public void test_pointwiseProduct()
{
IRandomVariable xRV = new RandVar("X", new BooleanDomain());
IRandomVariable yRV = new RandVar("Y", new BooleanDomain());
IRandomVariable zRV = new RandVar("Z", new BooleanDomain());
ProbabilityTable xyD = new ProbabilityTable(new double[] {
// X = true, Y = true
1.0,
// X = true, Y = false
2.0,
// X = false, Y = true
3.0,
// X = false, Y = false
4.0
}, xRV, yRV);
ProbabilityTable zD = new ProbabilityTable(new double[] { 3.0, 7.0 },
zRV);
// Not commutative
assertArrayEquals(new double[] { 3.0, 7.0, 6.0, 14.0, 9.0, 21.0,
12.0, 28.0 }, xyD.pointwiseProduct(zD).getValues(),
DELTA_THRESHOLD);
assertArrayEquals(new double[] { 3.0, 6.0, 9.0, 12.0, 7.0, 14.0,
21.0, 28.0 }, zD.pointwiseProduct(xyD).getValues(),
DELTA_THRESHOLD);
}
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 test_iterateOverTable_fixedValues()
{
RandVar aRV = new RandVar("A", new BooleanDomain());
RandVar bRV = new RandVar("B", new BooleanDomain());
RandVar cRV = new RandVar("C", new BooleanDomain());
ProbabilityTable ptABC = new ProbabilityTable(new double[] {
// A = true, B = true, C = true
1.0,
// A = true, B = true, C = false
10.0,
// A = true, B = false, C = true
100.0,
// A = true, B = false, C = false
1000.0,
// A = false, B = true, C = true
10000.0,
// A = false, B = true, C = false
100000.0,
// A = false, B = false, C = true
1000000.0,
// A = false, B = false, C = false
10000000.0
}, aRV, bRV, cRV);
ICollection <double> answer = CollectionFactory.CreateQueue <double>();
ProbabilityTable.ProbabilityTableIterator pti = new iter(answer);
answer.Clear();
ptABC.iterateOverTable(pti, new AssignmentProposition(aRV, true));
Assert.AreEqual(1111.0, sumOf(answer), DELTA_THRESHOLD);
answer.Clear();
ptABC.iterateOverTable(pti, new AssignmentProposition(aRV, false));
Assert.AreEqual(11110000.0, sumOf(answer), DELTA_THRESHOLD);
answer.Clear();
ptABC.iterateOverTable(pti, new aima.net.probability.proposition.AssignmentProposition(bRV, true));
Assert.AreEqual(110011.0, sumOf(answer), DELTA_THRESHOLD);
answer.Clear();
ptABC.iterateOverTable(pti, new AssignmentProposition(bRV, false));
Assert.AreEqual(11001100.0, sumOf(answer), DELTA_THRESHOLD);
answer.Clear();
ptABC.iterateOverTable(pti, new AssignmentProposition(cRV, true));
Assert.AreEqual(1010101.0, sumOf(answer), DELTA_THRESHOLD);
answer.Clear();
ptABC.iterateOverTable(pti, new AssignmentProposition(cRV, false));
Assert.AreEqual(10101010.0, sumOf(answer), DELTA_THRESHOLD);
answer.Clear();
ptABC.iterateOverTable(pti, new AssignmentProposition(bRV, true),
new AssignmentProposition(cRV, true));
Assert.AreEqual(10001.0, sumOf(answer), DELTA_THRESHOLD);
}
public void test_indexesOfValue()
{
RandVar X = new RandVar("X", new BooleanDomain());
RandVar Y = new RandVar("Y", new ArbitraryTokenDomain("A", "B", "C"));
RandVar Z = new RandVar("Z", new BooleanDomain());
// An ordered X,Y,Z enumeration of values should look like:
// 00: true, A, true
// 01: true, A, false
// 02: true, B, true
// 03: true, B, false
// 04: true, C, true
// 05: true, C, false
// 06: false, A, true
// 07: false, A, false
// 08: false, B, true
// 09: false, B, false
// 10: false, C, true
