Algorithm.java

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

import java.lang.reflect.Constructor;

import edu.tum.cs.bayesnets.core.BeliefNetworkEx;
import edu.tum.cs.bayesnets.inference.BNJPearl;
import edu.tum.cs.bayesnets.inference.BackwardSampling;
import edu.tum.cs.bayesnets.inference.BackwardSamplingWithChildren;
import edu.tum.cs.bayesnets.inference.BackwardSamplingWithPriors;
import edu.tum.cs.bayesnets.inference.BeliefPropagation;
import edu.tum.cs.bayesnets.inference.EnumerationAsk;
import edu.tum.cs.bayesnets.inference.IJGP;
import edu.tum.cs.bayesnets.inference.LikelihoodWeighting;
import edu.tum.cs.bayesnets.inference.LikelihoodWeightingWithUncertainEvidence;
import edu.tum.cs.bayesnets.inference.SampleSearch;
import edu.tum.cs.bayesnets.inference.SmileBackwardSampling;
import edu.tum.cs.bayesnets.inference.SmileEPIS;
import edu.tum.cs.bayesnets.inference.SmilePearl;
import edu.tum.cs.bayesnets.inference.VariableElimination;
import edu.tum.cs.srl.bayesnets.bln.AbstractGroundBLN;

public enum Algorithm {
                LikelihoodWeighting("likelihood weighting", null, LikelihoodWeighting.class), 
                LWU("likelihood weighting with uncertain evidence", null, LikelihoodWeightingWithUncertainEvidence.class), 
                GibbsSampling("Gibbs sampling (MCMC)", GibbsSampling.class, null), 
                EPIS("importance sampling based on evidence prepropagation [SMILE]", null, SmileEPIS.class), 
                BackwardSampling("backward simulation", null, BackwardSampling.class),                   
                BackwardSamplingPriors("backward simulation with prior bias", null, BackwardSamplingWithPriors.class), 
                BackwardSamplingChildren("backward simulation with extended context", null, BackwardSamplingWithChildren.class),
                LiftedBackwardSampling("a lifted version of backw. sampling with ext. context", LiftedBackwardSampling.class, null),
                SmileBackwardSampling("backward simulation [SMILE]", null, SmileBackwardSampling.class),                                                 
                SATIS("SAT-IS: satisfiability-based importance sampling", SATIS.class, null), 
                SATISEx("SAT-IS, extended with constraints from CPDs", SATISEx.class, null),
                SATISExGibbs("SAT-IS extended with interspersed Gibbs Sampling steps", SATISExGibbs.class, null),
                SampleSearch("SampleSearch: backtracking search for satisfiable states", null, SampleSearch.class),
                MCSAT("MC-SAT (MCMC method based on SAT-solving)", MCSAT.class, null),
                IJGP("Iterative Join-Graph Propagation", null, IJGP.class),
                BeliefPropagation("Belief Propagation", null, BeliefPropagation.class),
                EnumerationAsk("Enumeration-Ask (exact, highly inefficient)", null, EnumerationAsk.class),
                Pearl("Pearl's algorithm for polytrees (exact)", null, BNJPearl.class),
                SmilePearl("Pearl's algorithm for polytrees (exact) [SMILE]", null, SmilePearl.class),
                VarElim("variable elimination (exact)", null, VariableElimination.class),
                Experimental("an experimental algorithm (usually beta)", "dev.SampleSearchIB"),
                Experimental2("an experimental algorithm (usually beta)", "dev.SampleSearchIBLearning"),
                Experimental3("an experimental algorithm (usually beta)", "dev.SampleSearch2");
                
                protected String description;
                protected Class<? extends edu.tum.cs.bayesnets.inference.Sampler> bnClass;
                protected Class<? extends Sampler> blnClass;
                
                private Algorithm(String description, Class<? extends Sampler> blnClass, Class<? extends edu.tum.cs.bayesnets.inference.Sampler> bnClass) {
                        this.description = description;
                        this.blnClass = blnClass;
                        this.bnClass = bnClass;
                }
                
                @SuppressWarnings("unchecked")
                private Algorithm(String description, String className) {
                        this.description = description;
                        try {
                                Class<?> cl = Class.forName(className);
                                try {
                                        cl.getConstructor(BeliefNetworkEx.class);
                                        bnClass = (Class<? extends edu.tum.cs.bayesnets.inference.Sampler>)cl;
                                }
                                catch(Exception e) {
                                        blnClass = (Class<? extends Sampler>)cl;
                                }
                        } 
                        catch(ClassNotFoundException e) {       
                        } 
                }
                
                public String getDescription() {
                        return description;
                }
                
                public Sampler createSampler(AbstractGroundBLN gbln) throws Exception {
                        Sampler sampler = null;
                        if(bnClass != null) {
                                sampler = new BNSampler(gbln, bnClass);
                        }
                        else if(blnClass != null) {     
                                Constructor<? extends Sampler> constructor;
                                try {
                                         constructor = blnClass.getConstructor(gbln.getClass());
                                }
                                catch(NoSuchMethodException e) {
                                        throw new Exception("Don't know how to instantiate a sampler for the algorithm '" + toString() + "'");
                                }
                                sampler = constructor.newInstance(gbln);
                        }
                        else
                                throw new Exception("Cannot instantiate a sampler for the algorithm '" + toString() + "' - sampler class not given");
                        return sampler;
                }
                
                public static void printList(String indentation) {
                        for(Algorithm a : Algorithm.values()) 
                                System.out.printf("%s%-28s  %s\n", indentation, a.toString(), a.getDescription());
                }
}

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