Examples with TreeDataLikelihood dr.evomodel.treedatalikelihood.TreeDataLikelihood used on opensource projects

Example 21 with TreeDataLikelihood

use of dr.evomodel.treedatalikelihood.TreeDataLikelihood in project beast-mcmc by beast-dev.

the class ContinuousDataLikelihoodDelegateTest method testLikelihoodDiagonalOURelaxedFactor.

public void testLikelihoodDiagonalOURelaxedFactor() {
    System.out.println("\nTest Likelihood using diagonal Relaxed OU and factor:");
    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 })));
    DiagonalMatrix strengthOfSelectionMatrixParam = new DiagonalMatrix(new Parameter.Default(new double[] { 1.5, 20.0 }));
    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("likelihoodDiagonalOURelaxedFactor", dataModelFactor, dataLikelihoodFactors);
    // Conditional simulations
    MathUtils.setSeed(17890826);
    double[] expectedTraits = new double[] { 1.2546097113922914, -1.1761389606670978, 1.305611773283861, -1.0644815941127401, 1.4571577864569687, -1.1477885449972944, 1.749551506462585, -0.9890375857170963, 1.0763987351136657, -1.0671848958534547, 1.5276137550128892, -0.9822950795368887 };
    testConditionalSimulations(dataLikelihoodFactors, likelihoodDelegateFactors, diffusionModelFactor, dataModelFactor, rootPriorFactor, expectedTraits);
}

Example 22 with TreeDataLikelihood

use of dr.evomodel.treedatalikelihood.TreeDataLikelihood in project beast-mcmc by beast-dev.

the class RepeatedMeasureFactorTest method testLikelihoodBM.

public void testLikelihoodBM() {
    System.out.println("\nTest Likelihood using vanilla BM:");
    // Diffusion
    DiffusionProcessDelegate diffusionProcessDelegate = new HomogeneousDiffusionModelDelegate(treeModel, diffusionModel);
    // 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("likelihoodBMFactor", format.format(logDatumLikelihoodFactor), format.format(likelihoodFactorData + likelihoodFactorDiffusion));
    System.out.println("likelihoodBMFactor: " + 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("likelihoodBMRepMea", format.format(logDatumLikelihoodRepMea), format.format(likelihoodRepMeaDiffusion));
    System.out.println("likelihoodBMRepMea: " + 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("likelihoodBMRepFactor", 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));
}

Example 23 with TreeDataLikelihood

use of dr.evomodel.treedatalikelihood.TreeDataLikelihood in project beast-mcmc by beast-dev.

the class ContinuousDataLikelihoodDelegateTest method testLikelihoodFullOUFactor.

public void testLikelihoodFullOUFactor() {
    System.out.println("\nTest Likelihood using full OU and factor:");
    // 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.1", new double[] { 2.0 })));
    Parameter[] strengthOfSelectionParameters = new Parameter[2];
    strengthOfSelectionParameters[0] = new Parameter.Default(new double[] { 0.5, 0.05 });
    strengthOfSelectionParameters[1] = new Parameter.Default(new double[] { 0.05, 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("likelihoodFullOUFactor", dataModelFactor, dataLikelihoodFactors);
    // Conditional simulations
    MathUtils.setSeed(17890826);
    double[] expectedTraits = new double[] { 0.4889263054598222, 1.866143125522109, 0.41845209107775877, 1.978457443711536, 0.5589398189015322, 1.8942177991552116, 0.9699471556784252, 2.0423474270630155, 0.3288819110219145, 1.9759942582707206, 0.8081782260054755, 2.038299849681893 };
    testConditionalSimulations(dataLikelihoodFactors, likelihoodDelegateFactors, diffusionModelFactor, dataModelFactor, rootPriorFactor, expectedTraits);
}

Example 24 with TreeDataLikelihood

use of dr.evomodel.treedatalikelihood.TreeDataLikelihood in project beast-mcmc by beast-dev.

the class ContinuousDataLikelihoodDelegateTest method testLikelihoodDiagonalOUBMInd.

