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

Example 21 with DefaultBranchRateModel

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

the class BranchRateGradientParser method parseTreeDataLikelihood.

private GradientWrtParameterProvider parseTreeDataLikelihood(TreeDataLikelihood treeDataLikelihood, String traitName, boolean useHessian) throws XMLParseException {
    BranchRateModel branchRateModel = treeDataLikelihood.getBranchRateModel();
    if (branchRateModel instanceof DefaultBranchRateModel || branchRateModel instanceof ArbitraryBranchRates) {
        Parameter branchRates = null;
        if (branchRateModel instanceof ArbitraryBranchRates) {
            branchRates = ((ArbitraryBranchRates) branchRateModel).getRateParameter();
        }
        DataLikelihoodDelegate delegate = treeDataLikelihood.getDataLikelihoodDelegate();
        if (delegate instanceof ContinuousDataLikelihoodDelegate) {
            ContinuousDataLikelihoodDelegate continuousData = (ContinuousDataLikelihoodDelegate) delegate;
            return new BranchRateGradient(traitName, treeDataLikelihood, continuousData, branchRates);
        } else if (delegate instanceof BeagleDataLikelihoodDelegate) {
            BeagleDataLikelihoodDelegate beagleData = (BeagleDataLikelihoodDelegate) delegate;
            if (branchRateModel instanceof LocalBranchRates) {
                return new LocalBranchRateGradientForDiscreteTrait(traitName, treeDataLikelihood, beagleData, branchRates, useHessian);
            } else {
                return new BranchRateGradientForDiscreteTrait(traitName, treeDataLikelihood, beagleData, branchRates, useHessian);
            }
        } else {
            throw new XMLParseException("Unknown likelihood delegate type");
        }
    } else {
        throw new XMLParseException("Only implemented for an arbitrary rates model");
    }
}

Example 22 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 23 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 24 with DefaultBranchRateModel

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

the class ContinuousDataLikelihoodDelegateTest method testLikelihoodFullIOU.

public void testLikelihoodFullIOU() {
    System.out.println("\nTest Likelihood using Full IOU:");
    // Diffusion
    List<BranchRateModel> optimalTraitsModels = new ArrayList<BranchRateModel>();
    optimalTraitsModels.add(new StrictClockBranchRates(new Parameter.Default("rate.1", new double[] { 1.0 })));
    optimalTraitsModels.add(new StrictClockBranchRates(new Parameter.Default("rate.2", new double[] { 2.0 })));
    optimalTraitsModels.add(new StrictClockBranchRates(new Parameter.Default("rate.3", new double[] { -2.0 })));
    Parameter[] strengthOfSelectionParameters = new Parameter[3];
    strengthOfSelectionParameters[0] = new Parameter.Default(new double[] { 0.5, 0.5, 0.0 });
    strengthOfSelectionParameters[1] = new Parameter.Default(new double[] { 0.2, 5, 0.1 });
    strengthOfSelectionParameters[2] = new Parameter.Default(new double[] { 0.0, 1.0, 10.0 });
    MatrixParameter strengthOfSelectionMatrixParam = new MatrixParameter("strengthOfSelectionMatrix", strengthOfSelectionParameters);
    DiffusionProcessDelegate diffusionProcessDelegate = new IntegratedOUDiffusionModelDelegate(treeModel, diffusionModel, optimalTraitsModels, new MultivariateElasticModel(strengthOfSelectionMatrixParam));
    // Rates
    ContinuousRateTransformation rateTransformation = new ContinuousRateTransformation.Default(treeModel, true, false);
    BranchRateModel rateModel = new DefaultBranchRateModel();
    // CDL
    ContinuousDataLikelihoodDelegate likelihoodDelegate = new ContinuousDataLikelihoodDelegate(treeModel, diffusionProcessDelegate, dataModelIntegrated, rootPriorIntegrated, rateTransformation, rateModel, false);
    // Likelihood Computation
    TreeDataLikelihood dataLikelihood = new TreeDataLikelihood(likelihoodDelegate, treeModel, rateModel);
    testLikelihood("likelihoodFullIOU", dataLikelihood);
// Conditional moments (preorder)
// testConditionalMoments(dataLikelihood, likelihoodDelegate);
}

Example 25 with DefaultBranchRateModel

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

the class RepeatedMeasureFactorTest method testLikelihoodOU.

