Examples with DefaultBranchRateModel dr.evomodel.branchratemodel.DefaultBranchRateModel used on opensource projects

Example 16 with DefaultBranchRateModel

use of dr.evomodel.branchratemodel.DefaultBranchRateModel in project beast-mcmc by beast-dev.

the class LineageSpecificBranchModel method main.

// END: acceptState
public static void main(String[] args) {
    try {
        // the seed of the BEAST
        MathUtils.setSeed(666);
        // create tree
        NewickImporter importer = new NewickImporter("(SimSeq1:73.7468,(SimSeq2:25.256989999999995,SimSeq3:45.256989999999995):18.48981);");
        TreeModel tree = new TreeModel(importer.importTree(null));
        // create site model
        GammaSiteRateModel siteRateModel = new GammaSiteRateModel("siteModel");
        // create branch rate model
        BranchRateModel branchRateModel = new DefaultBranchRateModel();
        int sequenceLength = 10;
        ArrayList<Partition> partitionsList = new ArrayList<Partition>();
        // create Frequency Model
        Parameter freqs = new Parameter.Default(new double[] { //
        0.0163936, //
        0.01639344, //
        0.01639344, //
        0.01639344, //
        0.01639344, //
        0.01639344, //
        0.01639344, //
        0.01639344, //
        0.01639344, //
        0.01639344, //
        0.01639344, //
        0.01639344, //
        0.01639344, //
        0.01639344, //
        0.01639344, //
        0.01639344, //
        0.01639344, //
        0.01639344, //
        0.01639344, //
        0.01639344, //
        0.01639344, //
        0.01639344, //
        0.01639344, //
        0.01639344, //
        0.01639344, //
        0.01639344, //
        0.01639344, //
        0.01639344, //
        0.01639344, //
        0.01639344, //
        0.01639344, //
        0.01639344, //
        0.01639344, //
        0.01639344, //
        0.01639344, //
        0.01639344, //
        0.01639344, //
        0.01639344, //
        0.01639344, //
        0.01639344, //
        0.01639344, //
        0.01639344, //
        0.01639344, //
        0.01639344, //
        0.01639344, //
        0.01639344, //
        0.01639344, //
        0.01639344, //
        0.01639344, //
        0.01639344, //
        0.01639344, //
        0.01639344, //
        0.01639344, //
        0.01639344, //
        0.01639344, //
        0.01639344, //
        0.01639344, //
        0.01639344, //
        0.01639344, //
        0.01639344, //
        0.01639344 });
        FrequencyModel freqModel = new FrequencyModel(Codons.UNIVERSAL, freqs);
        // create substitution model
        Parameter alpha = new Parameter.Default(1, 10);
        Parameter beta = new Parameter.Default(1, 5);
        MG94CodonModel mg94 = new MG94CodonModel(Codons.UNIVERSAL, alpha, beta, freqModel);
        HomogeneousBranchModel substitutionModel = new HomogeneousBranchModel(mg94);
        // create partition
        Partition partition1 = new //
        Partition(//
        tree, //
        substitutionModel, //
        siteRateModel, //
        branchRateModel, //
        freqModel, // from
        0, // to
        sequenceLength - 1, // every
        1);
        partitionsList.add(partition1);
        // feed to sequence simulator and generate data
        BeagleSequenceSimulator simulator = new BeagleSequenceSimulator(partitionsList);
        Alignment alignment = simulator.simulate(false, false);
        ConvertAlignment convert = new ConvertAlignment(Nucleotides.INSTANCE, GeneticCode.UNIVERSAL, alignment);
        List<SubstitutionModel> substModels = new ArrayList<SubstitutionModel>();
        for (int i = 0; i < 2; i++) {
            //				alpha = new Parameter.Default(1, 10 );
            //				beta = new Parameter.Default(1, 5 );
            //				mg94 = new MG94CodonModel(Codons.UNIVERSAL, alpha, beta,
            //						freqModel);
            substModels.add(mg94);
        }
        Parameter uCategories = new Parameter.Default(2, 0);
        //            CountableBranchCategoryProvider provider = new CountableBranchCategoryProvider.IndependentBranchCategoryModel(tree, uCategories);
        LineageSpecificBranchModel branchSpecific = new //provider, 
        LineageSpecificBranchModel(//provider, 
        tree, //provider, 
        freqModel, //provider, 
        substModels, uCategories);
        BeagleTreeLikelihood like = new //
        BeagleTreeLikelihood(//
        convert, //
        tree, //
        branchSpecific, //
        siteRateModel, //
        branchRateModel, //
        null, //
        false, PartialsRescalingScheme.DEFAULT, true);
        BeagleTreeLikelihood gold = new //
        BeagleTreeLikelihood(//
        convert, //
        tree, //
        substitutionModel, //
        siteRateModel, //
        branchRateModel, //
        null, //
        false, PartialsRescalingScheme.DEFAULT, true);
        System.out.println("likelihood (gold) = " + gold.getLogLikelihood());
        System.out.println("likelihood = " + like.getLogLikelihood());
    } catch (Exception e) {
        e.printStackTrace();
    }
}

