Examples with MultivariateElasticModel dr.evomodel.continuous.MultivariateElasticModel used on opensource projects

Example 11 with MultivariateElasticModel

use of dr.evomodel.continuous.MultivariateElasticModel in project beast-mcmc by beast-dev.

the class ContinuousDataLikelihoodDelegateTest method testLikelihoodFullDiagonalOUFactor.

public void testLikelihoodFullDiagonalOUFactor() {
    System.out.println("\nTest Likelihood comparing full and diagonal 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, 0.0 });
    strengthOfSelectionParameters[1] = new Parameter.Default(new double[] { 0.0, 1.5 });
    MatrixParameter strengthOfSelectionMatrixParam = new MatrixParameter("strengthOfSelectionMatrix", strengthOfSelectionParameters);
    DiagonalMatrix strengthOfSelectionMatrixParamDiagonal = new DiagonalMatrix(new Parameter.Default(new double[] { 0.5, 1.5 }));
    DiffusionProcessDelegate diffusionProcessDelegate = new OUDiffusionModelDelegate(treeModel, diffusionModelFactor, optimalTraitsModels, new MultivariateElasticModel(strengthOfSelectionMatrixParam));
    DiffusionProcessDelegate diffusionProcessDelegateDiagonal = new OUDiffusionModelDelegate(treeModel, diffusionModelFactor, optimalTraitsModels, new MultivariateElasticModel(strengthOfSelectionMatrixParamDiagonal));
    // CDL
    ContinuousDataLikelihoodDelegate likelihoodDelegateFactors = new ContinuousDataLikelihoodDelegate(treeModel, diffusionProcessDelegate, dataModelFactor, rootPriorFactor, rateTransformation, rateModel, false);
    ContinuousDataLikelihoodDelegate likelihoodDelegateFactorsDiagonal = new ContinuousDataLikelihoodDelegate(treeModel, diffusionProcessDelegateDiagonal, dataModelFactor, rootPriorFactor, rateTransformation, rateModel, false);
    dataModelFactor.setLikelihoodDelegate(likelihoodDelegateFactors);
    // Likelihood Computation
    TreeDataLikelihood dataLikelihoodFactors = new TreeDataLikelihood(likelihoodDelegateFactors, treeModel, rateModel);
    TreeDataLikelihood dataLikelihoodFactorsDiagonal = new TreeDataLikelihood(likelihoodDelegateFactorsDiagonal, treeModel, rateModel);
    double likelihoodFactorData = dataLikelihoodFactors.getLogLikelihood();
    double likelihoodFactorDiffusion = dataModelFactor.getLogLikelihood();
    double likelihoodFactorDataDiagonal = dataLikelihoodFactorsDiagonal.getLogLikelihood();
    double likelihoodFactorDiffusionDiagonal = dataModelFactor.getLogLikelihood();
    assertEquals("likelihoodFullDiagonalOUFactor", format.format(likelihoodFactorData + likelihoodFactorDiffusion), format.format(likelihoodFactorDataDiagonal + likelihoodFactorDiffusionDiagonal));
}

Example 12 with MultivariateElasticModel

use of dr.evomodel.continuous.MultivariateElasticModel in project beast-mcmc by beast-dev.

the class ContinuousDataLikelihoodDelegateTest method testLikelihoodDiagonalOUBM.

public void testLikelihoodDiagonalOUBM() {
    System.out.println("\nTest Likelihood using Diagonal OU / BM:");
    // 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 })));
    DiagonalMatrix strengthOfSelectionMatrixParam = new DiagonalMatrix(new Parameter.Default(new double[] { 0.0, 0.000001, 50.0 }));
    DiffusionProcessDelegate diffusionProcessDelegate = new OUDiffusionModelDelegate(treeModel, diffusionModel, optimalTraitsModels, new MultivariateElasticModel(strengthOfSelectionMatrixParam));
    // CDL
    ContinuousDataLikelihoodDelegate likelihoodDelegate = new ContinuousDataLikelihoodDelegate(treeModel, diffusionProcessDelegate, dataModel, rootPrior, rateTransformation, rateModel, false);
    // Likelihood Computation
    TreeDataLikelihood dataLikelihood = new TreeDataLikelihood(likelihoodDelegate, treeModel, rateModel);
    testLikelihood("likelihoodDiagonalOUBM", dataLikelihood);
    // Conditional moments (preorder)
    testConditionalMoments(dataLikelihood, likelihoodDelegate);
    // Fixed Root
    ContinuousDataLikelihoodDelegate likelihoodDelegateInf = new ContinuousDataLikelihoodDelegate(treeModel, diffusionProcessDelegate, dataModel, rootPriorInf, rateTransformation, rateModel, true);
    TreeDataLikelihood dataLikelihoodInf = new TreeDataLikelihood(likelihoodDelegateInf, treeModel, rateModel);
    testLikelihood("likelihoodDiagonalOUBMInf", dataLikelihoodInf);
    testConditionalMoments(dataLikelihoodInf, likelihoodDelegateInf);
}