public void testLikelihoodOU() {
    System.out.println("\nTest Likelihood using full OU:");
    // Diffusion
    List<BranchRateModel> optimalTraitsModels = new ArrayList<BranchRateModel>();
    optimalTraitsModels.add(new StrictClockBranchRates(new Parameter.Default("rate.1", new double[] { 1.0 })));
    optimalTraitsModels.add(new StrictClockBranchRates(new Parameter.Default("rate.2", new double[] { 2.0 })));
    optimalTraitsModels.add(new StrictClockBranchRates(new Parameter.Default("rate.3", new double[] { -2.0 })));
    optimalTraitsModels.add(new StrictClockBranchRates(new Parameter.Default("rate.4", new double[] { 10.0 })));
    optimalTraitsModels.add(new StrictClockBranchRates(new Parameter.Default("rate.5", new double[] { 20.0 })));
    optimalTraitsModels.add(new StrictClockBranchRates(new Parameter.Default("rate.6", new double[] { -20.0 })));
    Parameter[] strengthOfSelectionParameters = new Parameter[6];
    strengthOfSelectionParameters[0] = new Parameter.Default(new double[] { 1.0, 0.1, 0.0, 0.0, 0.5, 2.0 });
    strengthOfSelectionParameters[1] = new Parameter.Default(new double[] { 0.1, 10., 0.0, 0.0, 0.0, 0.0 });
    strengthOfSelectionParameters[2] = new Parameter.Default(new double[] { 0.0, 0.0, 20., 0.3, 0.0, 0.0 });
    strengthOfSelectionParameters[3] = new Parameter.Default(new double[] { 0.0, 0.0, 0.3, 30., 3.0, 0.0 });
    strengthOfSelectionParameters[4] = new Parameter.Default(new double[] { 1.0, 0.0, 0.0, 3.0, 40., 0.0 });
    strengthOfSelectionParameters[5] = new Parameter.Default(new double[] { 0.0, 0.0, 0.5, 0.0, 0.0, 50. });
    MatrixParameter strengthOfSelectionMatrixParam = new MatrixParameter("strengthOfSelectionMatrix", strengthOfSelectionParameters);
    DiffusionProcessDelegate diffusionProcessDelegate = new OUDiffusionModelDelegate(treeModel, diffusionModel, optimalTraitsModels, new MultivariateElasticModel(strengthOfSelectionMatrixParam));
    // Rates
    ContinuousRateTransformation rateTransformation = new ContinuousRateTransformation.Default(treeModel, false, false);
    BranchRateModel rateModel = new DefaultBranchRateModel();
    // // Factor Model //// *****************************************************************************************
    // CDL
    ContinuousDataLikelihoodDelegate likelihoodDelegateFactors = new ContinuousDataLikelihoodDelegate(treeModel, diffusionProcessDelegate, dataModelFactor, rootPrior, rateTransformation, rateModel, true);
    dataModelFactor.setLikelihoodDelegate(likelihoodDelegateFactors);
    // Likelihood Computation
    TreeDataLikelihood dataLikelihoodFactors = new TreeDataLikelihood(likelihoodDelegateFactors, treeModel, rateModel);
    double logDatumLikelihoodFactor = getLogDatumLikelihood(dataModelFactor);
    double likelihoodFactorData = dataLikelihoodFactors.getLogLikelihood();
    double likelihoodFactorDiffusion = dataModelFactor.getLogLikelihood();
    assertEquals("likelihoodOUFactor", format.format(logDatumLikelihoodFactor), format.format(likelihoodFactorData + likelihoodFactorDiffusion));
    System.out.println("likelihoodOUFactor: " + format.format(logDatumLikelihoodFactor));
    // Simulation
    MathUtils.setSeed(17890826);
    double[] traitsFactors = getConditionalSimulations(dataLikelihoodFactors, likelihoodDelegateFactors, diffusionModel, dataModelFactor, rootPrior, treeModel, rateTransformation);
    System.err.println(new Vector(traitsFactors));
    // // Repeated Measures //// ************************************************************************************
    // CDL
    ContinuousDataLikelihoodDelegate likelihoodDelegateRepMea = new ContinuousDataLikelihoodDelegate(treeModel, diffusionProcessDelegate, dataModelRepeatedMeasures, rootPrior, rateTransformation, rateModel, true);
    // Likelihood Computation
    TreeDataLikelihood dataLikelihoodRepMea = new TreeDataLikelihood(likelihoodDelegateRepMea, treeModel, rateModel);
    double logDatumLikelihoodRepMea = getLogDatumLikelihood(dataLikelihoodRepMea);
    double likelihoodRepMeaDiffusion = dataLikelihoodRepMea.getLogLikelihood();
    assertEquals("likelihoodOURepMea", format.format(logDatumLikelihoodRepMea), format.format(likelihoodRepMeaDiffusion));
    System.out.println("likelihoodOURepMea: " + format.format(logDatumLikelihoodRepMea));
    // Simulation
    MathUtils.setSeed(17890826);
    double[] traitsRepMea = getConditionalSimulations(dataLikelihoodRepMea, likelihoodDelegateRepMea, diffusionModel, dataModelRepeatedMeasures, rootPrior, treeModel, rateTransformation);
    System.err.println(new Vector(traitsRepMea));
    // // Equal ? //// **********************************************************************************************
    assertEquals("likelihoodOURepFactor", format.format(likelihoodFactorData + likelihoodFactorDiffusion), format.format(likelihoodRepMeaDiffusion));
    for (int i = 0; i < traitsFactors.length; i++) {
        assertEquals(format.format(traitsRepMea[i]), format.format(traitsFactors[i]));
    }
    // // Repeated Measures Full //// *******************************************************************************
    // CDL
    ContinuousDataLikelihoodDelegate likelihoodDelegateRepMeaFull = new ContinuousDataLikelihoodDelegate(treeModel, diffusionProcessDelegate, dataModelRepeatedMeasuresFull, rootPrior, rateTransformation, rateModel, true);
    // Likelihood Computation
    TreeDataLikelihood dataLikelihoodRepMeaFull = new TreeDataLikelihood(likelihoodDelegateRepMeaFull, treeModel, rateModel);
    double logDatumLikelihoodRepMeaFull = getLogDatumLikelihood(dataLikelihoodRepMeaFull);
    double likelihoodRepMeaDiffusionFull = dataLikelihoodRepMeaFull.getLogLikelihood();
    assertEquals("likelihoodBMRepMea", format.format(logDatumLikelihoodRepMeaFull), format.format(likelihoodRepMeaDiffusionFull));
    System.out.println("likelihoodBMRepMeaFull: " + format.format(logDatumLikelihoodRepMeaFull));
}