// 11: false, C, false
IRandomVariable[] vars = new IRandomVariable[] { X, Y, Z };
IMap <IRandomVariable, object> even = CollectionFactory.CreateInsertionOrderedMap <IRandomVariable, object>();
even.Put(X, true);
CollectionAssert.AreEqual(new int[] { 0, 1, 2, 3, 4, 5 },
ProbUtil.indexesOfValue(vars, 0, even));
even.Put(X, false);
CollectionAssert.AreEqual(new int[] { 6, 7, 8, 9, 10, 11 },
ProbUtil.indexesOfValue(vars, 0, even));
even.Put(Y, "A");
CollectionAssert.AreEqual(new int[] { 0, 1, 6, 7 },
ProbUtil.indexesOfValue(vars, 1, even));
even.Put(Y, "B");
CollectionAssert.AreEqual(new int[] { 2, 3, 8, 9 },
ProbUtil.indexesOfValue(vars, 1, even));
even.Put(Y, "C");
CollectionAssert.AreEqual(new int[] { 4, 5, 10, 11 },
ProbUtil.indexesOfValue(vars, 1, even));
even.Put(Z, true);
CollectionAssert.AreEqual(new int[] { 0, 2, 4, 6, 8, 10 },
ProbUtil.indexesOfValue(vars, 2, even));
even.Put(Z, false);
CollectionAssert.AreEqual(new int[] { 1, 3, 5, 7, 9, 11 },
ProbUtil.indexesOfValue(vars, 2, even));
}
/**
* This method is the initialization phase of the algorithm. It has to be called to generate a set of samples of count N.
* @param N the count of samples.
* @return a set containing N samples.
*/
public ISet <P> generateCloud(int N)
{
ISet <P> samples = CollectionFactory.CreateSet <P>();
int[] indexes = new int[N];
for (int i = 0; i < N; ++i)
{
samples.Add(map.randomPose());
indexes[i] = i;
}
sampleIndexes = new RandVar(SAMPLE_INDEXES_NAME, new FiniteIntegerDomain(indexes));
return(samples);
}
public ICategoricalDistribution forward(ICategoricalDistribution f1_t, ICollection <AssignmentProposition> e_tp1)
{
ICategoricalDistribution s1 = new ProbabilityTable(f1_t.getFor());
// Set up required working variables
IProposition[] props = new IProposition[s1.getFor().Size()];
int i = 0;
foreach (IRandomVariable rv in s1.getFor())
{
props[i] = new RandVar(rv.getName(), rv.getDomain());
++i;
}
IProposition Xtp1 = ProbUtil.constructConjunction(props);
AssignmentProposition[] xt = new AssignmentProposition[tToTm1StateVarMap.Size()];
IMap <IRandomVariable, AssignmentProposition> xtVarAssignMap = CollectionFactory.CreateInsertionOrderedMap <IRandomVariable, AssignmentProposition>();
i = 0;
foreach (IRandomVariable rv in tToTm1StateVarMap.GetKeys())
{
xt[i] = new AssignmentProposition(tToTm1StateVarMap.Get(rv), "<Dummy Value>");
xtVarAssignMap.Put(rv, xt[i]);
++i;
}
// Step 1: Calculate the 1 time step prediction
// ∑<sub>x<sub>t</sub></sub>
CategoricalDistributionIterator if1_t = new CategoricalDistributionIteratorImpl(transitionModel,
xtVarAssignMap, s1, Xtp1, xt);
f1_t.iterateOver(if1_t);
// Step 2: multiply by the probability of the evidence
// and normalize
// <b>P</b>(e<sub>t+1</sub> | X<sub>t+1</sub>)
ICategoricalDistribution s2 = sensorModel.posteriorDistribution(ProbUtil
.constructConjunction(e_tp1.ToArray()), Xtp1);
return(s2.multiplyBy(s1).normalize());
}
public ICategoricalDistribution backward(ICategoricalDistribution b_kp2t, ICollection <AssignmentProposition> e_kp1)
{
ICategoricalDistribution b_kp1t = new ProbabilityTable(b_kp2t.getFor());
// Set up required working variables
IProposition[] props = new IProposition[b_kp1t.getFor().Size()];