Example 17 with DefaultBranchRateModel

use of dr.evomodel.branchratemodel.DefaultBranchRateModel in project beast-mcmc by beast-dev.

the class DataLikelihoodTester method main.

public static void main(String[] args) {
    // turn off logging to avoid screen noise...
    Logger logger = Logger.getLogger("dr");
    logger.setUseParentHandlers(false);
    SimpleAlignment alignment = createAlignment(sequences, Nucleotides.INSTANCE);
    TreeModel treeModel;
    try {
        treeModel = createSpecifiedTree("((human:0.1,chimp:0.1):0.1,gorilla:0.2)");
    } catch (Exception e) {
        throw new RuntimeException("Unable to parse Newick tree");
    }
    System.out.print("\nTest BeagleTreeLikelihood (kappa = 1): ");
    //substitutionModel
    Parameter freqs = new Parameter.Default(new double[] { 0.25, 0.25, 0.25, 0.25 });
    Parameter kappa = new Parameter.Default(HKYParser.KAPPA, 1.0, 0, 100);
    FrequencyModel f = new FrequencyModel(Nucleotides.INSTANCE, freqs);
    HKY hky = new HKY(kappa, f);
    //siteModel
    double alpha = 0.5;
    GammaSiteRateModel siteRateModel = new GammaSiteRateModel("gammaModel", alpha, 4);
    //        GammaSiteRateModel siteRateModel = new GammaSiteRateModel("siteRateModel");
    siteRateModel.setSubstitutionModel(hky);
    Parameter mu = new Parameter.Default(GammaSiteModelParser.SUBSTITUTION_RATE, 1.0, 0, Double.POSITIVE_INFINITY);
    siteRateModel.setRelativeRateParameter(mu);
    FrequencyModel f2 = new FrequencyModel(Nucleotides.INSTANCE, freqs);
    Parameter kappa2 = new Parameter.Default(HKYParser.KAPPA, 10.0, 0, 100);
    HKY hky2 = new HKY(kappa2, f2);
    GammaSiteRateModel siteRateModel2 = new GammaSiteRateModel("gammaModel", alpha, 4);
    siteRateModel2.setSubstitutionModel(hky2);
    siteRateModel2.setRelativeRateParameter(mu);
    //treeLikelihood
    SitePatterns patterns = new SitePatterns(alignment, null, 0, -1, 1, true);
    BranchModel branchModel = new HomogeneousBranchModel(siteRateModel.getSubstitutionModel(), siteRateModel.getSubstitutionModel().getFrequencyModel());
    BranchModel branchModel2 = new HomogeneousBranchModel(siteRateModel2.getSubstitutionModel(), siteRateModel2.getSubstitutionModel().getFrequencyModel());
    BranchRateModel branchRateModel = new DefaultBranchRateModel();
    BeagleTreeLikelihood treeLikelihood = new BeagleTreeLikelihood(patterns, treeModel, branchModel, siteRateModel, branchRateModel, null, false, PartialsRescalingScheme.AUTO, true);
    double logLikelihood = treeLikelihood.getLogLikelihood();
    System.out.println("logLikelihood = " + logLikelihood);
    System.out.print("\nTest BeagleDataLikelihoodDelegate (kappa = 1): ");
    BeagleDataLikelihoodDelegate dataLikelihoodDelegate = new BeagleDataLikelihoodDelegate(treeModel, patterns, branchModel, siteRateModel, false, PartialsRescalingScheme.NONE, false);
    TreeDataLikelihood treeDataLikelihood = new TreeDataLikelihood(dataLikelihoodDelegate, treeModel, branchRateModel);
    logLikelihood = treeDataLikelihood.getLogLikelihood();
    System.out.println("logLikelihood = " + logLikelihood);
    hky.setKappa(5.0);
    System.out.print("\nTest BeagleDataLikelihoodDelegate (kappa = 5): ");
    logLikelihood = treeDataLikelihood.getLogLikelihood();
    System.out.println("logLikelihood = " + logLikelihood);
    System.out.print("\nTest BeagleDataLikelihoodDelegate (kappa = 10): ");