Example 13 with MultivariateElasticModel

use of dr.evomodel.continuous.MultivariateElasticModel in project beast-mcmc by beast-dev.

the class ContinuousDataLikelihoodDelegateTest method testLikelihoodFullAndDiagonalOU.

public void testLikelihoodFullAndDiagonalOU() {
    System.out.println("\nTest Likelihood comparing Full and Diagonal OU:");
    // 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 ArbitraryBranchRates(treeModel, new Parameter.Default("rate.2", new double[] { 0, -1, 2, -3, 4, -5, 6, -7, 8, -9 }), transform, false));
    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, 0.0, 0.0 });
    strengthOfSelectionParameters[1] = new Parameter.Default(new double[] { 0.0, 10.5, 0.0 });
    strengthOfSelectionParameters[2] = new Parameter.Default(new double[] { 0.0, 0.0, 100.0 });
    MatrixParameter strengthOfSelectionMatrixParam = new MatrixParameter("strengthOfSelectionMatrix", strengthOfSelectionParameters);
    DiagonalMatrix strengthOfSelectionMatrixParamDiagonal = new DiagonalMatrix(new Parameter.Default(new double[] { 0.5, 10.5, 100.0 }));
    DiffusionProcessDelegate diffusionProcessDelegate = new OUDiffusionModelDelegate(treeModel, diffusionModel, optimalTraitsModels, new MultivariateElasticModel(strengthOfSelectionMatrixParam));
    DiffusionProcessDelegate diffusionProcessDelegateDiagonal = new OUDiffusionModelDelegate(treeModel, diffusionModel, optimalTraitsModels, new MultivariateElasticModel(strengthOfSelectionMatrixParamDiagonal));
    // CDL
    ContinuousDataLikelihoodDelegate likelihoodDelegate = new ContinuousDataLikelihoodDelegate(treeModel, diffusionProcessDelegate, dataModel, rootPrior, rateTransformation, rateModel, false);
    ContinuousDataLikelihoodDelegate likelihoodDelegateDiagonal = new ContinuousDataLikelihoodDelegate(treeModel, diffusionProcessDelegateDiagonal, dataModel, rootPrior, rateTransformation, rateModel, false);
    // Likelihood Computation
    TreeDataLikelihood dataLikelihood = new TreeDataLikelihood(likelihoodDelegate, treeModel, rateModel);
    TreeDataLikelihood dataLikelihoodDiagonal = new TreeDataLikelihood(likelihoodDelegateDiagonal, treeModel, rateModel);
    assertEquals("likelihoodFullDiagonalOU", format.format(dataLikelihood.getLogLikelihood()), format.format(dataLikelihoodDiagonal.getLogLikelihood()));
}

Example 14 with MultivariateElasticModel

use of dr.evomodel.continuous.MultivariateElasticModel in project beast-mcmc by beast-dev.

the class ContinuousDataLikelihoodDelegateTest method testLikelihoodDiagonalOURelaxed.

