Examples with HKY dr.evomodel.substmodel.nucleotide.HKY used on opensource projects

Example 1 with HKY

use of dr.evomodel.substmodel.nucleotide.HKY in project beast-mcmc by beast-dev.

the class BeagleTreeLikelihood method main.

public static void main(String[] args) {
    try {
        MathUtils.setSeed(666);
        System.out.println("Test case 1: simulateOnePartition");
        int sequenceLength = 1000;
        ArrayList<Partition> partitionsList = new ArrayList<Partition>();
        // create tree
        NewickImporter importer = new NewickImporter("(SimSeq1:73.7468,(SimSeq2:25.256989999999995,SimSeq3:45.256989999999995):18.48981);");
        Tree tree = importer.importTree(null);
        TreeModel treeModel = new TreeModel(tree);
        // create Frequency Model
        Parameter freqs = new Parameter.Default(new double[] { 0.25, 0.25, 0.25, 0.25 });
        FrequencyModel freqModel = new FrequencyModel(Nucleotides.INSTANCE, freqs);
        // create branch model
        Parameter kappa1 = new Parameter.Default(1, 1);
        Parameter kappa2 = new Parameter.Default(1, 1);
        HKY hky1 = new HKY(kappa1, freqModel);
        HKY hky2 = new HKY(kappa2, freqModel);
        HomogeneousBranchModel homogenousBranchSubstitutionModel = new HomogeneousBranchModel(hky1);
        List<SubstitutionModel> substitutionModels = new ArrayList<SubstitutionModel>();
        substitutionModels.add(hky1);
        substitutionModels.add(hky2);
        List<FrequencyModel> freqModels = new ArrayList<FrequencyModel>();
        freqModels.add(freqModel);
        Parameter epochTimes = new Parameter.Default(1, 20);
        // create branch rate model
        Parameter rate = new Parameter.Default(1, 0.001);
        BranchRateModel branchRateModel = new StrictClockBranchRates(rate);
        // create site model
        GammaSiteRateModel siteRateModel = new GammaSiteRateModel("siteModel");
        BranchModel homogeneousBranchModel = new HomogeneousBranchModel(hky1);
        BranchModel epochBranchModel = new EpochBranchModel(treeModel, substitutionModels, epochTimes);
        // create partition
        Partition partition1 = new //
        Partition(//
        treeModel, //
        homogenousBranchSubstitutionModel, //
        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);
        BeagleTreeLikelihood nbtl = new BeagleTreeLikelihood(alignment, treeModel, homogeneousBranchModel, siteRateModel, branchRateModel, null, false, PartialsRescalingScheme.DEFAULT, false);
        System.out.println("nBTL(homogeneous) = " + nbtl.getLogLikelihood());
        nbtl = new BeagleTreeLikelihood(alignment, treeModel, epochBranchModel, siteRateModel, branchRateModel, null, false, PartialsRescalingScheme.DEFAULT, false);
        System.out.println("nBTL(epoch) = " + nbtl.getLogLikelihood());
    } catch (Exception e) {
        e.printStackTrace();
        System.exit(-1);
    }
// END: try-catch block
}

Example 2 with HKY

use of dr.evomodel.substmodel.nucleotide.HKY in project beast-mcmc by beast-dev.