public void testLikelihoodDiagonalOUBMInd() {
    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[] { -3.0 })));
    optimalTraitsModels.add(new StrictClockBranchRates(new Parameter.Default("rate.3", new double[] { -2.0 })));
    DiagonalMatrix strengthOfSelectionMatrixParamOUBM = new DiagonalMatrix(new Parameter.Default(new double[] { 0.0, 0.0, 50.0 }));
    DiagonalMatrix strengthOfSelectionMatrixParamOU = new DiagonalMatrix(new Parameter.Default(new double[] { 10.0, 20.0, 50.0 }));
    DiagonalMatrix diffusionPrecisionMatrixParameter = new DiagonalMatrix(new Parameter.Default(new double[] { 1.0, 2.0, 3.0 }));
    MultivariateDiffusionModel diffusionModel = new MultivariateDiffusionModel(diffusionPrecisionMatrixParameter);
    DiffusionProcessDelegate diffusionProcessDelegateOUBM = new OUDiffusionModelDelegate(treeModel, diffusionModel, optimalTraitsModels, new MultivariateElasticModel(strengthOfSelectionMatrixParamOUBM));
    DiffusionProcessDelegate diffusionProcessDelegateOU = new OUDiffusionModelDelegate(treeModel, diffusionModel, optimalTraitsModels, new MultivariateElasticModel(strengthOfSelectionMatrixParamOU));
    DiffusionProcessDelegate diffusionProcessDelegateBM = new HomogeneousDiffusionModelDelegate(treeModel, diffusionModel);
    // CDL
    ContinuousDataLikelihoodDelegate likelihoodDelegateOUBM = new ContinuousDataLikelihoodDelegate(treeModel, diffusionProcessDelegateOUBM, dataModel, rootPriorInf, rateTransformation, rateModel, false);
    ContinuousDataLikelihoodDelegate likelihoodDelegateOU = new ContinuousDataLikelihoodDelegate(treeModel, diffusionProcessDelegateOU, dataModel, rootPriorInf, rateTransformation, rateModel, false);
    ContinuousDataLikelihoodDelegate likelihoodDelegateBM = new ContinuousDataLikelihoodDelegate(treeModel, diffusionProcessDelegateBM, dataModel, rootPriorInf, rateTransformation, rateModel, false);
    // Likelihood Computation
    TreeDataLikelihood dataLikelihoodOUBM = new TreeDataLikelihood(likelihoodDelegateOUBM, treeModel, rateModel);
    TreeDataLikelihood dataLikelihoodOU = new TreeDataLikelihood(likelihoodDelegateOU, treeModel, rateModel);
    TreeDataLikelihood dataLikelihoodBM = new TreeDataLikelihood(likelihoodDelegateBM, treeModel, rateModel);
    // Conditional simulations
    MathUtils.setSeed(17890826);
    double[] traitsOUBM = getConditionalSimulations(dataLikelihoodOUBM, likelihoodDelegateOUBM, diffusionModel, dataModel, rootPriorInf, treeModel, rateTransformation);
    System.err.println(new Vector(traitsOUBM));
    MathUtils.setSeed(17890826);
    double[] traitsOU = getConditionalSimulations(dataLikelihoodOU, likelihoodDelegateOU, diffusionModel, dataModel, rootPriorInf, treeModel, rateTransformation);
    System.err.println(new Vector(traitsOU));
    MathUtils.setSeed(17890826);
    double[] traitsBM = getConditionalSimulations(dataLikelihoodBM, likelihoodDelegateBM, diffusionModel, dataModel, rootPriorInf, treeModel, rateTransformation);
    System.err.println(new Vector(traitsBM));
    // Check that missing dimensions with the same process have the same values
    assertEquals(format.format(traitsBM[3]), format.format(traitsOUBM[3]));
    assertEquals(format.format(traitsBM[4]), format.format(traitsOUBM[4]));
    assertEquals(format.format(traitsBM[7]), format.format(traitsOUBM[7]));
    assertEquals(format.format(traitsOU[5]), format.format(traitsOUBM[5]));
}

Example 25 with TreeDataLikelihood

use of dr.evomodel.treedatalikelihood.TreeDataLikelihood 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));
}