int i = 0;
foreach (IRandomVariable rv in b_kp1t.getFor())
{
IRandomVariable prv = tToTm1StateVarMap.Get(rv);
props[i] = new RandVar(prv.getName(), prv.getDomain());
++i;
}
IProposition Xk = ProbUtil.constructConjunction(props);
AssignmentProposition[] ax_kp1 = new AssignmentProposition[tToTm1StateVarMap.Size()];
IMap <IRandomVariable, AssignmentProposition> x_kp1VarAssignMap = CollectionFactory.CreateInsertionOrderedMap <IRandomVariable, AssignmentProposition>();
i = 0;
foreach (IRandomVariable rv in b_kp1t.getFor())
{
ax_kp1[i] = new AssignmentProposition(rv, "<Dummy Value>");
x_kp1VarAssignMap.Put(rv, ax_kp1[i]);
++i;
}
IProposition x_kp1 = ProbUtil.constructConjunction(ax_kp1);
props = e_kp1.ToArray();
IProposition pe_kp1 = ProbUtil.constructConjunction(props);
// ∑<sub>x<sub>k+1</sub></sub>
CategoricalDistributionIterator ib_kp2t = new CategoricalDistributionIteratorImpl2(x_kp1VarAssignMap,
sensorModel, transitionModel, b_kp1t, pe_kp1, Xk, x_kp1);
b_kp2t.iterateOver(ib_kp2t);
return(b_kp1t);
}
public void test_getConditioningCase()
{
RandVar aRV = new RandVar("A", new BooleanDomain());
RandVar bRV = new RandVar("B", new BooleanDomain());
RandVar cRV = new RandVar("C", new BooleanDomain());
CPT cpt = new CPT(cRV, new double[] {
// A = true, B = true, C = true
0.1,
// A = true, B = true, C = false
0.9,
// A = true, B = false, C = true
0.2,
// A = true, B = false, C = false
0.8,
// A = false, B = true, C = true
0.3,
// A = false, B = true, C = false
0.7,
// A = false, B = false, C = true
0.4,
// A = false, B = false, C = false
0.6
}, aRV, bRV);
assertArrayEquals(new double[] { 0.1, 0.9 }, cpt
.GetConditioningCase(true, true).getValues(), DELTA_THRESHOLD);
assertArrayEquals(new double[] { 0.2, 0.8 }, cpt
.GetConditioningCase(true, false).getValues(), DELTA_THRESHOLD);
assertArrayEquals(new double[] { 0.3, 0.7 }, cpt
.GetConditioningCase(false, true).getValues(), DELTA_THRESHOLD);
assertArrayEquals(new double[] { 0.4, 0.6 }, cpt
.GetConditioningCase(false, false).getValues(), DELTA_THRESHOLD);
}
public void test_pointwiseProductPOS()
{
IRandomVariable xRV = new RandVar("X", new BooleanDomain());
IRandomVariable yRV = new RandVar("Y", new BooleanDomain());
IRandomVariable zRV = new RandVar("Z", new BooleanDomain());
ProbabilityTable xyD = new ProbabilityTable(new double[] {
// X = true, Y = true
1.0,
// X = true, Y = false
2.0,
// X = false, Y = true
3.0,
// X = false, Y = false
4.0
}, xRV, yRV);
ProbabilityTable zD = new ProbabilityTable(new double[] { 3.0, 7.0 },
zRV);
// Make commutative by specifying an order for the product
assertArrayEquals(xyD.pointwiseProduct(zD).getValues(), zD
.pointwiseProductPOS(xyD, xRV, yRV, zRV).getValues(),
DELTA_THRESHOLD);
}
public void test_indexOf()
{
RandVar X = new RandVar("X", new BooleanDomain());
RandVar Y = new RandVar("Y", new ArbitraryTokenDomain("A", "B", "C"));
RandVar Z = new RandVar("Z", new BooleanDomain());
// An ordered X,Y,Z enumeration of values should look like:
// 00: true, A, true
// 01: true, A, false
// 02: true, B, true
// 03: true, B, false
// 04: true, C, true
// 05: true, C, false
// 06: false, A, true
// 07: false, A, false
// 08: false, B, true
