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

Example 26 with DefaultBranchRateModel

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

the class DiffusionGradientTest method setUp.

public void setUp() throws Exception {
    super.setUp();
    delta = 1E-2;
    dimTrait = 6;
    Parameter offDiagonal = new Parameter.Default(new double[] { 0.12, -0.13, 0.14, -0.15, 0.16, -0.12, 0.13, -0.14, 0.15, 0.12, -0.13, 0.14, -0.12, 0.13, 0.12 });
    offDiagonal.addBounds(new Parameter.DefaultBounds(Double.POSITIVE_INFINITY, Double.NEGATIVE_INFINITY, dimTrait * (dimTrait - 1) / 2));
    Parameter diagonal = new Parameter.Default(new double[] { 0.1, 0.2, 0.3, 0.4, 0.5, 0.6 });
    precisionMatrix = new CompoundSymmetricMatrix(diagonal, offDiagonal, true, true);
    Parameter diagonalVar = new Parameter.Default(new double[] { 10.0, 20.0, 30.0, 40.0, 50.0, 60.0 });
    precisionMatrixInv = new CachedMatrixInverse("var", new CompoundSymmetricMatrix(diagonalVar, offDiagonal, true, false));
    diffusionModel = new MultivariateDiffusionModel(precisionMatrix);
    diffusionModelVar = new MultivariateDiffusionModel(precisionMatrixInv);
    // Data
    Parameter[] dataTraits = new Parameter[6];
    dataTraits[0] = new Parameter.Default("human", new double[] { -1.0, 2.0, 3.0, 4.0, 5.0, -6.0 });
    dataTraits[1] = new Parameter.Default("chimp", new double[] { 10.0, 12.0, 14.0, 16.0, 18.0, 20.0 });
    dataTraits[2] = new Parameter.Default("bonobo", new double[] { 0.5, -2.0, 5.5, -5.2, 3.1, 1.1 });
    dataTraits[3] = new Parameter.Default("gorilla", new double[] { 2.0, 5.0, -8.0, -4.0, 3.2, 3.4 });
    dataTraits[4] = new Parameter.Default("orangutan", new double[] { 11.0, 1.0, -1.5, 2.4, -4.2, 6.0 });
    dataTraits[5] = new Parameter.Default("siamang", new double[] { 1.0, 2.5, 4.0, 4.0, -5.2, 1.0 });
    traitParameter = new CompoundParameter("trait", dataTraits);
    traitParameter.setParameterValue(2, 0);
    missingIndices.add(6);
    missingIndices.add(7);
    missingIndices.add(8);
    missingIndices.add(9);
    missingIndices.add(10);
    missingIndices.add(11);
    missingIndices.add(13);
    missingIndices.add(15);
    missingIndices.add(25);
    missingIndices.add(29);
    // Tree
    createAlignment(PRIMATES_TAXON_SEQUENCE, Nucleotides.INSTANCE);
    treeModel = createPrimateTreeModel();
    // Rates
    rateTransformation = new ContinuousRateTransformation.Default(treeModel, false, false);
    rateModel = new DefaultBranchRateModel();
    // // Standard Model //// ***************************************************************************************
    PrecisionType precisionType = PrecisionType.FULL;
    // Root prior
    final double rootVal = fixedRoot ? Double.POSITIVE_INFINITY : 0.1;
