Examples with Likelihood dr.inference.model.Likelihood used on opensource projects

Example 21 with Likelihood

use of dr.inference.model.Likelihood in project beast-mcmc by beast-dev.

the class ThreadedCompoundLikelihoodParser method parseXMLObject.

public Object parseXMLObject(XMLObject xo) throws XMLParseException {
    ThreadedCompoundLikelihood compoundLikelihood = new ThreadedCompoundLikelihood();
    for (int i = 0; i < xo.getChildCount(); i++) {
        if (xo.getChild(i) instanceof Likelihood) {
            compoundLikelihood.addLikelihood((Likelihood) xo.getChild(i));
        } else {
            Object rogueElement = xo.getChild(i);
            throw new XMLParseException("An element (" + rogueElement + ") which is not a likelihood has been added to a " + THREADED_COMPOUND_LIKELIHOOD + " element");
        }
    }
    double weight = xo.getAttribute(WEIGHT, 0.0);
    if (weight < 0)
        throw new XMLParseException("Robust weight must be non-negative.");
    compoundLikelihood.setWeightFactor(Math.exp(-weight));
    return compoundLikelihood;
}

Example 22 with Likelihood

use of dr.inference.model.Likelihood in project beast-mcmc by beast-dev.

the class InvariantOperatorParser method parseXMLObject.

public Object parseXMLObject(XMLObject xo) throws XMLParseException {
    double weight = xo.getDoubleAttribute(MCMCOperator.WEIGHT);
    Parameter parameter = (Parameter) xo.getChild(Parameter.class);
    Likelihood likelihood = (Likelihood) xo.getChild(Likelihood.class);
    return new InvariantOperator.Rotation(parameter, weight, likelihood);
}

Example 23 with Likelihood

use of dr.inference.model.Likelihood in project beast-mcmc by beast-dev.

the class DirtyLikelihoodOperatorParser method parseXMLObject.

public Object parseXMLObject(XMLObject xo) throws XMLParseException {
    double weight = xo.getDoubleAttribute(MCMCOperator.WEIGHT);
    Likelihood likelihood = (Likelihood) xo.getChild(Likelihood.class);
    return new DirtyLikelihoodOperator(likelihood, weight);
}

Example 24 with Likelihood

use of dr.inference.model.Likelihood in project beast-mcmc by beast-dev.

the class ARGAddRemoveOperatorTest method flatPriorTester.

private void flatPriorTester(ARGModel arg, int chainLength, int sampleTreeEvery, double nodeCountSetting, double rootHeightAlpha, double rootHeightBeta, int maxCount) throws IOException, Importer.ImportException {
    MCMC mcmc = new MCMC("mcmc1");
    MCMCOptions options = new MCMCOptions(chainLength);
    //        double nodeCountSetting = 2.0;
    //        double rootHeightAlpha = 100;
    //        double rootHeightBeta = 0.5;
    OperatorSchedule schedule = getSchedule(arg);
    ARGUniformPrior uniformPrior = new ARGUniformPrior(arg, maxCount, arg.getExternalNodeCount());
    PoissonDistribution poisson = new PoissonDistribution(nodeCountSetting);
    DistributionLikelihood nodeCountPrior = new DistributionLikelihood(poisson, 0.0);
    ARGReassortmentNodeCountStatistic nodeCountStatistic = new ARGReassortmentNodeCountStatistic("nodeCount", arg);
    nodeCountPrior.addData(nodeCountStatistic);
    DistributionLikelihood rootPrior = new DistributionLikelihood(new GammaDistribution(rootHeightAlpha, rootHeightBeta), 0.0);
    CompoundParameter rootHeight = (CompoundParameter) arg.createNodeHeightsParameter(true, false, false);
    rootPrior.addData(rootHeight);
    List<Likelihood> likelihoods = new ArrayList<Likelihood>();
    likelihoods.add(uniformPrior);
    likelihoods.add(rootPrior);
    likelihoods.add(nodeCountPrior);
    CompoundLikelihood compoundLikelihood = new CompoundLikelihood(1, likelihoods);
    compoundLikelihood.setId("likelihood1");
    MCLogger[] loggers = new MCLogger[3];
    loggers[0] = new MCLogger(new TabDelimitedFormatter(System.out), 10000, false);
    loggers[0].add(compoundLikelihood);
    loggers[0].add(arg);
    File file = new File("test.args");
    file.deleteOnExit();
    FileOutputStream out = new FileOutputStream(file);
    loggers[1] = new ARGLogger(arg, new TabDelimitedFormatter(out), sampleTreeEvery, "test");
    ArrayLogFormatter formatter = new ArrayLogFormatter(false);
    loggers[2] = new MCLogger(formatter, sampleTreeEvery, false);
    loggers[2].add(arg);
    arg.getRootHeightParameter().setId("root");
    loggers[2].add(arg.getRootHeightParameter());
    mcmc.setShowOperatorAnalysis(true);
    mcmc.init(options, compoundLikelihood, schedule, loggers);
    mcmc.run();
    out.flush();
    out.close();
    List<Trace> traces = formatter.getTraces();
    //        Set<String> uniqueTrees = new HashSet<String>();
    //
    //        NexusImporter importer = new NexusImporter(new FileReader(file));
    //        while (importer.hasTree()) {
    //            Tree t = importer.importNextTree();
    //            uniqueTrees.add(Tree.Utils.uniqueNewick(t, t.getRoot()));
    //        }
    //
    //        TestCase.assertEquals(numTopologies, uniqueTrees.size());            List<Trace> traces = formatter.getTraces();
    ArrayTraceList traceList = new ArrayTraceList("ARGTest", traces, 0);
    for (int i = 1; i < traces.size(); i++) {
        traceList.analyseTrace(i);
    }
    TraceCorrelation nodeCountStats = traceList.getCorrelationStatistics(1);
    TraceCorrelation rootHeightStats = traceList.getCorrelationStatistics(4);
    assertExpectation("nodeCount", nodeCountStats, poisson.truncatedMean(maxCount));
    assertExpectation(TreeModelParser.ROOT_HEIGHT, rootHeightStats, rootHeightAlpha * rootHeightBeta);
}