    dataLikelihoodDelegate = new BeagleDataLikelihoodDelegate(treeModel, patterns, branchModel2, siteRateModel2, false, PartialsRescalingScheme.NONE, false);
    treeDataLikelihood = new TreeDataLikelihood(dataLikelihoodDelegate, treeModel, branchRateModel);
    logLikelihood = treeDataLikelihood.getLogLikelihood();
    System.out.println("logLikelihood = " + logLikelihood);
    hky2.setKappa(11.0);
    System.out.print("\nTest BeagleDataLikelihoodDelegate (kappa = 11): ");
    logLikelihood = treeDataLikelihood.getLogLikelihood();
    System.out.println("logLikelihood = " + logLikelihood);
    hky.setKappa(1.0);
    hky2.setKappa(10.0);
    MultiPartitionDataLikelihoodDelegate multiPartitionDataLikelihoodDelegate;
    System.out.print("\nTest MultiPartitionDataLikelihoodDelegate 1 partition (kappa = 1):");
    multiPartitionDataLikelihoodDelegate = new MultiPartitionDataLikelihoodDelegate(treeModel, Collections.singletonList((PatternList) patterns), Collections.singletonList((BranchModel) branchModel), Collections.singletonList((SiteRateModel) siteRateModel), true, PartialsRescalingScheme.NONE, false);
    treeDataLikelihood = new TreeDataLikelihood(multiPartitionDataLikelihoodDelegate, treeModel, branchRateModel);
    logLikelihood = treeDataLikelihood.getLogLikelihood();
    System.out.println("logLikelihood = " + logLikelihood);
    hky.setKappa(5.0);
    System.out.print("\nTest MultiPartitionDataLikelihoodDelegate 1 partition (kappa = 5):");
    logLikelihood = treeDataLikelihood.getLogLikelihood();
    System.out.println("logLikelihood = " + logLikelihood);
    hky.setKappa(1.0);
    System.out.print("\nTest MultiPartitionDataLikelihoodDelegate 1 partition (kappa = 10):");
    multiPartitionDataLikelihoodDelegate = new MultiPartitionDataLikelihoodDelegate(treeModel, Collections.singletonList((PatternList) patterns), Collections.singletonList((BranchModel) branchModel2), Collections.singletonList((SiteRateModel) siteRateModel2), true, PartialsRescalingScheme.NONE, false);
    treeDataLikelihood = new TreeDataLikelihood(multiPartitionDataLikelihoodDelegate, treeModel, branchRateModel);
    logLikelihood = treeDataLikelihood.getLogLikelihood();
    System.out.println("logLikelihood = " + logLikelihood);
    System.out.print("\nTest MultiPartitionDataLikelihoodDelegate 2 partitions (kappa = 1, 10): ");
    List<PatternList> patternLists = new ArrayList<PatternList>();
    patternLists.add(patterns);
    patternLists.add(patterns);
    List<SiteRateModel> siteRateModels = new ArrayList<SiteRateModel>();
    siteRateModels.add(siteRateModel);
    siteRateModels.add(siteRateModel2);
    List<BranchModel> branchModels = new ArrayList<BranchModel>();
    branchModels.add(branchModel);
    branchModels.add(branchModel2);
    multiPartitionDataLikelihoodDelegate = new MultiPartitionDataLikelihoodDelegate(treeModel, patternLists, branchModels, siteRateModels, true, PartialsRescalingScheme.NONE, false);
    treeDataLikelihood = new TreeDataLikelihood(multiPartitionDataLikelihoodDelegate, treeModel, branchRateModel);
    logLikelihood = treeDataLikelihood.getLogLikelihood();
    System.out.println("logLikelihood = " + logLikelihood + " (NOT OK: this is 2x the logLikelihood of the 2nd partition)\n\n");
    System.exit(0);
    //START ADDITIONAL TEST #1 - Guy Baele
