public void testFixedLagSmoothing_lag_1_UmbrellaWorld()
{
FixedLagSmoothing uw = new FixedLagSmoothing(HMMExampleFactory.getUmbrellaWorldModel(), 1);
// Day 1 - Lag 1
ICollection <AssignmentProposition> e1 = CollectionFactory.CreateQueue <AssignmentProposition>();
e1.Add(new AssignmentProposition(ExampleRV.UMBREALLA_t_RV, true));
ICategoricalDistribution smoothed = uw.fixedLagSmoothing(e1);
Assert.IsNull(smoothed);
// Day 2 - Lag 1
ICollection <AssignmentProposition> e2 = CollectionFactory.CreateQueue <AssignmentProposition>();
e2.Add(new AssignmentProposition(ExampleRV.UMBREALLA_t_RV, true));
smoothed = uw.fixedLagSmoothing(e2);
// Day 1 smoothed probabilities based on 2 days of evidence
Assert.IsNotNull(smoothed);
assertArrayEquals(new double[] { 0.883, 0.117 }, smoothed.getValues(), DELTA_THRESHOLD);
// Day 3 - Lag 1
ICollection <AssignmentProposition> e3 = CollectionFactory.CreateQueue <AssignmentProposition>();
e3.Add(new AssignmentProposition(ExampleRV.UMBREALLA_t_RV, false));
smoothed = uw.fixedLagSmoothing(e3);
// Day 2 smoothed probabilities based on 3 days of evidence
Assert.IsNotNull(smoothed);
assertArrayEquals(new double[] { 0.799, 0.201 }, smoothed.getValues(), DELTA_THRESHOLD);
}
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);
}
protected void testForwardStep_UmbrellaWorld(IForwardStepInference uw)
{
// AIMA3e pg. 572
// Day 0, no observations only the security guards prior beliefs
// P(R<sub>0</sub>) = <0.5, 0.5>
ICategoricalDistribution prior = new ProbabilityTable(new double[] { 0.5, 0.5 }, ExampleRV.RAIN_t_RV);
// Day 1, the umbrella appears, so U<sub>1</sub> = true.
// ≈ <0.818, 0.182>
ICollection <AssignmentProposition> e1 = CollectionFactory.CreateQueue <AssignmentProposition>();
e1.Add(new AssignmentProposition(ExampleRV.UMBREALLA_t_RV, true));
ICategoricalDistribution f1 = uw.forward(prior, e1);
assertArrayEquals(new double[] { 0.818, 0.182 }, f1.getValues(), DELTA_THRESHOLD);
// Day 2, the umbrella appears, so U<sub>2</sub> = true.
// ≈ <0.883, 0.117>
ICollection <AssignmentProposition> e2 = CollectionFactory.CreateQueue <AssignmentProposition>();
e2.Add(new AssignmentProposition(ExampleRV.UMBREALLA_t_RV, true));
ICategoricalDistribution f2 = uw.forward(f1, e2);
assertArrayEquals(new double[] { 0.883, 0.117 }, f2.getValues(),
DELTA_THRESHOLD);
}
// AIMA3e pg. 496
protected void test_MeningitisStiffNeckModel_Distributions(
IFiniteProbabilityModel model)
{
AssignmentProposition astiffNeck = new AssignmentProposition(
ExampleRV.STIFF_NECK_RV, true);
// AIMA3e pg. 497
// P<>(Mengingitis | stiffneck) = α<P(s | m)P(m), P(s | ~m)P(~m)>
ICategoricalDistribution dMeningitisGivenStiffNeck = model
.posteriorDistribution(ExampleRV.MENINGITIS_RV, astiffNeck);
Assert.AreEqual(2, dMeningitisGivenStiffNeck.getValues().Length);
Assert.AreEqual(0.0014, dMeningitisGivenStiffNeck.getValues()[0],
DELTA_THRESHOLD);
Assert.AreEqual(0.9986, dMeningitisGivenStiffNeck.getValues()[1],
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);
}
protected void testBackwardStep_UmbrellaWorld(IBackwardStepInference uw)
{
// AIMA3e pg. 575
ICategoricalDistribution b_kp2t = new ProbabilityTable(new double[] { 1.0, 1.0 }, ExampleRV.RAIN_t_RV);
ICollection <AssignmentProposition> e2 = CollectionFactory.CreateQueue <AssignmentProposition>();
e2.Add(new AssignmentProposition(ExampleRV.UMBREALLA_t_RV, true));
ICategoricalDistribution b1 = uw.backward(b_kp2t, e2);
assertArrayEquals(new double[] { 0.69, 0.41 }, b1.getValues(), DELTA_THRESHOLD);
}
public void iterate(IMap <IRandomVariable, object> possibleWorld, double probability)
{
// <b>P</b>(X<sub>t+1</sub> | x<sub>t</sub>)*
// P(x<sub>t</sub> | e<sub>1:t</sub>)
foreach (var av in possibleWorld)
{
xtVarAssignMap.Get(av.GetKey()).setValue(av.GetValue());
}
int i = 0;
foreach (double tp in transitionModel.posteriorDistribution(xtp1, xt).getValues())
{
s1.setValue(i, s1.getValues()[i] + (tp * probability));
++i;
}
}
public void iterate(IMap <IRandomVariable, object> possibleWorld, double probability)
{