    Parameter rootVar = new Parameter.Default("rootVar", rootVal);
    Parameter[] meanRootList = new Parameter[dimTrait];
    double[] meanRootVal = new double[] { -1.0, -3.0, 2.5, -2.5, 1.3, 4.0 };
    for (int i = 0; i < dimTrait; i++) {
        meanRootList[i] = new Parameter.Default("meanRoot." + (i + 1), meanRootVal[i]);
    }
    meanRoot = new CompoundParameter("rootMean", meanRootList);
    rootPrior = new ConjugateRootTraitPrior(meanRoot, rootVar);
    // Data Model
    dataModelMissing = new ContinuousTraitDataModel("dataModel", traitParameter, missingIndices, true, 6, precisionType);
    dataModel = new ContinuousTraitDataModel("dataModel", traitParameter, missingIndices, false, 6, precisionType);
    // // Factor Model //// *****************************************************************************************
    // Error model
    Parameter factorPrecisionParameters = new Parameter.Default("factorPrecision", new double[] { 1.0, 5.0, 0.5, 0.1, 0.2, 0.3 });
    // Loadings
    Parameter[] loadingsParameters = new Parameter[2];
    loadingsParameters[0] = new Parameter.Default(new double[] { 1.0, 2.0, 3.0, 0.0, 0.0, 0.0 });
    loadingsParameters[1] = new Parameter.Default(new double[] { 0.0, 0.0, 0.0, 0.0, 0.5, 1.0 });
    MatrixParameterInterface loadingsMatrixParameters = new MatrixParameter("loadings", loadingsParameters);
    dataModelFactor = new IntegratedFactorAnalysisLikelihood("dataModelFactors", traitParameter, missingIndices, loadingsMatrixParameters, factorPrecisionParameters, 0.0, null);
    // // Repeated Measures Model //// *****************************************************************************************
    Parameter offDiagonalSampling = new Parameter.Default(new double[] { 0.16, -0.15, 0.14, -0.13, 0.12, -0.15, 0.14, -0.13, 0.12, 0.0, 0.0, 0.0, -0.13, 0.12, 0.0 });
    offDiagonalSampling.addBounds(new Parameter.DefaultBounds(Double.POSITIVE_INFINITY, Double.NEGATIVE_INFINITY, dimTrait * (dimTrait - 1) / 2));
    Parameter diagonalSampling = new Parameter.Default(new double[] { 0.1, 0.2, 0.3, 0.2, 0.1, 0.01 });
    samplingPrecision = new CompoundSymmetricMatrix(diagonalSampling, offDiagonalSampling, true, true);
    Parameter diagonalVarSampling = new Parameter.Default(new double[] { 10.0, 20.0, 30.0, 20.0, 10.0, 100.0 });
    samplingPrecisionInv = new CachedMatrixInverse("samplingVar", new CompoundSymmetricMatrix(diagonalVarSampling, offDiagonalSampling, true, false));
    dataModelRepeatedMeasures = new RepeatedMeasuresTraitDataModel("dataModelRepeatedMeasures", traitParameter, missingIndices, true, dimTrait, samplingPrecision);
    dataModelRepeatedMeasuresInv = new RepeatedMeasuresTraitDataModel("dataModelRepeatedMeasuresInv", traitParameter, missingIndices, true, dimTrait, samplingPrecisionInv);
}