the class DataLikelihoodTester2 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, false, PartialsRescalingScheme.NONE, false, PreOrderSettings.getDefault());
    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, false, PartialsRescalingScheme.NONE, false, PreOrderSettings.getDefault());
    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):");
    try {
        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);
    } catch (DataLikelihoodDelegate.DelegateTypeException dte) {
        System.out.print("Failed to create multiPartitionDataLikelihoodDelegate instance (wrong resource type or no partitions, needs to be CUDA or OpenCL device with multiple partitions)");
    }
    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):");
    try {
        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);
    } catch (DataLikelihoodDelegate.DelegateTypeException dte) {
        System.out.print("Failed to create multiPartitionDataLikelihoodDelegate instance (wrong resource type or no partitions, needs to be CUDA or OpenCL device with multiple partitions)");
    }
    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);
    try {
        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");
    } catch (DataLikelihoodDelegate.DelegateTypeException dte) {
        System.out.print("Failed to create multiPartitionDataLikelihoodDelegate instance (wrong resource type or no partitions, needs to be CUDA or OpenCL device with multiple partitions)");
    }
    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);
    try {
        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");
    } catch (DataLikelihoodDelegate.DelegateTypeException dte) {
        System.out.print("Failed to create multiPartitionDataLikelihoodDelegate instance (wrong resource type or no partitions, needs to be CUDA or OpenCL device with multiple partitions)");
    }
    // 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);
    try {
        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");
    } catch (DataLikelihoodDelegate.DelegateTypeException dte) {
        System.out.print("Failed to create multiPartitionDataLikelihoodDelegate instance (wrong resource type or no partitions, needs to be CUDA or OpenCL device with multiple partitions)");
    }
    // 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, false, PartialsRescalingScheme.NONE, false, PreOrderSettings.getDefault());
    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, false, PartialsRescalingScheme.NONE, false, PreOrderSettings.getDefault());
    TreeDataLikelihood treeDataLikelihoodTwo = new TreeDataLikelihood(dataLikelihoodDelegateTwo, treeModel, branchRateModel);
    logLikelihood = treeDataLikelihoodTwo.getLogLikelihood();
    System.out.println("BeagleDataLikelihoodDelegate logLikelihood partition 2 (kappa = 10) = " + logLikelihood + "\n");
    try {
        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);
    } catch (DataLikelihoodDelegate.DelegateTypeException dte) {
        System.out.print("Failed to create multiPartitionDataLikelihoodDelegate instance (wrong resource type or no partitions, needs to be CUDA or OpenCL device with multiple partitions)");
    }
    hky.setKappa(1.0);
    try {
        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");
    } catch (DataLikelihoodDelegate.DelegateTypeException dte) {
        System.out.print("Failed to create multiPartitionDataLikelihoodDelegate instance (wrong resource type or no partitions, needs to be CUDA or OpenCL device with multiple partitions)");
    }
    patternLists = new ArrayList<PatternList>();
    patternLists.add(patterns);
    patternLists.add(morePatterns);
    try {
        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?");
    } catch (DataLikelihoodDelegate.DelegateTypeException dte) {
        System.out.print("Failed to create multiPartitionDataLikelihoodDelegate instance (wrong resource type or no partitions, needs to be CUDA or OpenCL device with multiple partitions)");
    }
// END ADDITIONAL TEST - Guy Baele
}

Example 3 with HKY

use of dr.evomodel.substmodel.nucleotide.HKY in project beast-mcmc by beast-dev.

the class PartitionData method createBranchModel.