    System.out.println("-- Test #1 SiteRateModels -- ");
    //alpha in partition 1 reject followed by alpha in partition 2 reject
    System.out.print("Adjust alpha in partition 1: ");
    siteRateModel.setAlpha(0.4);
    logLikelihood = treeDataLikelihood.getLogLikelihood();
    System.out.println("logLikelihood = " + logLikelihood);
    System.out.print("Return alpha in partition 1 to original value: ");
    siteRateModel.setAlpha(0.5);
    logLikelihood = treeDataLikelihood.getLogLikelihood();
    System.out.println("logLikelihood = " + logLikelihood + " (i.e. reject: OK)\n");
    System.out.print("Adjust alpha in partition 2: ");
    siteRateModel2.setAlpha(0.35);
    logLikelihood = treeDataLikelihood.getLogLikelihood();
    System.out.println("logLikelihood = " + logLikelihood);
    System.out.print("Return alpha in partition 2 to original value: ");
    siteRateModel2.setAlpha(0.5);
    logLikelihood = treeDataLikelihood.getLogLikelihood();
    System.out.println("logLikelihood = " + logLikelihood + " (i.e. reject: OK)\n");
    //alpha in partition 1 accept followed by alpha in partition 2 accept
    System.out.print("Adjust alpha in partition 1: ");
    siteRateModel.setAlpha(0.4);
    logLikelihood = treeDataLikelihood.getLogLikelihood();
    System.out.println("logLikelihood = " + logLikelihood);
    System.out.print("Adjust alpha in partition 2: ");
    siteRateModel2.setAlpha(0.35);
    logLikelihood = treeDataLikelihood.getLogLikelihood();
    System.out.println("logLikelihood = " + logLikelihood + " (NOT OK: same logLikelihood as only setting alpha in partition 2)");
    System.out.print("Return alpha in partition 1 to original value: ");
    siteRateModel.setAlpha(0.5);
    logLikelihood = treeDataLikelihood.getLogLikelihood();
    System.out.println("logLikelihood = " + logLikelihood + " (NOT OK: alpha in partition 2 has not been returned to original value yet)");
    System.out.print("Return alpha in partition 2 to original value: ");
    siteRateModel2.setAlpha(0.5);
    logLikelihood = treeDataLikelihood.getLogLikelihood();
    System.out.println("logLikelihood = " + logLikelihood + "\n");
    //adjusting alphas in both partitions without explicitly calling getLogLikelihood() in between
    System.out.print("Adjust both alphas in partitions 1 and 2: ");
    siteRateModel.setAlpha(0.4);
    siteRateModel2.setAlpha(0.35);
    logLikelihood = treeDataLikelihood.getLogLikelihood();
    System.out.println("logLikelihood = " + logLikelihood);
    System.out.print("Return alpha in partition 2 to original value: ");
    siteRateModel2.setAlpha(0.5);
    logLikelihood = treeDataLikelihood.getLogLikelihood();
    System.out.println("logLikelihood = " + logLikelihood + " (NOT OK: alpha in partition 1 has not been returned to original value yet)");
    System.out.print("Return alpha in partition 1 to original value: ");
    siteRateModel.setAlpha(0.5);
    logLikelihood = treeDataLikelihood.getLogLikelihood();
    System.out.println("logLikelihood = " + logLikelihood + "\n\n");
    //END ADDITIONAL TEST - Guy Baele
    //START ADDITIONAL TEST #2 - Guy Baele
    System.out.println("-- Test #2 SiteRateModels -- ");
    logLikelihood = treeDataLikelihood.getLogLikelihood();
    System.out.println("logLikelihood = " + logLikelihood);
    //1 siteRateModel shared across 2 partitions
    siteRateModels = new ArrayList<SiteRateModel>();
