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

Example 16 with ArbitraryBranchRates

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

the class ContinuousDataLikelihoodDelegateTest method testLikelihoodDriftRelaxedFactor.

public void testLikelihoodDriftRelaxedFactor() {
    System.out.println("\nTest Likelihood using drifted Relaxed BM and factor:");
    // Diffusion
    List<BranchRateModel> driftModels = new ArrayList<BranchRateModel>();
    ArbitraryBranchRates.BranchRateTransform transform = make(false, false, false);
    driftModels.add(new ArbitraryBranchRates(treeModel, new Parameter.Default("rate.1", new double[] { 0, 10, 20, 30, 40, 40, 30, 20, 10, 0 }), transform, false));
    driftModels.add(new StrictClockBranchRates(new Parameter.Default("rate.2", new double[] { 0.0 })));
    DiffusionProcessDelegate diffusionProcessDelegate = new DriftDiffusionModelDelegate(treeModel, diffusionModelFactor, driftModels);
    // CDL
    ContinuousDataLikelihoodDelegate likelihoodDelegateFactors = new ContinuousDataLikelihoodDelegate(treeModel, diffusionProcessDelegate, dataModelFactor, rootPriorFactor, rateTransformation, rateModel, false);
    dataModelFactor.setLikelihoodDelegate(likelihoodDelegateFactors);
    // Likelihood Computation
    TreeDataLikelihood dataLikelihoodFactors = new TreeDataLikelihood(likelihoodDelegateFactors, treeModel, rateModel);
    testLikelihood("likelihoodDriftRelaxedFactor", dataModelFactor, dataLikelihoodFactors);
    // Conditional simulations
    MathUtils.setSeed(17890826);
    double[] expectedTraits = new double[] { 0.21992781609528125, 1.271388273711557, 0.40761548539751596, 1.3682648770877144, 0.6599021787120436, 1.2830636141108613, 1.1488658943588324, 1.472103688153391, 0.8971632986744889, 1.20748933414854, 1.603739823726808, 1.4761482401796842 };
    testConditionalSimulations(dataLikelihoodFactors, likelihoodDelegateFactors, diffusionModelFactor, dataModelFactor, rootPriorFactor, expectedTraits);
}

Example 17 with ArbitraryBranchRates

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

the class ContinuousDataLikelihoodDelegateTest method testLikelihoodFullOURelaxedFactor.

public void testLikelihoodFullOURelaxedFactor() {
    System.out.println("\nTest Likelihood using full Relaxed OU and factor:");
    // Diffusion
    List<BranchRateModel> optimalTraitsModels = new ArrayList<BranchRateModel>();
    ArbitraryBranchRates.BranchRateTransform transform = make(false, false, false);
    optimalTraitsModels.add(new ArbitraryBranchRates(treeModel, new Parameter.Default("rate.1", new double[] { 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 }), transform, false));
    optimalTraitsModels.add(new StrictClockBranchRates(new Parameter.Default("rate.2", new double[] { 1.5 })));
    Parameter[] strengthOfSelectionParameters = new Parameter[2];
    strengthOfSelectionParameters[0] = new Parameter.Default(new double[] { 0.5, 0.15 });
    strengthOfSelectionParameters[1] = new Parameter.Default(new double[] { 0.15, 25.5 });
    MatrixParameter strengthOfSelectionMatrixParam = new MatrixParameter("strengthOfSelectionMatrix", strengthOfSelectionParameters);
    DiffusionProcessDelegate diffusionProcessDelegate = new OUDiffusionModelDelegate(treeModel, diffusionModelFactor, optimalTraitsModels, new MultivariateElasticModel(strengthOfSelectionMatrixParam));
    // CDL
    ContinuousDataLikelihoodDelegate likelihoodDelegateFactors = new ContinuousDataLikelihoodDelegate(treeModel, diffusionProcessDelegate, dataModelFactor, rootPriorFactor, rateTransformation, rateModel, false);
    dataModelFactor.setLikelihoodDelegate(likelihoodDelegateFactors);
    // Likelihood Computation
    TreeDataLikelihood dataLikelihoodFactors = new TreeDataLikelihood(likelihoodDelegateFactors, treeModel, rateModel);
    testLikelihood("likelihoodFullRelaxedOUFactor", dataModelFactor, dataLikelihoodFactors);
    // Conditional simulations
    MathUtils.setSeed(17890826);
    double[] expectedTraits = new double[] { 0.6074917696668031, 1.4240248941610945, 0.5818653246406664, 1.545237778993696, 0.7248840308905077, 1.4623057820376757, 1.0961030597302799, 1.603694717986661, 0.44280937767720896, 1.5374906898020686, 0.920698984735896, 1.6011019734876784 };
    testConditionalSimulations(dataLikelihoodFactors, likelihoodDelegateFactors, diffusionModelFactor, dataModelFactor, rootPriorFactor, expectedTraits);
}