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

Example 11 with HKY

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

the class BeagleSeqSimTest method simulateOnePartition.

// END: simulate topology
static void simulateOnePartition() {
    try {
        MathUtils.setSeed(666);
        System.out.println("Test case 2: simulateOnePartition");
        int sequenceLength = 10;
        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 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
        sequenceLength - 1, // every
        1);
        Sequence ancestralSequence = new Sequence();
        ancestralSequence.appendSequenceString("TCAAGTGAGG");
        partition1.setRootSequence(ancestralSequence);
        partitionsList.add(partition1);
        // feed to sequence simulator and generate data
        BeagleSequenceSimulator simulator = new BeagleSequenceSimulator(partitionsList);
        SimpleAlignment alignment = simulator.simulate(simulateInPar, false);
        // alignment.setOutputType(SimpleAlignment.OutputType.NEXUS);
        alignment.setOutputType(SimpleAlignment.OutputType.XML);
        System.out.println(alignment.toString());
    } catch (Exception e) {
        e.printStackTrace();
        System.exit(-1);
    }
// END: try-catch block
}

Example 12 with HKY

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

the class AncestralStateBeagleTreeLikelihoodTest method testJointLikelihood.

public void testJointLikelihood() {
    TreeModel treeModel = new TreeModel("treeModel", tree);
    Sequence[] sequence = new Sequence[3];
    sequence[0] = new Sequence(new Taxon("0"), "A");
    sequence[1] = new Sequence(new Taxon("1"), "C");
    sequence[2] = new Sequence(new Taxon("2"), "C");
    Taxa taxa = new Taxa();
    for (Sequence s : sequence) {
        taxa.addTaxon(s.getTaxon());
    }
    SimpleAlignment alignment = new SimpleAlignment();
    for (Sequence s : sequence) {
        alignment.addSequence(s);
    }
    Parameter mu = new Parameter.Default(1, 1.0);
    Parameter kappa = new Parameter.Default(1, 1.0);
    double[] pi = { 0.25, 0.25, 0.25, 0.25 };
    Parameter freqs = new Parameter.Default(pi);
    FrequencyModel f = new FrequencyModel(Nucleotides.INSTANCE, freqs);
    HKY hky = new HKY(kappa, f);
    GammaSiteRateModel siteRateModel = new GammaSiteRateModel("gammaModel", mu, null, -1, null);
    siteRateModel.setSubstitutionModel(hky);
    BranchModel branchModel = new HomogeneousBranchModel(siteRateModel.getSubstitutionModel());
    BranchRateModel branchRateModel = null;
    AncestralStateBeagleTreeLikelihood treeLikelihood = new AncestralStateBeagleTreeLikelihood(alignment, treeModel, branchModel, siteRateModel, branchRateModel, null, false, PartialsRescalingScheme.DEFAULT, true, null, hky.getDataType(), "stateTag", // useMap = true
    true, false);
    double logLike = treeLikelihood.getLogLikelihood();
    StringBuffer buffer = new StringBuffer();
    //        Tree.Utils.newick(treeModel, treeModel.getRoot(), false, Tree.BranchLengthType.LENGTHS_AS_TIME,
    //                null, null, new NodeAttributeProvider[]{treeLikelihood}, null, null, buffer);
    TreeUtils.newick(treeModel, treeModel.getRoot(), false, TreeUtils.BranchLengthType.LENGTHS_AS_TIME, null, null, new TreeTraitProvider[] { treeLikelihood }, null, buffer);
    System.out.println(buffer);
    System.out.println("t_CA(2) = " + t(false, 2.0));
    System.out.println("t_CC(1) = " + t(true, 1.0));
    double trueValue = 0.25 * t(false, 2.0) * Math.pow(t(true, 1.0), 3.0);
    assertEquals(logLike, Math.log(trueValue), 1e-6);
}

Example 13 with HKY

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

the class MarkovJumpsTest method testMarkovJumps.

