Many resources are needed to download a project. Please understand that we have to compensate our server costs. Thank you in advance. Project price only 1 $
You can buy this project and download/modify it how often you want.
* function GIBBS-ASK(X, e, bn, N) returns an estimate of P(X|e)
* local variables: N, a vector of counts for each value of X, initially zero
* Z, the nonevidence variables in bn
* x, the current state of the network, initially copied from e
*
* initialize x with random values for the variables in Z
* for j = 1 to N do
* for each Zi in Z do
* set the value of Zi in x by sampling from P(Zi|mb(Zi))
* N[x] <- N[x] + 1 where x is the value of X in x
* return NORMALIZE(N)
*
*
* Figure 14.16 The Gibbs sampling algorithm for approximate inference in
* Bayesian networks; this version cycles through the variables, but choosing
* variables at random also works.
*
* Note: The implementation has been extended to handle queries with
* multiple variables.
*
* @author Ciaran O'Reilly
* @author Ravi Mohan
*/
public class GibbsAsk implements BayesSampleInference {
private Randomizer randomizer = null;
public GibbsAsk() {
this(new JavaRandomizer(new Random()));
}
public GibbsAsk(Randomizer r) {
this.randomizer = r;
}
// function GIBBS-ASK(X, e, bn, N) returns an estimate of P(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
* P(X1,...,Xn)
* @param Nsamples
* the total number of samples to be generated
* @return an estimate of P(X|e)
*/
public CategoricalDistribution gibbsAsk(RandomVariable[] X,
AssignmentProposition[] e, BayesianNetwork bn, int Nsamples) {
// local variables: N, a vector of counts for each value of X,
// initially zero
double[] N = new double[ProbUtil
.expectedSizeOfCategoricalDistribution(X)];
// Z, the nonevidence variables in bn
Set Z = new LinkedHashSet(
bn.getVariablesInTopologicalOrder());
for (AssignmentProposition ap : e) {
Z.remove(ap.getTermVariable());
}
// x, the current state of the network, initially copied from e
Map x = new LinkedHashMap();
for (AssignmentProposition ap : e) {
x.put(ap.getTermVariable(), ap.getValue());
}
// initialize x with random values for the variables in Z
for (RandomVariable Zi : 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 Zi in Z do
for (RandomVariable Zi : Z) {
// set the value of Zi in x by sampling from
// P(Zi|mb(Zi))
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).
//
// N[x] <- N[x] + 1
// where x is the value of X in x
N[ProbUtil.indexOf(X, x)] += 1.0;
}
// return NORMALIZE(N)
return new ProbabilityTable(N, X).normalize();
}
//
// START-BayesSampleInference
@Override
public CategoricalDistribution ask(final RandomVariable[] X,
final AssignmentProposition[] observedEvidence,
final BayesianNetwork bn, int N) {
return gibbsAsk(X, observedEvidence, bn, N);
}
// END-BayesSampleInference
//
}