public void testLikelihoodDiagonalOURelaxed() {
    System.out.println("\nTest Likelihood using Diagonal OU Relaxed:");
    // 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 ArbitraryBranchRates(treeModel, new Parameter.Default("rate.2", new double[] { 0, -1, 2, -3, 4, -5, 6, -7, 8, -9 }), transform, false));
    optimalTraitsModels.add(new StrictClockBranchRates(new Parameter.Default("rate.3", new double[] { -2.0 })));
    DiagonalMatrix strengthOfSelectionMatrixParam = new DiagonalMatrix(new Parameter.Default(new double[] { 1.0, 100.0, 100.0 }));
    DiffusionProcessDelegate diffusionProcessDelegate = new OUDiffusionModelDelegate(treeModel, diffusionModel, optimalTraitsModels, new MultivariateElasticModel(strengthOfSelectionMatrixParam));
    // CDL
    ContinuousDataLikelihoodDelegate likelihoodDelegate = new ContinuousDataLikelihoodDelegate(treeModel, diffusionProcessDelegate, dataModel, rootPrior, rateTransformation, rateModel, false);
    // Likelihood Computation
    TreeDataLikelihood dataLikelihood = new TreeDataLikelihood(likelihoodDelegate, treeModel, rateModel);
    testLikelihood("likelihoodDiagonalOURelaxed", dataLikelihood);
    // Conditional moments (preorder)
    testConditionalMoments(dataLikelihood, likelihoodDelegate);
    // Conditional simulations
    MathUtils.setSeed(17890826);
    double[] expectedTraits = new double[] { -1.0, 2.0, 0.0, 1.811803424441062, 0.6837595819961084, -1.0607909328094163, 0.5, 3.8623525502275142, 5.5, 2.0, 5.0, -8.0, 11.0, 1.0, -1.5, 1.0, 2.5, 4.0 };
    testConditionalSimulations(dataLikelihood, likelihoodDelegate, diffusionModel, dataModel, rootPrior, expectedTraits);
    // Fixed Root
    ContinuousDataLikelihoodDelegate likelihoodDelegateInf = new ContinuousDataLikelihoodDelegate(treeModel, diffusionProcessDelegate, dataModel, rootPriorInf, rateTransformation, rateModel, true);
    TreeDataLikelihood dataLikelihoodInf = new TreeDataLikelihood(likelihoodDelegateInf, treeModel, rateModel);
    testLikelihood("likelihoodDiagonalOURelaxedInf", dataLikelihoodInf);
    testConditionalMoments(dataLikelihoodInf, likelihoodDelegateInf);
}

Example 15 with MultivariateElasticModel

use of dr.evomodel.continuous.MultivariateElasticModel in project beast-mcmc by beast-dev.

the class ContinuousDataLikelihoodDelegateTest method testLikelihoodFullNonSymmetricOU.

public void testLikelihoodFullNonSymmetricOU() {
    System.out.println("\nTest Likelihood using Full Non symmetric 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 })));
    Parameter[] strengthOfSelectionParameters = new Parameter[3];
    strengthOfSelectionParameters[0] = new Parameter.Default(new double[] { 0.5, 0.0, 0.0 });
    strengthOfSelectionParameters[1] = new Parameter.Default(new double[] { 0.2, 100.0, 0.1 });
    strengthOfSelectionParameters[2] = new Parameter.Default(new double[] { 10.0, 0.1, 50.5 });
    MatrixParameter strengthOfSelectionMatrixParam = new MatrixParameter("strengthOfSelectionMatrix", strengthOfSelectionParameters);
    DiffusionProcessDelegate diffusionProcessDelegate = new OUDiffusionModelDelegate(treeModel, diffusionModel, optimalTraitsModels, new MultivariateElasticModel(strengthOfSelectionMatrixParam));
    // CDL
    ContinuousDataLikelihoodDelegate likelihoodDelegate = new ContinuousDataLikelihoodDelegate(treeModel, diffusionProcessDelegate, dataModel, rootPrior, rateTransformation, rateModel, false);
    // Likelihood Computation
    TreeDataLikelihood dataLikelihood = new TreeDataLikelihood(likelihoodDelegate, treeModel, rateModel);
    testLikelihood("likelihoodFullNonSymmetricOU", dataLikelihood);
    // Conditional moments (preorder)
    testConditionalMoments(dataLikelihood, likelihoodDelegate);
    // Fixed Root
    ContinuousDataLikelihoodDelegate likelihoodDelegateInf = new ContinuousDataLikelihoodDelegate(treeModel, diffusionProcessDelegate, dataModel, rootPriorInf, rateTransformation, rateModel, true);
    TreeDataLikelihood dataLikelihoodInf = new TreeDataLikelihood(likelihoodDelegateInf, treeModel, rateModel);
    testLikelihood("likelihoodFullNonSymmetricOUInf", dataLikelihoodInf);
    testConditionalMoments(dataLikelihoodInf, likelihoodDelegateInf);
}