// Assign current values for x<sub>k+1</sub>
foreach (var av in possibleWorld)
{
x_kp1VarAssignMap.Get(av.GetKey()).setValue(av.GetValue());
}
// P(e<sub>k+1</sub> | x<sub>k+1</sub>)
// P(e<sub>k+2:t</sub> | x<sub>k+1</sub>)
double p = sensorModel.posterior(pe_kp1, x_kp1) * probability;
// <b>P</b>(x<sub>k+1</sub> | X<sub>k</sub>)
int i = 0;
foreach (double tp in transitionModel.posteriorDistribution(x_kp1, xk).getValues())
{
b_kp1t.setValue(i, b_kp1t.getValues()[i] + (tp * p));
++i;
}
}
public virtual Matrix convert(ICategoricalDistribution fromCD)
{
double[] values = fromCD.getValues();
return(new Matrix(values, values.Length));
}
//
// PROTECTED
//
protected void test_RollingPairFairDiceModel_Distributions(IFiniteProbabilityModel model)
{
AssignmentProposition ad1_1 = new AssignmentProposition(ExampleRV.DICE_1_RV, 1);
ICategoricalDistribution dD1_1 = model.priorDistribution(ad1_1);
assertArrayEquals(new double[] { 1.0 / 6.0 }, dD1_1.getValues(), DELTA_THRESHOLD);
ICategoricalDistribution dPriorDice1 = model.priorDistribution(ExampleRV.DICE_1_RV);
assertArrayEquals(new double[] { 1.0 / 6.0, 1.0 / 6.0, 1.0 / 6.0, 1.0 / 6.0, 1.0 / 6.0, 1.0 / 6.0 },
dPriorDice1.getValues(), DELTA_THRESHOLD);
ICategoricalDistribution dPriorDice2 = model.priorDistribution(ExampleRV.DICE_2_RV);
assertArrayEquals(new double[] { 1.0 / 6.0, 1.0 / 6.0, 1.0 / 6.0, 1.0 / 6.0, 1.0 / 6.0, 1.0 / 6.0 },
dPriorDice2.getValues(), DELTA_THRESHOLD);
ICategoricalDistribution dJointDice1Dice2 = model.jointDistribution(ExampleRV.DICE_1_RV, ExampleRV.DICE_2_RV);
Assert.AreEqual(36, dJointDice1Dice2.getValues().Length);
for (int i = 0; i < dJointDice1Dice2.getValues().Length; i++)
{
Assert.AreEqual(1.0 / 36.0, dJointDice1Dice2.getValues()[i], DELTA_THRESHOLD);
}
ICategoricalDistribution dJointDice2Dice1 = model.jointDistribution(ExampleRV.DICE_2_RV, ExampleRV.DICE_1_RV);
Assert.AreEqual(36, dJointDice2Dice1.getValues().Length);
for (int i = 0; i < dJointDice2Dice1.getValues().Length; i++)
{
Assert.AreEqual(1.0 / 36.0, dJointDice2Dice1.getValues()[i], DELTA_THRESHOLD);
}
//
// Test Sets of events
IntegerSumProposition total11 = new IntegerSumProposition("Total",
new FiniteIntegerDomain(11), ExampleRV.DICE_1_RV,
ExampleRV.DICE_2_RV);
// P<>(Total = 11) = <2.0/36.0>
assertArrayEquals(new double[] { 2.0 / 36.0 }, model.priorDistribution(total11).getValues(), DELTA_THRESHOLD);
// P<>(Dice1, Total = 11)
// = <0.0, 0.0, 0.0, 0.0, 1.0/36.0, 1.0/36.0>
assertArrayEquals(new double[] { 0, 0, 0, 0, 1.0 / 36.0, 1.0 / 36.0 },
model.priorDistribution(ExampleRV.DICE_1_RV, total11)
.getValues(), DELTA_THRESHOLD);
EquivalentProposition doubles = new EquivalentProposition("Doubles",
ExampleRV.DICE_1_RV, ExampleRV.DICE_2_RV);
// P(Doubles) = <1.0/6.0>
assertArrayEquals(new double[] { 1.0 / 6.0 }, model
.priorDistribution(doubles).getValues(), DELTA_THRESHOLD);
//
// Test posterior
//
// P<>(Dice1, Total = 11)
// = <0.0, 0.0, 0.0, 0.0, 0.5, 0.5>
assertArrayEquals(new double[] { 0, 0, 0, 0, 0.5, 0.5 }, model
.posteriorDistribution(ExampleRV.DICE_1_RV, total11)
.getValues(), DELTA_THRESHOLD);
// P<>(Dice1 | Doubles) = <1/6, 1/6, 1/6, 1/6, 1/6, 1/6>
assertArrayEquals(new double[] { 1.0 / 6.0, 1.0 / 6.0, 1.0 / 6.0, 1.0 / 6.0, 1.0 / 6.0, 1.0 / 6.0 }, model
.posteriorDistribution(ExampleRV.DICE_1_RV, doubles)
.getValues(), DELTA_THRESHOLD);
ICategoricalDistribution dPosteriorDice1GivenDice2 = model
.posteriorDistribution(ExampleRV.DICE_1_RV, ExampleRV.DICE_2_RV);
Assert.AreEqual(36, dPosteriorDice1GivenDice2.getValues().Length);
for (int i = 0; i < dPosteriorDice1GivenDice2.getValues().Length; i++)
{
Assert.AreEqual(1.0 / 6.0, dPosteriorDice1GivenDice2.getValues()[i], DELTA_THRESHOLD);
}
ICategoricalDistribution dPosteriorDice2GivenDice1 = model
.posteriorDistribution(ExampleRV.DICE_2_RV, ExampleRV.DICE_1_RV);
Assert.AreEqual(36, dPosteriorDice2GivenDice1.getValues().Length);
for (int i = 0; i < dPosteriorDice2GivenDice1.getValues().Length; i++)
{
Assert.AreEqual(1.0 / 6.0, dPosteriorDice2GivenDice1.getValues()[i], DELTA_THRESHOLD);
}
}