// END-BayesInference
//
//
// PROTECTED METHODS
//
// function ENUMERATE-ALL(vars, e) returns a real number
protected double enumerateAll(ICollection <IRandomVariable> vars, ObservedEvidence e)
{
// if EMPTY?(vars) then return 1.0
if (0 == vars.Size())
{
return(1);
}
// Y <- FIRST(vars)
IRandomVariable Y = Util.first(vars);
// if Y has value y in e
if (e.containsValue(Y))
{
// then return P(y | parents(Y)) * ENUMERATE-ALL(REST(vars), e)
return(e.posteriorForParents(Y) * enumerateAll(Util.rest(vars), e));
}
/**
* <pre>
* else return ∑<sub>y</sub> P(y | parents(Y)) * ENUMERATE-ALL(REST(vars), e<sub>y</sub>)
* where e<sub>y</sub> is e extended with Y = y
* </pre>
*/
double sum = 0;
foreach (object y in ((IFiniteDomain)Y.getDomain()).GetPossibleValues())
{
e.setExtendedValue(Y, y);
sum += e.posteriorForParents(Y) * enumerateAll(Util.rest(vars), e);
}
return(sum);
}
public ProbabilityTableIteratorImpl(IBayesianNetwork bn, ProbabilityTable q, ObservedEvidence e, IRandomVariable[] x, EnumerationAsk enumerationAsk)
{
this.bn = bn;
this.q = q;
this.e = e;
this.x = x;
this.enumerationAsk = enumerationAsk;
}
// function ENUMERATION-ASK(X, e, bn) returns a distribution over X
/**
* The ENUMERATION-ASK algorithm in Figure 14.9 evaluates expression trees
* (Figure 14.8) using depth-first recursion.
*
* @param X
* the query variables.
* @param observedEvidence
* observed values for variables E.
* @param bn
* a Bayes net with variables {X} ∪ E ∪ Y /* Y = hidden
* variables //
* @return a distribution over the query variables.
*/
public ICategoricalDistribution enumerationAsk(IRandomVariable[] X,
AssignmentProposition[] observedEvidence,
IBayesianNetwork bn)
{
// Q(X) <- a distribution over X, initially empty
ProbabilityTable Q = new ProbabilityTable(X);
ObservedEvidence e = new ObservedEvidence(X, observedEvidence, bn);
// for each value x<sub>i</sub> of X do
ProbabilityTable.ProbabilityTableIterator di = new ProbabilityTableIteratorImpl(bn, Q, e, X, this);
Q.iterateOverTable(di);
// return NORMALIZE(Q(X))
return(Q.normalize());
}
public CategoricalDistribution enumerationAsk(RandomVariable[] X,
AssignmentProposition[] observedEvidence,
BayesianNetwork bn)
{
// Q(X) <- a distribution over X, initially empty
ProbabilityTable Q = new ProbabilityTable(X);
ObservedEvidence e = new ObservedEvidence(X, observedEvidence, bn);
// for each value x<sub>i</sub> of X do
// ProbabilityTable.Iterator di = new ProbabilityTable.Iterator()
// {
// int cnt = 0;
// /**
// * <pre>
// * Q(x<sub>i</sub>) <- ENUMERATE-ALL(bn.VARS, e<sub>x<sub>i</sub></sub>)
// * where e<sub>x<sub>i</sub></sub> is e extended with X = x<sub>i</sub>
// * </pre>
// */
// public void iterate(Map<RandomVariable,
// Object > possibleWorld,
// double probability) {
// for (int i = 0; i < X.length; i++) {
// e.setExtendedValue(X[i],
// possibleWorld.get(X[i]));
// }
// Q.setValue(cnt,
// enumerateAll(bn.getVariablesInTopologicalOrder(), e));
// cnt++;
//}
//}
// ;
// Q.iterateOverTable(di);
// // return NORMALIZE(Q(X))
// return Q.normalize();
// TODO: need to clean this up, fail for now
return(null);
}