public BranchModel createBranchModel() {
    BranchModel branchModel = null;
    if (this.substitutionModelIndex == 0) {
        // HKY
        Parameter kappa = new Parameter.Default(1, substitutionParameterValues[0]);
        FrequencyModel frequencyModel = this.createFrequencyModel();
        HKY hky = new HKY(kappa, frequencyModel);
        branchModel = new HomogeneousBranchModel(hky);
    } else if (this.substitutionModelIndex == 1) {
        // GTR
        Parameter ac = new Parameter.Default(1, substitutionParameterValues[1]);
        Parameter ag = new Parameter.Default(1, substitutionParameterValues[2]);
        Parameter at = new Parameter.Default(1, substitutionParameterValues[3]);
        Parameter cg = new Parameter.Default(1, substitutionParameterValues[4]);
        Parameter ct = new Parameter.Default(1, substitutionParameterValues[5]);
        Parameter gt = new Parameter.Default(1, substitutionParameterValues[6]);
        FrequencyModel frequencyModel = this.createFrequencyModel();
        GTR gtr = new GTR(ac, ag, at, cg, ct, gt, frequencyModel);
        branchModel = new HomogeneousBranchModel(gtr);
    } else if (this.substitutionModelIndex == 2) {
        // TN93
        Parameter kappa1 = new Parameter.Default(1, substitutionParameterValues[7]);
        Parameter kappa2 = new Parameter.Default(1, substitutionParameterValues[8]);
        FrequencyModel frequencyModel = this.createFrequencyModel();
        TN93 tn93 = new TN93(kappa1, kappa2, frequencyModel);
        branchModel = new HomogeneousBranchModel(tn93);
    } else if (this.substitutionModelIndex == 3) {
        // Yang Codon Model
        FrequencyModel frequencyModel = this.createFrequencyModel();
        Parameter kappa = new Parameter.Default(1, substitutionParameterValues[9]);
        Parameter omega = new Parameter.Default(1, substitutionParameterValues[10]);
        GY94CodonModel yangCodonModel = new GY94CodonModel(Codons.UNIVERSAL, omega, kappa, frequencyModel);
        branchModel = new HomogeneousBranchModel(yangCodonModel);
    } else if (this.substitutionModelIndex == 4) {
        // MG94HKYCodonModel
        FrequencyModel frequencyModel = this.createFrequencyModel();
        Parameter alpha = new Parameter.Default(1, substitutionParameterValues[11]);
        Parameter beta = new Parameter.Default(1, substitutionParameterValues[12]);
        Parameter kappa = new Parameter.Default(1, substitutionParameterValues[13]);
        MG94HKYCodonModel mg94 = new MG94HKYCodonModel(Codons.UNIVERSAL, alpha, beta, kappa, frequencyModel, new CodonOptions());
        branchModel = new HomogeneousBranchModel(mg94);
    } else if (this.substitutionModelIndex == 5) {
        // Blosum62
        FrequencyModel frequencyModel = this.createFrequencyModel();
        EmpiricalRateMatrix rateMatrix = Blosum62.INSTANCE;
        EmpiricalAminoAcidModel empiricalAminoAcidModel = new EmpiricalAminoAcidModel(rateMatrix, frequencyModel);
        branchModel = new HomogeneousBranchModel(empiricalAminoAcidModel);
    } else if (this.substitutionModelIndex == 6) {
        // CPREV
        FrequencyModel frequencyModel = this.createFrequencyModel();
        EmpiricalRateMatrix rateMatrix = CPREV.INSTANCE;
        EmpiricalAminoAcidModel empiricalAminoAcidModel = new EmpiricalAminoAcidModel(rateMatrix, frequencyModel);
        branchModel = new HomogeneousBranchModel(empiricalAminoAcidModel);
    } else if (this.substitutionModelIndex == 7) {
        // Dayhoff
        FrequencyModel frequencyModel = this.createFrequencyModel();
        EmpiricalRateMatrix rateMatrix = Dayhoff.INSTANCE;
        EmpiricalAminoAcidModel empiricalAminoAcidModel = new EmpiricalAminoAcidModel(rateMatrix, frequencyModel);
        branchModel = new HomogeneousBranchModel(empiricalAminoAcidModel);
    } else if (this.substitutionModelIndex == 8) {
        // JTT
        FrequencyModel frequencyModel = this.createFrequencyModel();
        EmpiricalRateMatrix rateMatrix = JTT.INSTANCE;
        EmpiricalAminoAcidModel empiricalAminoAcidModel = new EmpiricalAminoAcidModel(rateMatrix, frequencyModel);
        branchModel = new HomogeneousBranchModel(empiricalAminoAcidModel);
    } else if (this.substitutionModelIndex == 9) {
        // LG
        FrequencyModel frequencyModel = this.createFrequencyModel();
        EmpiricalRateMatrix rateMatrix = LG.INSTANCE;
        EmpiricalAminoAcidModel empiricalAminoAcidModel = new EmpiricalAminoAcidModel(rateMatrix, frequencyModel);
        branchModel = new HomogeneousBranchModel(empiricalAminoAcidModel);
    } else if (this.substitutionModelIndex == 10) {
        // MTREV
        FrequencyModel frequencyModel = this.createFrequencyModel();
        EmpiricalRateMatrix rateMatrix = MTREV.INSTANCE;
        EmpiricalAminoAcidModel empiricalAminoAcidModel = new EmpiricalAminoAcidModel(rateMatrix, frequencyModel);
        branchModel = new HomogeneousBranchModel(empiricalAminoAcidModel);
    } else if (this.substitutionModelIndex == 11) {
        // WAG
        FrequencyModel frequencyModel = this.createFrequencyModel();
        EmpiricalRateMatrix rateMatrix = WAG.INSTANCE;
        EmpiricalAminoAcidModel empiricalAminoAcidModel = new EmpiricalAminoAcidModel(rateMatrix, frequencyModel);
        branchModel = new HomogeneousBranchModel(empiricalAminoAcidModel);
    } else {
        System.out.println("Not yet implemented");
    }
    return branchModel;
}

