// function WEIGHTED-SAMPLE(bn, e) returns an event and a weight
/**
* The WEIGHTED-SAMPLE function in Figure 14.15.
*
* @param e
* observed values for variables E
* @param bn
* a Bayesian network specifying joint distribution
* <b>P</b>(X<sub>1</sub>,...,X<sub>n</sub>)
* @return return <b>x</b>, w - an event with its associated weight.
*/
public Pair <IMap <IRandomVariable, object>, double> weightedSample(IBayesianNetwork bn, AssignmentProposition[] e)
{
// w <- 1;
double w = 1.0;
// <b>x</b> <- an event with n elements initialized from e
IMap <IRandomVariable, object> x = CollectionFactory.CreateInsertionOrderedMap <IRandomVariable, object>();
foreach (AssignmentProposition ap in e)
{
x.Put(ap.getTermVariable(), ap.getValue());
}
// foreach variable X<sub>i</sub> in X<sub>1</sub>,...,X<sub>n</sub> do
foreach (IRandomVariable Xi in bn.GetVariablesInTopologicalOrder())
{
// if X<sub>i</sub> is an evidence variable with value x<sub>i</sub>
// in e
if (x.ContainsKey(Xi))
{
// then w <- w * P(X<sub>i</sub> = x<sub>i</sub> |
// parents(X<sub>i</sub>))
w *= bn.GetNode(Xi)
.GetCPD()
.GetValue(ProbUtil.getEventValuesForXiGivenParents(bn.GetNode(Xi), x));
}
else
{
// else <b>x</b>[i] <- a random sample from
// <b>P</b>(X<sub>i</sub> | parents(X<sub>i</sub>))
x.Put(Xi, ProbUtil.randomSample(bn.GetNode(Xi), x, randomizer));
}
}
// return <b>x</b>, w
return(new Pair <IMap <IRandomVariable, object>, double>(x, w));
}
// function GIBBS-ASK(X, e, bn, N) returns an estimate of <b>P</b>(X|e)
/**
* The GIBBS-ASK algorithm in Figure 14.16. For answering queries given
* evidence in a Bayesian Network.
*
* @param X
* the query variables
* @param e
* observed values for variables E
* @param bn
* a Bayesian network specifying joint distribution
* <b>P</b>(X<sub>1</sub>,...,X<sub>n</sub>)
* @param Nsamples
* the total number of samples to be generated
* @return an estimate of <b>P</b>(X|e)
*/
public ICategoricalDistribution gibbsAsk(IRandomVariable[] X,
AssignmentProposition[] e, IBayesianNetwork bn, int Nsamples)
{
// local variables: <b>N</b>, a vector of counts for each value of X,
// initially zero
double[] N = new double[ProbUtil.expectedSizeOfCategoricalDistribution(X)];
// Z, the nonevidence variables in bn
ISet <IRandomVariable> Z = CollectionFactory.CreateSet <IRandomVariable>(bn.GetVariablesInTopologicalOrder());
foreach (AssignmentProposition ap in e)
{
Z.Remove(ap.getTermVariable());
}
// <b>x</b>, the current state of the network, initially copied from e
IMap <IRandomVariable, object> x = CollectionFactory.CreateInsertionOrderedMap <IRandomVariable, object>();
foreach (AssignmentProposition ap in e)
{
x.Put(ap.getTermVariable(), ap.getValue());
}
// initialize <b>x</b> with random values for the variables in Z
foreach (IRandomVariable Zi in Z)
{
x.Put(Zi, ProbUtil.randomSample(bn.GetNode(Zi), x, randomizer));
}
// for j = 1 to N do
for (int j = 0; j < Nsamples; j++)
{
// for each Z<sub>i</sub> in Z do
foreach (IRandomVariable Zi in Z)
{
// set the value of Z<sub>i</sub> in <b>x</b> by sampling from
// <b>P</b>(Z<sub>i</sub>|mb(Z<sub>i</sub>))
x.Put(Zi, ProbUtil.mbRandomSample(bn.GetNode(Zi), x, randomizer));
}
// Note: moving this outside the previous for loop,
// as described in fig 14.6, as will only work
// correctly in the case of a single query variable X.
// However, when multiple query variables, rare events
// will get weighted incorrectly if done above. In case
// of single variable this does not happen as each possible
// value gets * |Z| above, ending up with the same ratios
// when normalized (i.e. its still more efficient to place
// outside the loop).
//
// <b>N</b>[x] <- <b>N</b>[x] + 1
// where x is the value of X in <b>x</b>
N[ProbUtil.indexOf(X, x)] += 1.0;
}
// return NORMALIZE(<b>N</b>)
return(new ProbabilityTable(N, X).normalize());
}
// function PRIOR-SAMPLE(bn) returns an event sampled from the prior
// specified by bn
/**
* The PRIOR-SAMPLE algorithm in Figure 14.13. A sampling algorithm that
* generates events from a Bayesian network. Each variable is sampled
* according to the conditional distribution given the values already
* sampled for the variable's parents.
*
* @param bn
* a Bayesian network specifying joint distribution
* <b>P</b>(X<sub>1</sub>,...,X<sub>n</sub>)
* @return an event sampled from the prior specified by bn
*/
public IMap <IRandomVariable, object> priorSample(IBayesianNetwork bn)
{
// x <- an event with n elements
IMap <IRandomVariable, object> x = CollectionFactory.CreateInsertionOrderedMap <IRandomVariable, object>();
// foreach variable X<sub>i</sub> in X<sub>1</sub>,...,X<sub>n</sub> do
foreach (IRandomVariable Xi in bn.GetVariablesInTopologicalOrder())
{
// x[i] <- a random sample from
// <b>P</b>(X<sub>i</sub> | parents(X<sub>i</sub>))
x.Put(Xi, ProbUtil.randomSample(bn.GetNode(Xi), x, randomizer));
}
// return x
return(x);
}
//
// PRIVATE METHODS
//
private IFactor makeFactor(IRandomVariable var, AssignmentProposition[] e, IBayesianNetwork bn)
{
INode n = bn.GetNode(var);
if (!(n is IFiniteNode))
{
throw new IllegalArgumentException("Elimination-Ask only works with finite Nodes.");
}
IFiniteNode fn = (IFiniteNode)n;
ICollection <AssignmentProposition> evidence = CollectionFactory.CreateQueue <AssignmentProposition>();
foreach (AssignmentProposition ap in e)
{
if (fn.GetCPT().Contains(ap.getTermVariable()))
{
evidence.Add(ap);
}
}
return(fn.GetCPT().GetFactorFor(evidence.ToArray()));
}
public double posteriorForParents(IRandomVariable rv)
{
INode n = bn.GetNode(rv);
if (!(n is IFiniteNode))
{
throw new IllegalArgumentException("Enumeration-Ask only works with finite Nodes.");
}
IFiniteNode fn = (IFiniteNode)n;
object[] vals = new object[1 + fn.GetParents().Size()];
int idx = 0;
foreach (INode pn in n.GetParents())
{
vals[idx] = extendedValues[varIdxs.Get(pn.GetRandomVariable())];
idx++;
}
vals[idx] = extendedValues[varIdxs.Get(rv)];
return(fn.GetCPT().GetValue(vals));
}