public void testMarkovJumps() {
    MathUtils.setSeed(666);
    createAlignment(sequencesTwo, Nucleotides.INSTANCE);
    try {
        //            createSpecifiedTree("((human:1,chimp:1):1,gorilla:2)");
        createSpecifiedTree("(human:1,chimp:1)");
    } catch (Exception e) {
        throw new RuntimeException("Unable to parse Newick tree");
    }
    //substitutionModel
    Parameter freqs = new Parameter.Default(new double[] { 0.40, 0.25, 0.25, 0.10 });
    Parameter kappa = new Parameter.Default(HKYParser.KAPPA, 10.0, 0, 100);
    FrequencyModel f = new FrequencyModel(Nucleotides.INSTANCE, freqs);
    HKY hky = new HKY(kappa, f);
    //siteModel
    //        double alpha = 0.5;
    Parameter mu = new Parameter.Default(GammaSiteModelParser.MUTATION_RATE, 0.5, 0, Double.POSITIVE_INFINITY);
    //        Parameter pInv = new Parameter.Default("pInv", 0.5, 0, 1);
    Parameter pInv = null;
    GammaSiteRateModel siteRateModel = new GammaSiteRateModel("gammaModel", mu, null, -1, pInv);
    siteRateModel.setSubstitutionModel(hky);
    //treeLikelihood
    SitePatterns patterns = new SitePatterns(alignment, null, 0, -1, 1, true);
    BranchModel branchModel = new HomogeneousBranchModel(siteRateModel.getSubstitutionModel());
    BranchRateModel branchRateModel = null;
    MarkovJumpsBeagleTreeLikelihood mjTreeLikelihood = new MarkovJumpsBeagleTreeLikelihood(patterns, treeModel, branchModel, siteRateModel, branchRateModel, null, false, PartialsRescalingScheme.AUTO, true, null, hky.getDataType(), "stateTag", // use MAP
    false, // return ML
    true, // use uniformization
    false, false, 1000);
    int nRegisters = registerValues.length;
    int nSim = 10000;
    for (int i = 0; i < nRegisters; i++) {
        Parameter registerParameter = new Parameter.Default(registerValues[i]);
        registerParameter.setId(registerTages[i]);
        mjTreeLikelihood.addRegister(registerParameter, registerTypes[i], registerScales[i]);
    }
    double logLikelihood = mjTreeLikelihood.getLogLikelihood();
    System.out.println("logLikelihood = " + logLikelihood);
    double[] averages = new double[nRegisters];
    for (int i = 0; i < nSim; i++) {
        for (int r = 0; r < nRegisters; r++) {
            double[][] values = mjTreeLikelihood.getMarkovJumpsForRegister(treeModel, r);
            for (double[] value : values) {
                averages[r] += value[0];
            }
        }
        mjTreeLikelihood.makeDirty();
    }
    for (int r = 0; r < nRegisters; r++) {
        averages[r] /= (double) nSim;
        System.out.print(" " + averages[r]);
    }
    System.out.println("");
    assertEquals(valuesFromR, averages, 1E-2);
}

Example 14 with HKY

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

the class TinyTest method testTiny.

public void testTiny() {
    createAlignment(sequences, Nucleotides.INSTANCE);
    try {
        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.println("\nTest Likelihood using JC69:");
    //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);
    siteRateModel.setSubstitutionModel(hky);
    Parameter mu = new Parameter.Default(GammaSiteModelParser.MUTATION_RATE, 1.0, 0, Double.POSITIVE_INFINITY);
    siteRateModel.setRelativeRateParameter(mu);
// @todo update to use latest beagle
//treeLikelihood
//        SitePatterns patterns = new SitePatterns(alignment, null, 0, -1, 1, true);
//
//        BranchSubstitutionModel branchSubstitutionModel = new HomogenousBranchSubstitutionModel(
//                siteRateModel.getSubstitutionModel(),
//                siteRateModel.getSubstitutionModel().getFrequencyModel());
//
//        BranchRateModel branchRateModel = null;
//
//
//        OldBeagleTreeLikelihood treeLikelihood = new OldBeagleTreeLikelihood(
//                patterns,
//                treeModel,
//                branchSubstitutionModel,
//                siteRateModel,
//                branchRateModel,
//                null,
//                false, PartialsRescalingScheme.AUTO);
//
//        double logLikelihood = treeLikelihood.getLogLikelihood();
//
//        System.out.println("logLikelihood = " + logLikelihood);
//
//        assertEquals(-1504.51794, logLikelihood, 1E-3);
}

Example 15 with HKY

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

the class BeagleBranchLikelihood method main.

// END: LikelihoodColumn class
// ////////////
// ---TEST---//
// ////////////
public static void main(String[] args) {
    try {
        MathUtils.setSeed(666);
        int sequenceLength = 1000;
        ArrayList<Partition> partitionsList = new ArrayList<Partition>();
        // create tree
        NewickImporter importer = new NewickImporter("((SimSeq1:22.0,SimSeq2:22.0):12.0,(SimSeq3:23.1,SimSeq4:23.1):10.899999999999999);");
        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);
        HKY hky1 = new HKY(kappa1, freqModel);
        BranchModel homogeneousBranchModel = new HomogeneousBranchModel(hky1);
        List<SubstitutionModel> substitutionModels = new ArrayList<SubstitutionModel>();
        substitutionModels.add(hky1);
        List<FrequencyModel> freqModels = new ArrayList<FrequencyModel>();
        freqModels.add(freqModel);
        // create branch rate model
        Parameter rate = new Parameter.Default(1, 1.000);
        BranchRateModel branchRateModel = new StrictClockBranchRates(rate);
        // create site model
        GammaSiteRateModel siteRateModel = new GammaSiteRateModel("siteModel");
        // create partition
        Partition partition1 = new //
        Partition(//
        treeModel, //
        homogeneousBranchModel, //
        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);
        System.out.println(alignment);
        BeagleTreeLikelihood btl = new BeagleTreeLikelihood(alignment, treeModel, homogeneousBranchModel, siteRateModel, branchRateModel, null, false, PartialsRescalingScheme.DEFAULT, true);
        System.out.println("BTL(homogeneous) = " + btl.getLogLikelihood());
        BeagleBranchLikelihood bbl = new BeagleBranchLikelihood(alignment, treeModel, homogeneousBranchModel, siteRateModel, freqModel, branchRateModel);
        int branchIndex = 4;
        System.out.println(bbl.getBranchLogLikelihood(branchIndex));
        bbl.finalizeBeagle();
    } catch (Exception e) {
        e.printStackTrace();
        System.exit(-1);
    } catch (Throwable e) {
        e.printStackTrace();
        System.exit(-1);
    }
// END: try-catch block
}