BNJInference.java

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 * Created on Oct 15, 2009
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package edu.tum.cs.bayesnets.inference;

import edu.ksu.cis.bnj.ver3.core.CPF;
import edu.ksu.cis.bnj.ver3.core.DiscreteEvidence;
import edu.tum.cs.bayesnets.core.BeliefNetworkEx;

public class BNJInference extends Sampler {

        Class<? extends edu.ksu.cis.bnj.ver3.inference.Inference> algorithmClass;
        
        public BNJInference(BeliefNetworkEx bn, Class<? extends edu.ksu.cis.bnj.ver3.inference.Inference> algoClass) throws Exception {
                super(bn);
                this.algorithmClass = algoClass;
        }

        @Override
        public SampledDistribution _infer()
                        throws Exception {
                // set evidence
                for(int i = 0; i < evidenceDomainIndices.length; i++)
                        if(evidenceDomainIndices[i] != -1)
                                nodes[i].setEvidence(new DiscreteEvidence(evidenceDomainIndices[i]));
                
                // run inference
                edu.ksu.cis.bnj.ver3.inference.Inference algo = algorithmClass.newInstance();
                algo.run(bn.bn);
                
                // retrieve results
                createDistribution();
                for(int i = 0; i < nodes.length; i++) {
                        CPF cpf = algo.queryMarginal(nodes[i]);
                        for(int j = 0; j < dist.values[i].length; j++)
                                dist.values[i][j] = cpf.getDouble(j);
                }
                dist.Z = 1.0;
                dist.steps = 1;
                dist.trials = 1;

                // remove evidence
                bn.removeAllEvidences();
                
                return dist;
        }
        
}

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