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* function ENUMERATION-ASK(X, e, bn) returns a distribution over X
* inputs: X, the query variable
* e, observed values for variables E
* bn, a Bayes net with variables {X} ∪ E ∪ Y /* Y = hidden variables //
*
* Q(X) <- a distribution over X, initially empty
* for each value xi of X do
* Q(xi) <- ENUMERATE-ALL(bn.VARS, exi)
* where exi is e extended with X = xi
* return NORMALIZE(Q(X))
*
* ---------------------------------------------------------------------------------------------------
*
* function ENUMERATE-ALL(vars, e) returns a real number
* if EMPTY?(vars) then return 1.0
* Y <- FIRST(vars)
* if Y has value y in e
* then return P(y | parents(Y)) * ENUMERATE-ALL(REST(vars), e)
* else return ∑y P(y | parents(Y)) * ENUMERATE-ALL(REST(vars), ey)
* where ey is e extended with Y = y
*
*
* Figure 14.9 The enumeration algorithm for answering queries on Bayesian
* networks.
*
* Note: The implementation has been extended to handle queries with
* multiple variables.
*
* @author Ciaran O'Reilly
*/
public class EnumerationAsk implements BayesInference {
public 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 CategoricalDistribution enumerationAsk(final RandomVariable[] X,
final AssignmentProposition[] observedEvidence,
final BayesianNetwork bn) {
// Q(X) <- a distribution over X, initially empty
final ProbabilityTable Q = new ProbabilityTable(X);
final ObservedEvidence e = new ObservedEvidence(X, observedEvidence, bn);
// for each value xi of X do
ProbabilityTable.Iterator di = new ProbabilityTable.Iterator() {
int cnt = 0;
/**
*
* Q(xi) <- ENUMERATE-ALL(bn.VARS, exi)
* where exi is e extended with X = xi
*
*/
public void iterate(Map 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();
}
//
// START-BayesInference
public CategoricalDistribution ask(final RandomVariable[] X,
final AssignmentProposition[] observedEvidence,
final BayesianNetwork bn) {
return this.enumerationAsk(X, observedEvidence, bn);
}
// END-BayesInference
//
//
// PROTECTED METHODS
//
// function ENUMERATE-ALL(vars, e) returns a real number
protected double enumerateAll(List vars, ObservedEvidence e) {
// if EMPTY?(vars) then return 1.0
if (0 == vars.size()) {
return 1;
}
// Y <- FIRST(vars)
RandomVariable 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);
}
/**
*
* else return ∑y P(y | parents(Y)) * ENUMERATE-ALL(REST(vars), ey)
* where ey is e extended with Y = y
*
*/
double sum = 0;
for (Object y : ((FiniteDomain) Y.getDomain()).getPossibleValues()) {
e.setExtendedValue(Y, y);
sum += e.posteriorForParents(Y) * enumerateAll(Util.rest(vars), e);
}
return sum;
}
protected class ObservedEvidence {
private BayesianNetwork bn = null;
private Object[] extendedValues = null;
private int hiddenStart = 0;
private int extendedIdx = 0;
private RandomVariable[] var = null;
private Map varIdxs = new HashMap();
public ObservedEvidence(RandomVariable[] queryVariables,
AssignmentProposition[] e, BayesianNetwork bn) {
this.bn = bn;
int maxSize = bn.getVariablesInTopologicalOrder().size();
extendedValues = new Object[maxSize];
var = new RandomVariable[maxSize];
// query variables go first
int idx = 0;
for (int i = 0; i < queryVariables.length; i++) {
var[idx] = queryVariables[i];
varIdxs.put(var[idx], idx);
idx++;
}
// initial evidence variables go next
for (int i = 0; i < e.length; i++) {
var[idx] = e[i].getTermVariable();
varIdxs.put(var[idx], idx);
extendedValues[idx] = e[i].getValue();
idx++;
}
extendedIdx = idx - 1;
hiddenStart = idx;
// the remaining slots are left open for the hidden variables
for (RandomVariable rv : bn.getVariablesInTopologicalOrder()) {
if (!varIdxs.containsKey(rv)) {
var[idx] = rv;
varIdxs.put(var[idx], idx);
idx++;
}
}
}
public void setExtendedValue(RandomVariable rv, Object value) {
int idx = varIdxs.get(rv);
extendedValues[idx] = value;
if (idx >= hiddenStart) {
extendedIdx = idx;
} else {
extendedIdx = hiddenStart - 1;
}
}
public boolean containsValue(RandomVariable rv) {
return varIdxs.get(rv) <= extendedIdx;
}
public double posteriorForParents(RandomVariable rv) {
Node n = bn.getNode(rv);
if (!(n instanceof FiniteNode)) {
throw new IllegalArgumentException(
"Enumeration-Ask only works with finite Nodes.");
}
FiniteNode fn = (FiniteNode) n;
Object[] vals = new Object[1 + fn.getParents().size()];
int idx = 0;
for (Node pn : n.getParents()) {
vals[idx] = extendedValues[varIdxs.get(pn.getRandomVariable())];
idx++;
}
vals[idx] = extendedValues[varIdxs.get(rv)];
return fn.getCPT().getValue(vals);
}
}
//
// PRIVATE METHODS
//
}
