GibbsSampling.java

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package edu.tum.cs.bayesnets.inference;

import java.util.HashMap;

import edu.ksu.cis.bnj.ver3.core.BeliefNode;
import edu.ksu.cis.bnj.ver3.core.Discrete;
import edu.tum.cs.bayesnets.core.BeliefNetworkEx;
import edu.tum.cs.util.Stopwatch;

public class GibbsSampling extends Sampler {
        int[] nodeOrder;        
        HashMap<BeliefNode, BeliefNode[]> children;
        
        public GibbsSampling(BeliefNetworkEx bn) throws Exception {
                super(bn);
                children = new HashMap<BeliefNode, BeliefNode[]>();
                for(int i = 0; i < nodes.length; i++) {
                        children.put(nodes[i], bn.bn.getChildren(nodes[i]));
                }
                nodeOrder = bn.getTopologicalOrder();
        }
        
        public SampledDistribution _infer() throws Exception {
                Stopwatch sw = new Stopwatch();
                createDistribution();           

                // get initial setting with non-zero evidence probability
                out.println("initial setting...");
                WeightedSample s = bn.getWeightedSample(nodeOrder, evidenceDomainIndices, generator);
                if(s == null)
                        throw new Exception("Could not find an initial state with non-zero probability in given number of trials.");
                
                // do Gibbs sampling
                out.println("Gibbs sampling...");
                sw.start();             
                // - get a bunch of samples
                for(int i = 1; i <= numSamples; i++) {
                        if(i % infoInterval == 0)
                                out.println("  step " + i);
                        gibbsStep(evidenceDomainIndices, s);
                        s.trials = 1;
                        s.weight = 1;
                        addSample(s);
                }

                sw.stop();
                report(String.format("time taken: %.2fs (%.4fs per sample, %.1f trials/step)\n", sw.getElapsedTimeSecs(), sw.getElapsedTimeSecs()/numSamples, dist.getTrialsPerStep()));
                return dist;
        }
        
        public void gibbsStep(int[] evidenceDomainIndices, WeightedSample s) {
                // resample all of the (non-evidence) nodes
                for(int j = 0; j < nodes.length; j++)  {
                        // skip evidence nodes
                        if(evidenceDomainIndices[j] != -1)
                                continue;
                        // initialize
                        BeliefNode n = nodes[j];
                        Discrete dom = (Discrete)n.getDomain();
                        int domSize = dom.getOrder();
                        double[] distribution = new double[domSize];
                        // for the current node, calculate a value for each setting
                        for(int d = 0; d < domSize; d++) {
                                s.nodeDomainIndices[j] = d;
                                // consider the probability of the setting given the node's parents
                                double value = getCPTProbability(n, s.nodeDomainIndices);
                                // consider the probability of the children's settings given the respective parents                                     
                                for(BeliefNode child : children.get(n)) {
                                        value *= getCPTProbability(child, s.nodeDomainIndices);
                                }                       
                                distribution[d] = value;
                        }
                        s.nodeDomainIndices[j] = sample(distribution, generator);
                }
        }
}

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srldb
Author(s): Dominik Jain, Stefan Waldherr, Moritz Tenorth
autogenerated on Fri Jan 11 09:58:29 2013