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

Example 31 with StrictClockBranchRates

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

the class ContinuousDataLikelihoodDelegateTest method testLikelihoodDriftRelaxed.

public void testLikelihoodDriftRelaxed() {
    System.out.println("\nTest Likelihood using Drifted relaxed BM:");
    // 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, 100, 200, 300, 400, 500, 600, 700, 800, 900 }), transform, false));
    driftModels.add(new ArbitraryBranchRates(treeModel, new Parameter.Default("rate.2", new double[] { 0, -100, 200, -300, 400, -500, 600, -700, 800, -900 }), transform, false));
    driftModels.add(new StrictClockBranchRates(new Parameter.Default("rate.3", new double[] { -2.0 })));
    DiffusionProcessDelegate diffusionProcessDelegate = new DriftDiffusionModelDelegate(treeModel, diffusionModel, driftModels);
    // CDL
    ContinuousDataLikelihoodDelegate likelihoodDelegate = new ContinuousDataLikelihoodDelegate(treeModel, diffusionProcessDelegate, dataModel, rootPrior, rateTransformation, rateModel, false);
    // Likelihood Computation
    TreeDataLikelihood dataLikelihood = new TreeDataLikelihood(likelihoodDelegate, treeModel, rateModel);
    testLikelihood("likelihoodDriftRelaxed", dataLikelihood);
    // Conditional moments (preorder)
    testConditionalMoments(dataLikelihood, likelihoodDelegate);
    // Conditional simulations
    MathUtils.setSeed(17890826);
    double[] expectedTraits = new double[] { -1.0, 2.0, 0.0, 2.843948876154644, 10.866053719140933, 3.467579698926694, 0.5, 12.000214659757933, 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("likelihoodDriftRelaxedInf", dataLikelihoodInf);
    testConditionalMoments(dataLikelihoodInf, likelihoodDelegateInf);
}

Example 32 with StrictClockBranchRates

use of dr.evomodel.branchratemodel.StrictClockBranchRates 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));
}

Example 33 with StrictClockBranchRates

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

the class DiffusionGradientTest method testGradientSingleDriftWithMissing.

public void testGradientSingleDriftWithMissing() {
    // Diffusion
    List<BranchRateModel> driftModels = new ArrayList<BranchRateModel>();
    CompoundParameter driftParam = new CompoundParameter("drift");
    for (int i = 0; i < dimTrait; i++) {
        Parameter rate = new Parameter.Default("rate." + (i + 1), new double[] { 2.2 * i + 3.1 });
        driftParam.addParameter(rate);
        driftModels.add(new StrictClockBranchRates(rate));
    }
    // Wrt Precision
    DiffusionProcessDelegate diffusionProcessDelegate = new DriftDiffusionModelDelegate(treeModel, diffusionModel, driftModels);
    System.out.println("\nTest single drift gradient drift.");
    testGradient(diffusionModel, diffusionProcessDelegate, dataModel, precisionMatrix, driftParam);
    System.out.println("\nTest single drift gradient precision with missing.");
    testGradient(diffusionModel, diffusionProcessDelegate, dataModelMissing, precisionMatrix, driftParam);
    // Wrt Variance
    DiffusionProcessDelegate diffusionProcessDelegateVariance = new DriftDiffusionModelDelegate(treeModel, diffusionModelVar, driftModels);
    System.out.println("\nTest single drift gradient variance.");
    testGradient(diffusionModelVar, diffusionProcessDelegateVariance, dataModel, precisionMatrixInv, driftParam);
    System.out.println("\nTest single drift gradient variance with missing.");
    testGradient(diffusionModelVar, diffusionProcessDelegateVariance, dataModelMissing, precisionMatrixInv, driftParam);
    // Repeated Measures Model
    System.out.println("\nTest gradient precision repeated measures.");
    testGradient(diffusionModel, diffusionProcessDelegate, dataModelRepeatedMeasures, rootPrior, meanRoot, precisionMatrix, false, null, driftParam, samplingPrecision);
    testGradient(diffusionModel, diffusionProcessDelegate, dataModelRepeatedMeasuresInv, rootPrior, meanRoot, precisionMatrix, false, null, driftParam, samplingPrecisionInv);
}

Example 34 with StrictClockBranchRates

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

the class DiffusionGradientTest method testGradientDriftWithMissing.

