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 test_RollingPairFairDiceModel_Distributions()
{
foreach (IBayesInference bi in getBayesInferenceImplementations())
{
test_RollingPairFairDiceModel_Distributions(new FiniteBayesModel(
BayesNetExampleFactory.construct2FairDiceNetwor(), bi));
}
}
public void test_BurglaryAlarmModel_Distributions()
{
foreach (IBayesInference bi in getBayesInferenceImplementations())
{
test_BurglaryAlarmModel_Distributions(new FiniteBayesModel(
BayesNetExampleFactory.constructBurglaryAlarmNetwork(), bi));
}
}
public void testInferenceOnBurglaryAlarmNetwork()
{
IBayesianNetwork bn = BayesNetExampleFactory
.constructBurglaryAlarmNetwork();
// AIMA3e. pg. 514
ICategoricalDistribution d = bayesInference
.Ask(new IRandomVariable[] { ExampleRV.ALARM_RV },
new AssignmentProposition[] {
new AssignmentProposition(
ExampleRV.BURGLARY_RV, false),
new AssignmentProposition(
ExampleRV.EARTHQUAKE_RV, false),
new AssignmentProposition(
ExampleRV.JOHN_CALLS_RV, true),
new AssignmentProposition(
ExampleRV.MARY_CALLS_RV, true)
}, bn);
// System.Console.WriteLine("P(Alarm | ~b, ~e, j, m)=" + d);
Assert.AreEqual(2, d.getValues().Length);
Assert.AreEqual(0.5577689243027888, d.getValues()[0],
ProbabilityModelImpl.DEFAULT_ROUNDING_THRESHOLD);
Assert.AreEqual(0.44223107569721115, d.getValues()[1],
ProbabilityModelImpl.DEFAULT_ROUNDING_THRESHOLD);
// AIMA3e pg. 523
// P(Burglary | JohnCalls = true, MaryCalls = true) = <0.284, 0.716>
d = bayesInference
.Ask(new IRandomVariable[] { ExampleRV.BURGLARY_RV },
new AssignmentProposition[] {
new AssignmentProposition(
ExampleRV.JOHN_CALLS_RV, true),
new AssignmentProposition(
ExampleRV.MARY_CALLS_RV, true)
}, bn);
// System.Console.WriteLine("P(Burglary | j, m)=" + d);
Assert.AreEqual(2, d.getValues().Length);
Assert.AreEqual(0.2841718353643929, d.getValues()[0],
ProbabilityModelImpl.DEFAULT_ROUNDING_THRESHOLD);
Assert.AreEqual(0.7158281646356071, d.getValues()[1],
ProbabilityModelImpl.DEFAULT_ROUNDING_THRESHOLD);
// AIMA3e pg. 528
// P(JohnCalls | Burglary = true)
d = bayesInference.Ask(
new IRandomVariable[] { ExampleRV.JOHN_CALLS_RV },
new AssignmentProposition[] { new AssignmentProposition(
ExampleRV.BURGLARY_RV, true) }, bn);
// System.Console.WriteLine("P(JohnCalls | b)=" + d);
Assert.AreEqual(2, d.getValues().Length);
Assert.AreEqual(0.8490169999999999, d.getValues()[0],
ProbabilityModelImpl.DEFAULT_ROUNDING_THRESHOLD);
Assert.AreEqual(0.15098299999999998, d.getValues()[1],
ProbabilityModelImpl.DEFAULT_ROUNDING_THRESHOLD);
}
public void test_MeningitisStiffNeckModel()
{
foreach (IBayesInference bi in getBayesInferenceImplementations())
{
test_MeningitisStiffNeckModel(new FiniteBayesModel(
BayesNetExampleFactory.constructMeningitisStiffNeckNetwork(),
bi));
}
}
public void test_ToothacheCavityCatchWeatherModel_Distributions()
{
foreach (IBayesInference bi in getBayesInferenceImplementations())
{
test_ToothacheCavityCatchWeatherModel_Distributions(new FiniteBayesModel(