Example 4 with HKY

use of dr.evomodel.substmodel.nucleotide.HKY in project beast-mcmc by beast-dev.

the class BeagleSeqSimTest method simulateTwoPartitions.

// END: simulateOnePartition
static void simulateTwoPartitions() {
    try {
        System.out.println("Test case 3: simulateTwoPartitions");
        MathUtils.setSeed(666);
        int sequenceLength = 11;
        ArrayList<Partition> partitionsList = new ArrayList<Partition>();
        // create tree
        NewickImporter importer = new NewickImporter("(SimSeq1:73.7468,(SimSeq2:25.256989999999995,SimSeq3:45.256989999999995):18.48981);");
        Tree tree = importer.importTree(null);
        DefaultTreeModel treeModel = new DefaultTreeModel(tree);
        // create Frequency Model
        Parameter freqs = new Parameter.Default(new double[] { 0.25, 0.25, 0.25, 0.25 });
        FrequencyModel freqModel = new FrequencyModel(Nucleotides.INSTANCE, freqs);
        // create substitution model
        Parameter kappa = new Parameter.Default(1, 10);
        HKY hky = new HKY(kappa, freqModel);
        HomogeneousBranchModel substitutionModel = new HomogeneousBranchModel(hky);
        // create site model
        GammaSiteRateModel siteRateModel = new GammaSiteRateModel("siteModel");
        // create branch rate model
        BranchRateModel branchRateModel = new DefaultBranchRateModel();
        // create partition
        Partition partition1 = new // 
        Partition(// 
        treeModel, // 
        substitutionModel, // 
        siteRateModel, // 
        branchRateModel, // 
        freqModel, // from
        0, // to
        3, // every
        1);
        // create partition
        Partition Partition = new // 
        Partition(// 
        treeModel, // 
        substitutionModel, // 
        siteRateModel, // 
        branchRateModel, // 
        freqModel, // from
        4, // to
        sequenceLength - 1, // every
        1);
        Sequence ancestralSequence = new Sequence();
        ancestralSequence.appendSequenceString("TCAAGTG");
        Partition.setRootSequence(ancestralSequence);
        partitionsList.add(partition1);
        partitionsList.add(Partition);
        // feed to sequence simulator and generate data
        BeagleSequenceSimulator simulator = new BeagleSequenceSimulator(partitionsList);
        System.out.println(simulator.simulate(simulateInPar, false).toString());
    } catch (Exception e) {
        e.printStackTrace();
        System.exit(-1);
    }
// END: try-catch block
}

Example 5 with HKY

use of dr.evomodel.substmodel.nucleotide.HKY in project beast-mcmc by beast-dev.

the class CompleteHistorySimulatorTest method testHKYSimulation.

public void testHKYSimulation() {
    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;
    runSimulation(tree, siteModel, branchRateModel, nSites, new double[][] { register1, register2 }, analyticResult);
}