BNSampler.java

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package edu.tum.cs.srl.bayesnets.inference;

import edu.tum.cs.bayesnets.core.BeliefNetworkEx;
import edu.tum.cs.bayesnets.inference.ITimeLimitedInference;
import edu.tum.cs.bayesnets.inference.SampledDistribution;
import edu.tum.cs.srl.bayesnets.bln.AbstractGroundBLN;

public class BNSampler extends Sampler implements ITimeLimitedInference {
        protected int maxTrials;
        protected boolean skipFailedSteps;
        protected Class<? extends edu.tum.cs.bayesnets.inference.Sampler> samplerClass;
        protected edu.tum.cs.bayesnets.inference.Sampler sampler;
        protected int[] evidenceDomainIndices;
                
        public BNSampler(AbstractGroundBLN gbln, Class<? extends edu.tum.cs.bayesnets.inference.Sampler> samplerClass) throws Exception {
                super(gbln);
                maxTrials = 5000;
                this.paramHandler.add("maxTrials", "setMaxTrials");
                this.paramHandler.add("skipFailedSteps", "setSkipFailedSteps");
                this.samplerClass = samplerClass;
        }
        
        public void setMaxTrials(int maxTrials) {
                this.maxTrials = maxTrials; 
        }
        
        public void setSkipFailedSteps(boolean canSkip) {
                this.skipFailedSteps = canSkip;
        }
        
        @Override
        public SampledDistribution _infer() throws Exception {
                // create full evidence
                String[][] evidence = this.gbln.getDatabase().getEntriesAsArray();
                evidenceDomainIndices = gbln.getFullEvidence(evidence);
        
                // initialize sampler           
                if(verbose) System.out.println("initializing...");              
                sampler = getSampler();
                paramHandler.addSubhandler(sampler.getParameterHandler());
                sampler.setEvidence(evidenceDomainIndices);
                sampler.setQueryVars(queryVars);
                sampler.setDebugMode(debug);
                sampler.setNumSamples(numSamples);
                sampler.setInfoInterval(infoInterval);
                sampler.setMaxTrials(maxTrials);
                sampler.setSkipFailedSteps(skipFailedSteps);
                
                // run inference
                if(verbose) System.out.printf("running %s...\n", sampler.getAlgorithmName());
                SampledDistribution dist = sampler.infer();
                return dist;
        }
        
        protected edu.tum.cs.bayesnets.inference.Sampler getSampler() throws Exception {
                return samplerClass.getConstructor(BeliefNetworkEx.class).newInstance(gbln.getGroundNetwork()); 
        }

        @Override
        public String getAlgorithmName() {
                return "BNInference:" + samplerClass.getSimpleName();
        }
        
        public SampledDistribution pollResults() throws CloneNotSupportedException {
                if(sampler == null)
                        return null;
                return sampler.pollResults();
        }
}

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