Example 25 with Likelihood

use of dr.inference.model.Likelihood in project beast-mcmc by beast-dev.

the class TestCalibratedYuleModel method yuleTester.

private void yuleTester(TreeModel treeModel, OperatorSchedule schedule, Parameter brParameter, double S, int chainLength) throws IOException, TreeUtils.MissingTaxonException {
    MCMC mcmc = new MCMC("mcmc1");
    MCMCOptions options = new MCMCOptions(chainLength);
    TreeLengthStatistic tls = new TreeLengthStatistic(TL, treeModel);
    TreeHeightStatistic rootHeight = new TreeHeightStatistic(TREE_HEIGHT, treeModel);
    SpeciationModel speciationModel = new BirthDeathGernhard08Model("yule", brParameter, null, null, BirthDeathGernhard08Model.TreeType.UNSCALED, Units.Type.SUBSTITUTIONS, false);
    Likelihood speciationLikelihood = new SpeciationLikelihood(treeModel, speciationModel, "yule.like");
    Taxa halfTaxa = new Taxa();
    for (int i = 0; i < taxa.getTaxonCount() / 2; i++) {
        halfTaxa.addTaxon(new Taxon("T" + Integer.toString(i)));
    }
    TMRCAStatistic tmrca = new TMRCAStatistic("tmrca(halfTaxa)", treeModel, halfTaxa, false, false);
    DistributionLikelihood logNormalLikelihood = new DistributionLikelihood(new LogNormalDistribution(M, S), // meanInRealSpace="false"
    0);
    logNormalLikelihood.addData(tmrca);
    MonophylyStatistic monophylyStatistic = new MonophylyStatistic("monophyly(halfTaxa)", treeModel, halfTaxa, null);
    BooleanLikelihood booleanLikelihood = new BooleanLikelihood();
    booleanLikelihood.addData(monophylyStatistic);
    //CompoundLikelihood
    List<Likelihood> likelihoods = new ArrayList<Likelihood>();
    likelihoods.add(speciationLikelihood);
    likelihoods.add(logNormalLikelihood);
    likelihoods.add(booleanLikelihood);
    Likelihood prior = new CompoundLikelihood(0, likelihoods);
    prior.setId(CompoundLikelihoodParser.PRIOR);
    ArrayLogFormatter logformatter = new ArrayLogFormatter(false);
    MCLogger[] loggers = new MCLogger[1];
    loggers[0] = new MCLogger(logformatter, (int) (options.getChainLength() / 10000), false);
    loggers[0].add(speciationLikelihood);
    loggers[0].add(rootHeight);
    loggers[0].add(tmrca);
    loggers[0].add(tls);
    loggers[0].add(brParameter);
    mcmc.setShowOperatorAnalysis(false);
    mcmc.init(options, prior, schedule, loggers);
    mcmc.run();
    List<Trace> traces = logformatter.getTraces();
    ArrayTraceList traceList = new ArrayTraceList("yuleModelTest", traces, 1000);
    for (int i = 1; i < traces.size(); i++) {
        traceList.analyseTrace(i);
    }
    NumberFormatter formatter = new NumberFormatter(8);
    TraceCorrelation tlStats = traceList.getCorrelationStatistics(traceList.getTraceIndex(TL));
    TraceCorrelation treeHeightStats = traceList.getCorrelationStatistics(traceList.getTraceIndex("tmrca(halfTaxa)"));
    //        out.write("tmrcaHeight = \t");
    out.write(formatter.format(treeHeightStats.getMean()));
    out.write("\t");
    double expectedNodeHeight = Math.pow(Math.E, (M + (Math.pow(S, 2) / 2)));
    //        out.write("expectation = \t");
    out.write(formatter.format(expectedNodeHeight));
    out.write("\t");
    double error = Math.abs((treeHeightStats.getMean() - expectedNodeHeight) / expectedNodeHeight);
    NumberFormat percentFormatter = NumberFormat.getNumberInstance();
    percentFormatter.setMinimumFractionDigits(5);
    percentFormatter.setMinimumFractionDigits(5);
    //        out.write("error = \t");
    out.write(percentFormatter.format(error));
    out.write("\t");
    //        out.write("tl.ess = \t");
    out.write(Double.toString(tlStats.getESS()));
    System.out.println("tmrcaHeight = " + formatter.format(treeHeightStats.getMean()) + ";  expectation = " + formatter.format(expectedNodeHeight) + ";  error = " + percentFormatter.format(error) + ";  tl.ess = " + tlStats.getESS());
}