have Rain = false.
ma[i + 2] = 0.9; // i.e. Rain=false
}
ma[i + 3] = 0.1; // i.e. WetGrass=true
}
}
MockRandomizer r = new MockRandomizer(ma);
RejectionSampling rs = new RejectionSampling(new PriorSample(r));
double[] estimate = rs.rejectionSampling(
new IRandomVariable[] { ExampleRV.RAIN_RV }, e, bn, 100)
.getValues();
assertArrayEquals(new double[] { 0.2962962962962963, 0.7037037037037037 }, 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);
}
public void testPriorSample_basic()
{
// AIMA3e pg. 530
IBayesianNetwork bn = BayesNetExampleFactory
.constructCloudySprinklerRainWetGrassNetwork();
IRandom r = new MockRandomizer(
new double[] { 0.5, 0.5, 0.5, 0.5 });
PriorSample ps = new PriorSample(r);
IMap <IRandomVariable, object> even = ps.priorSample(bn);
Assert.AreEqual(4, even.GetKeys().Size());
Assert.AreEqual(true, even.Get(ExampleRV.CLOUDY_RV));
Assert.AreEqual(false, even.Get(ExampleRV.SPRINKLER_RV));
Assert.AreEqual(true, even.Get(ExampleRV.RAIN_RV));
Assert.AreEqual(true, even.Get(ExampleRV.WET_GRASS_RV));
}