SampledDistribution.java

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package edu.tum.cs.bayesnets.inference;

import java.io.PrintStream;

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.inference.BasicSampledDistribution;

public class SampledDistribution extends BasicSampledDistribution implements Cloneable {
        public BeliefNetworkEx bn;
        public int steps, trials, operations;
        protected double maxWeight = 0.0;
        protected boolean debug = true;
        protected BeliefNode[] nodes;
        
        public SampledDistribution(BeliefNetworkEx bn) throws Exception {
                this.bn = bn;
                this.Z = 0.0;
                nodes = bn.bn.getNodes();
                values = new double[nodes.length][];
                for(int i = 0; i < nodes.length; i++)
                        values[i] = new double[nodes[i].getDomain().getOrder()];                        
        }
        
        public synchronized void addSample(WeightedSample s) {
                if(s.weight == 0.0) {
                        throw new RuntimeException("Zero-weight sample was added to distribution. Precision loss?");
                }
                
                // update normalization constant and maximum weight
                Z += s.weight;          
                if(maxWeight < s.weight)
                        maxWeight = s.weight;
                
                // debug info
                if(debug) {
                        double prob = bn.getWorldProbability(s.nodeDomainIndices);
                        /*for(int i = 0; i < nodes.length; i++) {
                                System.out.printf(" %s = %s\n", nodes[i].getName(), nodes[i].getDomain().getName(s.nodeDomainIndices[i]));
                        }*/
                        System.out.printf("sample weight: %s (%.2f%%); max weight: %s (%.2f%%); prob: %s\n", s.weight, s.weight*100/Z, maxWeight, maxWeight*100/Z, prob);
                }
                
                // update distribution values
                for(int i = 0;  i < s.nodeIndices.length; i++) {
                        try {
                                values[s.nodeIndices[i]][s.nodeDomainIndices[i]] += s.weight;
                        }
                        catch(ArrayIndexOutOfBoundsException e) {
                                System.err.println("Error: Node " + nodes[s.nodeIndices[i]].getName() + " was not sampled correctly.");
                                throw e;
                        }
                }
                
                // update number of steps and trials
                trials += s.trials;
                operations += s.operations;
                steps++;
        }
        
        @Override
        public void printVariableDistribution(PrintStream out, int index) {
                BeliefNode node = nodes[index];
                out.println(node.getName() + ":");
                Discrete domain = (Discrete)node.getDomain();
                for(int j = 0; j < domain.getOrder(); j++) {
                        double prob = values[index][j] / Z;
                        out.println(String.format("  %.4f %s", prob, domain.getName(j)));
                }
        }
        
        public double getTrialsPerStep() {
                return (double)trials/steps;
        }
        
        @Override
        public synchronized SampledDistribution clone() throws CloneNotSupportedException {
                return (SampledDistribution)super.clone();
        }

        @Override
        public String[] getDomain(int idx) {
                return BeliefNetworkEx.getDiscreteDomainAsArray(bn.getNode(idx));
        }

        @Override
        public String getVariableName(int idx) {
                return bn.getNode(idx).getName();
        }

        @Override
        public int getVariableIndex(String name) {
                return bn.getNodeIndex(name);
        }

        public void setDebugMode(boolean active) {
                debug = active;
        }

        @Override
        public Integer getNumSamples() {                
                return steps;
        }
}

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