    siteRateModels.add(siteRateModel);
    multiPartitionDataLikelihoodDelegate = new MultiPartitionDataLikelihoodDelegate(treeModel, patternLists, branchModels, siteRateModels, true, PartialsRescalingScheme.NONE, false);
    treeDataLikelihood = new TreeDataLikelihood(multiPartitionDataLikelihoodDelegate, treeModel, branchRateModel);
    logLikelihood = treeDataLikelihood.getLogLikelihood();
    System.out.println("logLikelihood = " + logLikelihood + "\n");
    System.out.print("Adjust alpha in shared siteRateModel: ");
    siteRateModel.setAlpha(0.4);
    logLikelihood = treeDataLikelihood.getLogLikelihood();
    System.out.println("logLikelihood = " + logLikelihood + " (NOT OK: same logLikelihood as only adjusted alpha for partition 1)");
    siteRateModel.setAlpha(0.5);
    logLikelihood = treeDataLikelihood.getLogLikelihood();
    System.out.println("logLikelihood = " + logLikelihood + "\n\n");
    //END ADDITIONAL TEST - Guy Baele
    //START ADDITIONAL TEST #3 - Guy Baele
    System.out.println("-- Test #3 SiteRateModels -- ");
    siteRateModel = new GammaSiteRateModel("gammaModel");
    siteRateModel.setSubstitutionModel(hky);
    siteRateModel.setRelativeRateParameter(mu);
    siteRateModel2 = new GammaSiteRateModel("gammaModel2");
    siteRateModel2.setSubstitutionModel(hky2);
    siteRateModel2.setRelativeRateParameter(mu);
    siteRateModels = new ArrayList<SiteRateModel>();
    siteRateModels.add(siteRateModel);
    siteRateModels.add(siteRateModel2);
    multiPartitionDataLikelihoodDelegate = new MultiPartitionDataLikelihoodDelegate(treeModel, patternLists, branchModels, siteRateModels, true, PartialsRescalingScheme.NONE, false);
    treeDataLikelihood = new TreeDataLikelihood(multiPartitionDataLikelihoodDelegate, treeModel, branchRateModel);
    logLikelihood = treeDataLikelihood.getLogLikelihood();
    System.out.println("logLikelihood = " + logLikelihood + "\n");
    System.out.print("Adjust kappa in partition 1: ");
    hky.setKappa(5.0);
    logLikelihood = treeDataLikelihood.getLogLikelihood();
    System.out.println("logLikelihood = " + logLikelihood + " (NOT OK: logLikelihood has not changed?)");
    System.out.print("Return kappa in partition 1 to original value: ");
    hky.setKappa(1.0);
    logLikelihood = treeDataLikelihood.getLogLikelihood();
    System.out.println("logLikelihood = " + logLikelihood + "\n");
    System.out.print("Adjust kappa in partition 2: ");
    hky2.setKappa(11.0);
    logLikelihood = treeDataLikelihood.getLogLikelihood();
    System.out.println("logLikelihood = " + logLikelihood);
    System.out.print("Return kappa in partition 2 to original value: ");
    hky2.setKappa(10.0);
    logLikelihood = treeDataLikelihood.getLogLikelihood();
    System.out.println("logLikelihood = " + logLikelihood + " (i.e. reject: OK)\n\n");
    //END ADDITIONAL TEST - Guy Baele
    //START ADDITIONAL TEST #4 - Guy Baele
    System.out.println("-- Test #4 SiteRateModels -- ");
    SimpleAlignment secondAlignment = createAlignment(moreSequences, Nucleotides.INSTANCE);
    SitePatterns morePatterns = new SitePatterns(secondAlignment, null, 0, -1, 1, true);
    BeagleDataLikelihoodDelegate dataLikelihoodDelegateOne = new BeagleDataLikelihoodDelegate(treeModel, patterns, branchModel, siteRateModel, false, PartialsRescalingScheme.NONE, false);