BayesNetExampleFactory
.constructToothacheCavityCatchWeatherNetwork(),
bi));
}
}
static void bayesEnumerationAskDemo()
{
System.Console.WriteLine("DEMO: Bayes Enumeration Ask");
System.Console.WriteLine("===========================");
demoToothacheCavityCatchModel(new FiniteBayesModel(
BayesNetExampleFactory.constructToothacheCavityCatchNetwork(),
new EnumerationAsk()));
demoBurglaryAlarmModel(new FiniteBayesModel(
BayesNetExampleFactory.constructBurglaryAlarmNetwork(),
new EnumerationAsk()));
System.Console.WriteLine("===========================");
}
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);
}
static void bayesGibbsAskDemo()
{
System.Console.WriteLine("DEMO: Bayes Gibbs Ask N = " + NUM_SAMPLES);
System.Console.WriteLine("=====================");
demoToothacheCavityCatchModel(new FiniteBayesModel(
BayesNetExampleFactory.constructToothacheCavityCatchNetwork(),
new BayesInferenceApproxAdapter(new GibbsAsk(), NUM_SAMPLES)));
demoBurglaryAlarmModel(new FiniteBayesModel(
BayesNetExampleFactory.constructBurglaryAlarmNetwork(),
new BayesInferenceApproxAdapter(new GibbsAsk(), NUM_SAMPLES)));
System.Console.WriteLine("=====================");
}
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 testInferenceOnToothacheCavityCatchNetwork()
{
IBayesianNetwork bn = BayesNetExampleFactory
.constructToothacheCavityCatchNetwork();
ICategoricalDistribution d = bayesInference.Ask(
new IRandomVariable[] { ExampleRV.CAVITY_RV },
new AssignmentProposition[] { }, bn);
// System.Console.WriteLine("P(Cavity)=" + d);
Assert.AreEqual(2, d.getValues().Length);
Assert.AreEqual(0.2, d.getValues()[0],
ProbabilityModelImpl.DEFAULT_ROUNDING_THRESHOLD);
Assert.AreEqual(0.8, d.getValues()[1],
ProbabilityModelImpl.DEFAULT_ROUNDING_THRESHOLD);
// AIMA3e pg. 493
// P(Cavity | toothache) = <0.6, 0.4>
d = bayesInference.Ask(new IRandomVariable[] { ExampleRV.CAVITY_RV },
new AssignmentProposition[] { new AssignmentProposition(
ExampleRV.TOOTHACHE_RV, true) }, bn);
// System.Console.WriteLine("P(Cavity | toothache)=" + d);
Assert.AreEqual(2, d.getValues().Length);
Assert.AreEqual(0.6, d.getValues()[0],
ProbabilityModelImpl.DEFAULT_ROUNDING_THRESHOLD);
Assert.AreEqual(0.4, d.getValues()[1],
ProbabilityModelImpl.DEFAULT_ROUNDING_THRESHOLD);
// AIMA3e pg. 497
// P(Cavity | toothache AND catch) = <0.871, 0.129>
d = bayesInference
.Ask(new IRandomVariable[] { ExampleRV.CAVITY_RV },
new AssignmentProposition[] {
new AssignmentProposition(
ExampleRV.TOOTHACHE_RV, true),
new AssignmentProposition(ExampleRV.CATCH_RV,
true)
}, bn);
// System.Console.WriteLine("P(Cavity | toothache, catch)=" + d);
Assert.AreEqual(2, d.getValues().Length);
Assert.AreEqual(0.8709677419354839, d.getValues()[0],
ProbabilityModelImpl.DEFAULT_ROUNDING_THRESHOLD);
Assert.AreEqual(0.12903225806451615, d.getValues()[1],
ProbabilityModelImpl.DEFAULT_ROUNDING_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));
}
