public ProbabilityTable sumOut(params IRandomVariable[] vars)
{
ISet <IRandomVariable> soutVars = CollectionFactory.CreateSet <IRandomVariable>(this.randomVarInfo.GetKeys());
foreach (IRandomVariable rv in vars)
{
soutVars.Remove(rv);
}
ProbabilityTable summedOut = new ProbabilityTable(soutVars);
if (1 == summedOut.getValues().Length)
{
summedOut.getValues()[0] = getSum();
}
else
{
// Otherwise need to iterate through this distribution
// to calculate the summed out distribution.
object[] termValues = new object[summedOut.randomVarInfo.Size()];
ProbabilityTableIterator di = new ProbabilityTableIteratorImpl(summedOut, termValues);
iterateOverTable(di);
}
return(summedOut);
}
// 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());
}