    TreeDataLikelihood treeDataLikelihoodOne = new TreeDataLikelihood(dataLikelihoodDelegateOne, treeModel, branchRateModel);
    logLikelihood = treeDataLikelihoodOne.getLogLikelihood();
    System.out.println("\nBeagleDataLikelihoodDelegate logLikelihood partition 1 (kappa = 1) = " + logLikelihood);
    hky.setKappa(10.0);
    logLikelihood = treeDataLikelihoodOne.getLogLikelihood();
    System.out.println("BeagleDataLikelihoodDelegate logLikelihood partition 1 (kappa = 10) = " + logLikelihood);
    hky.setKappa(1.0);
    BeagleDataLikelihoodDelegate dataLikelihoodDelegateTwo = new BeagleDataLikelihoodDelegate(treeModel, morePatterns, branchModel2, siteRateModel2, false, PartialsRescalingScheme.NONE, false);
    TreeDataLikelihood treeDataLikelihoodTwo = new TreeDataLikelihood(dataLikelihoodDelegateTwo, treeModel, branchRateModel);
    logLikelihood = treeDataLikelihoodTwo.getLogLikelihood();
    System.out.println("BeagleDataLikelihoodDelegate logLikelihood partition 2 (kappa = 10) = " + logLikelihood + "\n");
    multiPartitionDataLikelihoodDelegate = new MultiPartitionDataLikelihoodDelegate(treeModel, Collections.singletonList((PatternList) patterns), Collections.singletonList((BranchModel) branchModel), Collections.singletonList((SiteRateModel) siteRateModel), true, PartialsRescalingScheme.NONE, false);
    treeDataLikelihood = new TreeDataLikelihood(multiPartitionDataLikelihoodDelegate, treeModel, branchRateModel);
    logLikelihood = treeDataLikelihood.getLogLikelihood();
    System.out.print("Test MultiPartitionDataLikelihoodDelegate 1st partition (kappa = 1):");
    System.out.println("logLikelihood = " + logLikelihood);
    hky.setKappa(10.0);
    logLikelihood = treeDataLikelihood.getLogLikelihood();
    System.out.print("Test MultiPartitionDataLikelihoodDelegate 1st partition (kappa = 10):");
    System.out.println("logLikelihood = " + logLikelihood);
    hky.setKappa(1.0);
    multiPartitionDataLikelihoodDelegate = new MultiPartitionDataLikelihoodDelegate(treeModel, Collections.singletonList((PatternList) morePatterns), Collections.singletonList((BranchModel) branchModel2), Collections.singletonList((SiteRateModel) siteRateModel2), true, PartialsRescalingScheme.NONE, false);
    treeDataLikelihood = new TreeDataLikelihood(multiPartitionDataLikelihoodDelegate, treeModel, branchRateModel);
    logLikelihood = treeDataLikelihood.getLogLikelihood();
    System.out.print("Test MultiPartitionDataLikelihoodDelegate 2nd partition (kappa = 10):");
    System.out.println("logLikelihood = " + logLikelihood + "\n");
    patternLists = new ArrayList<PatternList>();
    patternLists.add(patterns);
    patternLists.add(morePatterns);
    multiPartitionDataLikelihoodDelegate = new MultiPartitionDataLikelihoodDelegate(treeModel, patternLists, branchModels, siteRateModels, true, PartialsRescalingScheme.NONE, false);
    treeDataLikelihood = new TreeDataLikelihood(multiPartitionDataLikelihoodDelegate, treeModel, branchRateModel);
    logLikelihood = treeDataLikelihood.getLogLikelihood();
    System.out.print("Test MultiPartitionDataLikelihoodDelegate 2 partitions (kappa = 1, 10): ");
    System.out.println("logLikelihood = " + logLikelihood + " (NOT OK: should be the sum of both separate logLikelihoods)\nKappa value of partition 2 is used to compute logLikelihood for both partitions?");
//END ADDITIONAL TEST - Guy Baele
}