// 09: false, B, false
// 10: false, C, true
// 11: false, C, false
IRandomVariable[] vars = new IRandomVariable[] { X, Y, Z };
IMap <IRandomVariable, object> even = CollectionFactory.CreateInsertionOrderedMap <IRandomVariable, object>();
even.Put(X, true);
even.Put(Y, "A");
even.Put(Z, true);
Assert.AreEqual(0, ProbUtil.indexOf(vars, even));
even.Put(Z, false);
Assert.AreEqual(1, ProbUtil.indexOf(vars, even));
even.Put(Y, "B");
even.Put(Z, true);
Assert.AreEqual(2, ProbUtil.indexOf(vars, even));
even.Put(Z, false);
Assert.AreEqual(3, ProbUtil.indexOf(vars, even));
even.Put(Y, "C");
even.Put(Z, true);
Assert.AreEqual(4, ProbUtil.indexOf(vars, even));
even.Put(Z, false);
Assert.AreEqual(5, ProbUtil.indexOf(vars, even));
//
even.Put(X, false);
even.Put(Y, "A");
even.Put(Z, true);
Assert.AreEqual(6, ProbUtil.indexOf(vars, even));
even.Put(Z, false);
Assert.AreEqual(7, ProbUtil.indexOf(vars, even));
even.Put(Y, "B");
even.Put(Z, true);
Assert.AreEqual(8, ProbUtil.indexOf(vars, even));
even.Put(Z, false);
Assert.AreEqual(9, ProbUtil.indexOf(vars, even));
even.Put(Y, "C");
even.Put(Z, true);
Assert.AreEqual(10, ProbUtil.indexOf(vars, even));
even.Put(Z, false);
Assert.AreEqual(11, ProbUtil.indexOf(vars, even));
}
public void test_divideBy()
{
IRandomVariable xRV = new RandVar("X", new BooleanDomain());
IRandomVariable yRV = new RandVar("Y", new BooleanDomain());
IRandomVariable zRV = new RandVar("Z", new BooleanDomain());
ProbabilityTable xyzD = new ProbabilityTable(new double[] {
// X = true, Y = true, Z = true
1.0,
// X = true, Y = true, Z = false
2.0,
// X = true, Y = false, Z = true
3.0,
// X = true, Y = false, Z = false
4.0,
// X = false, Y = true, Z = true
5.0,
// X = false, Y = true, Z = false
6.0,
// X = false, Y = false, Z = true
7.0,
// X = false, Y = false, Z = false
8.0,
}, xRV, yRV, zRV);
ProbabilityTable xzyD = new ProbabilityTable(new double[] {
// X = true, Z = true, Y = true
1.0,
// X = true, Z = true, Y = false
3.0,
// X = true, Z = false, Y = true
2.0,
// X = true, Z = false, Y = false
4.0,
// X = false, Z = true, Y = true
5.0,
// X = false, Z = true, Y = false
7.0,
// X = false, Z = false, Y = true
6.0,
// X = false, Z = false, Y = false
8.0,
}, xRV, zRV, yRV);
ProbabilityTable zxyD = new ProbabilityTable(new double[] {
// Z = true, X = true, Y = true
1.0,
// Z = true, X = true, Y = false
3.0,
// Z = true, X = false, Y = true
5.0,
// Z = true, X = false, Y = false
7.0,
// Z = false, X = true, Y = true
2.0,
// Z = false, X = true, Y = false
4.0,
// Z = false, X = false, Y = true
6.0,
// Z = false, X = false, Y = false
8.0,
}, zRV, xRV, yRV);
ProbabilityTable zD = new ProbabilityTable(new double[] { 0.5, 0.2 }, zRV);
// The identity distribution (to order results for comparison purposes)
ProbabilityTable iD = new ProbabilityTable(new double[] { 1.0 });
// Ensure the order of the dividends
// makes no difference to the result
assertArrayEquals(xyzD.divideBy(zD).getValues(),
xzyD.divideBy(zD).pointwiseProductPOS(iD, xRV, yRV, zRV)
.getValues(), DELTA_THRESHOLD);
assertArrayEquals(xzyD.divideBy(zD).getValues(),
zxyD.divideBy(zD).pointwiseProductPOS(iD, xRV, zRV, yRV)
.getValues(), DELTA_THRESHOLD);
}