Example 27 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);
}

Example 28 with DefaultBranchRateModel

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

the class ContinuousTraitTest method setUp.

public void setUp() throws Exception {
    super.setUp();
    dimTrait = 3;
    format.setMaximumFractionDigits(5);
    // Tree
    createAlignment(PRIMATES_TAXON_SEQUENCE, Nucleotides.INSTANCE);
    treeModel = createPrimateTreeModel();
    // Rates
    rateTransformation = new ContinuousRateTransformation.Default(treeModel, false, false);
    rateModel = new DefaultBranchRateModel();
    // Data
    nTips = 6;
    Parameter[] dataTraits = new Parameter[6];
    dataTraits[0] = new Parameter.Default("human", new double[] { -1.0, 2.0, 3.0 });
    dataTraits[1] = new Parameter.Default("chimp", new double[] { 10.0, 12.0, 14.0 });
    dataTraits[2] = new Parameter.Default("bonobo", new double[] { 0.5, -2.0, 5.5 });
    dataTraits[3] = new Parameter.Default("gorilla", new double[] { 2.0, 5.0, -8.0 });
    dataTraits[4] = new Parameter.Default("orangutan", new double[] { 11.0, 1.0, -1.5 });
    dataTraits[5] = new Parameter.Default("siamang", new double[] { 1.0, 2.5, 4.0 });
    traitParameter = new CompoundParameter("trait", dataTraits);
    List<Integer> missingIndices = new ArrayList<Integer>();
    traitParameter.setParameterValue(2, 0);
    missingIndices.add(3);
    missingIndices.add(4);
    missingIndices.add(5);
    missingIndices.add(7);
    // // Standard Model //// ***************************************************************************************
    // Diffusion
    Parameter[] precisionParameters = new Parameter[dimTrait];
    precisionParameters[0] = new Parameter.Default(new double[] { 1.0, 0.1, 0.2 });
    precisionParameters[1] = new Parameter.Default(new double[] { 0.1, 2.0, 0.0 });
    precisionParameters[2] = new Parameter.Default(new double[] { 0.2, 0.0, 3.0 });
    MatrixParameterInterface diffusionPrecisionMatrixParameter = new MatrixParameter("precisionMatrix", precisionParameters);
    diffusionModel = new MultivariateDiffusionModel(diffusionPrecisionMatrixParameter);
    PrecisionType precisionType = PrecisionType.FULL;
    // Root prior
    Parameter rootMean = new Parameter.Default(new double[] { -1.0, -3.0, 2.5 });
    Parameter rootSampleSize = new Parameter.Default(10.0);
    rootPrior = new ConjugateRootTraitPrior(rootMean, rootSampleSize);
    // Root prior Inf
    Parameter rootMeanInf = new Parameter.Default(new double[] { -1.0, -3.0, 2.0 });
    Parameter rootSampleSizeInf = new Parameter.Default(Double.POSITIVE_INFINITY);
    rootPriorInf = new ConjugateRootTraitPrior(rootMeanInf, rootSampleSizeInf);
    // Data Model
    dataModel = new ContinuousTraitDataModel("dataModel", traitParameter, missingIndices, true, dimTrait, precisionType);
    // // Factor Model //// *****************************************************************************************
    // Diffusion
    Parameter offDiagonal = new Parameter.Default(new double[] { 0.08164966 });
    offDiagonal.addBounds(new Parameter.DefaultBounds(Double.POSITIVE_INFINITY, Double.NEGATIVE_INFINITY, 1));
    Parameter diagonal = new Parameter.Default(new double[] { 1.0, 1.5 });
    precisionMatrixFactor = new CompoundSymmetricMatrix(diagonal, offDiagonal, true, true);
    diffusionModelFactor = new MultivariateDiffusionModel(precisionMatrixFactor);
    // Root prior
    rootMeanFactor = new Parameter.Default(new double[] { -1.0, 2.0 });
    Parameter rootSampleSizeFac = new Parameter.Default(10.0);
    rootPriorFactor = new ConjugateRootTraitPrior(rootMeanFactor, rootSampleSizeFac);
    // Error model
    Parameter factorPrecisionParameters = new Parameter.Default("factorPrecision", new double[] { 1.0, 5.0, 0.5 });
    // Loadings
    Parameter[] loadingsParameters = new Parameter[2];
    loadingsParameters[0] = new Parameter.Default(new double[] { 1.0, 2.0, 3.0 });
    loadingsParameters[1] = new Parameter.Default(new double[] { 0.0, 0.5, 1.0 });
    MatrixParameterInterface loadingsMatrixParameters = new MatrixParameter("loadings", loadingsParameters);
    dataModelFactor = new IntegratedFactorAnalysisLikelihood("dataModelFactors", traitParameter, missingIndices, loadingsMatrixParameters, factorPrecisionParameters, 0.0, null);
    // // Integrated Process //// ***********************************************************************************
    // Data Model
    dataModelIntegrated = new IntegratedProcessTraitDataModel("dataModelIntegrated", traitParameter, missingIndices, true, dimTrait, precisionType);
    // Root prior
    String sI = "<beast>\n" + "    <conjugateRootPrior>\n" + "        <meanParameter>\n" + "            <parameter id=\"meanRoot\"  value=\"0.0 1.2 -0.5 -1.0 -3.0 2.5\"/>\n" + "        </meanParameter>\n" + "        <priorSampleSize>\n" + "            <parameter id=\"sampleSizeRoot\" value=\"10.0\"/>\n" + "        </priorSampleSize>\n" + "    </conjugateRootPrior>\n" + "</beast>";
    XMLParser parserI = new XMLParser(true, true, true, null);
    parserI.addXMLObjectParser(new AttributeParser());
    parserI.addXMLObjectParser(new ParameterParser());
    parserI.parse(new StringReader(sI), true);
    rootPriorIntegrated = ConjugateRootTraitPrior.parseConjugateRootTraitPrior(parserI.getRoot(), 2 * dimTrait);
}