public void testGradientDriftWithMissing() {
    // Diffusion
    List<BranchRateModel> driftModels = new ArrayList<BranchRateModel>();
    driftModels.add(new StrictClockBranchRates(new Parameter.Default("rate.1", new double[] { 0.0 })));
    driftModels.add(new StrictClockBranchRates(new Parameter.Default("rate.2", new double[] { 200.0 })));
    driftModels.add(new StrictClockBranchRates(new Parameter.Default("rate.3", new double[] { -200.0 })));
    driftModels.add(new StrictClockBranchRates(new Parameter.Default("rate.4", new double[] { 1.0 })));
    driftModels.add(new StrictClockBranchRates(new Parameter.Default("rate.5", new double[] { 200.0 })));
    driftModels.add(new StrictClockBranchRates(new Parameter.Default("rate.6", new double[] { -200.0 })));
    // Wrt Precision
    DiffusionProcessDelegate diffusionProcessDelegate = new DriftDiffusionModelDelegate(treeModel, diffusionModel, driftModels);
    System.out.println("\nTest drift gradient precision.");
    testGradient(diffusionModel, diffusionProcessDelegate, dataModel, precisionMatrix);
    System.out.println("\nTest drift gradient precision with missing.");
    testGradient(diffusionModel, diffusionProcessDelegate, dataModelMissing, precisionMatrix);
    // Wrt Variance
    DiffusionProcessDelegate diffusionProcessDelegateVariance = new DriftDiffusionModelDelegate(treeModel, diffusionModelVar, driftModels);
    System.out.println("\nTest drift gradient variance.");
    testGradient(diffusionModelVar, diffusionProcessDelegateVariance, dataModel, precisionMatrixInv);
    System.out.println("\nTest drift gradient variance with missing.");
    testGradient(diffusionModelVar, diffusionProcessDelegateVariance, dataModelMissing, precisionMatrixInv);
    // Factor Model
    // Diffusion
    List<BranchRateModel> driftModelsFactor = new ArrayList<BranchRateModel>();
    driftModelsFactor.add(new StrictClockBranchRates(new Parameter.Default("rate.1", new double[] { 0.0 })));
    driftModelsFactor.add(new StrictClockBranchRates(new Parameter.Default("rate.2", new double[] { 200.0 })));
    DiffusionProcessDelegate diffusionProcessDelegateFactor = new DriftDiffusionModelDelegate(treeModel, diffusionModelFactor, driftModelsFactor);
    System.out.println("\nTest gradient precision.");
    testGradient(diffusionModelFactor, diffusionProcessDelegateFactor, dataModelFactor, rootPriorFactor, rootMeanFactor, precisionMatrixFactor, false);
    // Repeated Measures Model
    System.out.println("\nTest gradient precision repeated measures.");
    testGradient(diffusionModel, diffusionProcessDelegate, dataModelRepeatedMeasures, rootPrior, meanRoot, precisionMatrix, false, null, null, samplingPrecision);
    testGradient(diffusionModel, diffusionProcessDelegate, dataModelRepeatedMeasuresInv, rootPrior, meanRoot, precisionMatrix, false, null, null, samplingPrecisionInv);
}

Example 35 with StrictClockBranchRates

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

the class BranchSpecificGradientTest method testRateGradient.

public void testRateGradient() {
    System.out.println("\nTest Likelihood using vanilla BM:");
    // Diffusion
    List<BranchRateModel> driftModels = new ArrayList<BranchRateModel>();
    driftModels.add(new StrictClockBranchRates(new Parameter.Default("rate.1", new double[] { 1.0 })));
    driftModels.add(new StrictClockBranchRates(new Parameter.Default("rate.2", new double[] { 2.0 })));
    driftModels.add(new StrictClockBranchRates(new Parameter.Default("rate.3", new double[] { -2.0 })));
    DiffusionProcessDelegate diffusionProcessDelegate = new DriftDiffusionModelDelegate(treeModel, diffusionModel, driftModels);
    // Rates
    ArbitraryBranchRates.BranchRateTransform transform = make(false, true, false, null, null);
    Parameter branchRates = new Parameter.Default(new double[] { 10, 9, 8, 7, 6, 5, 4, 3, 2, 1 });
    ArbitraryBranchRates rateModel = new ArbitraryBranchRates(treeModel, branchRates, transform, false);
    ContinuousRateTransformation rateTransformation = new ContinuousRateTransformation.Default(treeModel, false, false);
    // CDL
    ContinuousDataLikelihoodDelegate likelihoodDelegate = new ContinuousDataLikelihoodDelegate(treeModel, diffusionProcessDelegate, dataModel, rootPrior, rateTransformation, rateModel, false);
    // Likelihood Computation
    TreeDataLikelihood dataLikelihood = new TreeDataLikelihood(likelihoodDelegate, treeModel, rateModel);
    // Gradient (Rates)
    BranchRateGradient branchGradient1 = new BranchRateGradient("trait", dataLikelihood, likelihoodDelegate, branchRates);
    double[] gradient1 = branchGradient1.getGradientLogDensity();
    // Gradient (Specific)
    ContinuousTraitGradientForBranch.RateGradient traitGradient = new ContinuousTraitGradientForBranch.RateGradient(dimTrait, treeModel, rateModel);
    BranchSpecificGradient branchGradient2 = new BranchSpecificGradient("trait", dataLikelihood, likelihoodDelegate, traitGradient, branchRates);
    double[] gradient2 = branchGradient2.getGradientLogDensity();
    double[] numericalGradient = branchGradient1.getNumericalGradient();
    System.err.println("\tGradient with rate method    = " + new dr.math.matrixAlgebra.Vector(gradient1));
    System.err.println("\tGradient with general method = " + new dr.math.matrixAlgebra.Vector(gradient2));
    System.err.println("\tNumerical gradient           = " + new dr.math.matrixAlgebra.Vector(numericalGradient));
    assertEquals("length", gradient1.length, gradient2.length);
    for (int i = 0; i < gradient1.length; i++) {
        assertEquals("numeric " + i, gradient1[i], numericalGradient[i], 1E-4);
    }
    for (int i = 0; i < gradient1.length; i++) {
        assertEquals("gradient " + i, format.format(gradient1[i]), format.format(gradient2[i]));
    }
}