Example 18 with DefaultBranchRateModel

use of dr.evomodel.branchratemodel.DefaultBranchRateModel in project beast-mcmc by beast-dev.

the class SequenceSimulator method main.

// getDefaultSiteModel
public static void main(String[] args) {
    try {
        int nReplications = 10;
        // create tree
        NewickImporter importer = new NewickImporter("((A:1.0,B:1.0)AB:1.0,(C:1.0,D:1.0)CD:1.0)ABCD;");
        Tree tree = importer.importTree(null);
        // create site model
        SiteModel siteModel = getDefaultSiteModel();
        // create branch rate model
        BranchRateModel branchRateModel = new DefaultBranchRateModel();
        // feed to sequence simulator and generate leaves
        SequenceSimulator treeSimulator = new SequenceSimulator(tree, siteModel, branchRateModel, nReplications);
        Sequence ancestralSequence = new Sequence();
        ancestralSequence.appendSequenceString("TCAGGTCAAG");
        treeSimulator.setAncestralSequence(ancestralSequence);
        System.out.println(treeSimulator.simulate().toString());
    } catch (Exception e) {
        e.printStackTrace();
    }
//END: try-catch block
}

Example 19 with DefaultBranchRateModel

use of dr.evomodel.branchratemodel.DefaultBranchRateModel in project beast-mcmc by beast-dev.

the class MicrosatelliteSimulatorParser method parseXMLObject.

public Object parseXMLObject(XMLObject xo) throws XMLParseException {
    Microsatellite msatDataType = (Microsatellite) xo.getChild(Microsatellite.class);
    Taxa taxa = (Taxa) xo.getChild(Taxa.class);
    Tree tree = (Tree) xo.getChild(Tree.class);
    MicrosatelliteModel msatModel = (MicrosatelliteModel) xo.getChild(MicrosatelliteModel.class);
    BranchRateModel brModel = (BranchRateModel) xo.getChild(BranchRateModel.class);
    if (brModel == null) {
        brModel = new DefaultBranchRateModel();
    }
    MicrosatelliteSimulator msatSim = new MicrosatelliteSimulator(msatDataType, taxa, tree, new GammaSiteModel(msatModel), brModel);
    Patterns patterns = msatSim.simulateMsatPattern();
    String msatPatId = xo.getAttribute("id", "simMsatPat");
    patterns.setId(msatPatId);
    MicrosatellitePatternParser.printDetails(patterns);
    MicrosatellitePatternParser.printMicrosatContent(patterns);
    return patterns;
}

Example 20 with DefaultBranchRateModel

use of dr.evomodel.branchratemodel.DefaultBranchRateModel in project beast-mcmc by beast-dev.

the class VariableBranchCompleteHistorySimulatorTest method testHKYVariableSimulation.

public void testHKYVariableSimulation() {
    System.out.println("Starting HKY variable branch simulation");
    Parameter kappa = new Parameter.Default(1, 2.0);
    double[] pi = { 0.45, 0.05, 0.25, 0.25 };
    Parameter freqs = new Parameter.Default(pi);
    FrequencyModel f = new FrequencyModel(Nucleotides.INSTANCE, freqs);
    HKY hky = new HKY(kappa, f);
    int stateCount = hky.getDataType().getStateCount();
    Parameter mu = new Parameter.Default(1, 0.5);
    Parameter alpha = new Parameter.Default(1, 0.5);
    GammaSiteRateModel siteModel = new GammaSiteRateModel("gammaModel", mu, alpha, 4, null);
    siteModel.setSubstitutionModel(hky);
    BranchRateModel branchRateModel = new DefaultBranchRateModel();
    double analyticResult = TreeUtils.getTreeLength(tree, tree.getRoot()) * mu.getParameterValue(0);
    int nSites = 200;
    double[] register1 = new double[stateCount * stateCount];
    double[] register2 = new double[stateCount * stateCount];
    // Count all jumps
    MarkovJumpsCore.fillRegistrationMatrix(register1, stateCount);
    // Move some jumps from 1 to 2
    register1[1 * stateCount + 2] = 0;
    register2[1 * stateCount + 2] = 1;
    register1[1 * stateCount + 3] = 0;
    register2[1 * stateCount + 3] = 1;
    register1[2 * stateCount + 3] = 0;
    register2[2 * stateCount + 3] = 1;
    double[] branchValues = { 10.0, 10.0, 10.0, 10.0, 10.0 };
    Parameter branchValuesParam = new Parameter.Default(branchValues);
    runSimulation(N, tree, siteModel, branchRateModel, nSites, new double[][] { register1, register2 }, analyticResult, kappa, branchValuesParam);
}