Examples with PrecisionType dr.evomodel.treedatalikelihood.continuous.cdi.PrecisionType used on opensource projects

Example 1 with PrecisionType

use of dr.evomodel.treedatalikelihood.continuous.cdi.PrecisionType in project beast-mcmc by beast-dev.

the class ContinuousDataLikelihoodParser method parseXMLObject.

public Object parseXMLObject(XMLObject xo) throws XMLParseException {
    TreeModel treeModel = (TreeModel) xo.getChild(TreeModel.class);
    MultivariateDiffusionModel diffusionModel = (MultivariateDiffusionModel) xo.getChild(MultivariateDiffusionModel.class);
    BranchRateModel rateModel = (BranchRateModel) xo.getChild(BranchRateModel.class);
    TreeTraitParserUtilities utilities = new TreeTraitParserUtilities();
    String traitName = TreeTraitParserUtilities.DEFAULT_TRAIT_NAME;
    TreeTraitParserUtilities.TraitsAndMissingIndices returnValue = utilities.parseTraitsFromTaxonAttributes(xo, traitName, treeModel, true);
    CompoundParameter traitParameter = returnValue.traitParameter;
    List<Integer> missingIndices = returnValue.missingIndices;
    Parameter sampleMissingParameter = returnValue.sampleMissingParameter;
    traitName = returnValue.traitName;
    final int dim = diffusionModel.getPrecisionmatrix().length;
    PrecisionType precisionType = PrecisionType.SCALAR;
    if (missingIndices.size() > 0 && !xo.getAttribute(FORCE_COMPLETELY_MISSING, false)) {
        precisionType = PrecisionType.FULL;
    }
    System.err.println("Using precisionType == " + precisionType + " for data model.");
    ContinuousTraitDataModel dataModel = new ContinuousTraitDataModel(traitName, traitParameter, missingIndices, dim, precisionType);
    ConjugateRootTraitPrior rootPrior = ConjugateRootTraitPrior.parseConjugateRootTraitPrior(xo, dim);
    boolean useTreeLength = xo.getAttribute(USE_TREE_LENGTH, false);
    boolean scaleByTime = xo.getAttribute(SCALE_BY_TIME, false);
    if (rateModel == null) {
        rateModel = new DefaultBranchRateModel();
    }
    ContinuousRateTransformation rateTransformation = new ContinuousRateTransformation.Default(treeModel, scaleByTime, useTreeLength);
    ContinuousDataLikelihoodDelegate delegate = new ContinuousDataLikelihoodDelegate(treeModel, diffusionModel, dataModel, rootPrior, rateTransformation, rateModel);
    TreeDataLikelihood treeDataLikelihood = new TreeDataLikelihood(delegate, treeModel, rateModel);
    boolean reconstructTraits = xo.getAttribute(RECONSTRUCT_TRAITS, true);
    if (reconstructTraits) {
        if (missingIndices.size() == 0) {
            ProcessSimulationDelegate simulationDelegate = new ProcessSimulationDelegate.ConditionalOnTipsRealizedDelegate(traitName, treeModel, diffusionModel, dataModel, rootPrior, rateTransformation, rateModel, delegate);
            TreeTraitProvider traitProvider = new ProcessSimulation(traitName, treeDataLikelihood, simulationDelegate);
            treeDataLikelihood.addTraits(traitProvider.getTreeTraits());
        } else {
            ProcessSimulationDelegate simulationDelegate = delegate.getPrecisionType() == PrecisionType.SCALAR ? new ProcessSimulationDelegate.ConditionalOnTipsRealizedDelegate(traitName, treeModel, diffusionModel, dataModel, rootPrior, rateTransformation, rateModel, delegate) : new ProcessSimulationDelegate.MultivariateConditionalOnTipsRealizedDelegate(traitName, treeModel, diffusionModel, dataModel, rootPrior, rateTransformation, rateModel, delegate);
            TreeTraitProvider traitProvider = new ProcessSimulation(traitName, treeDataLikelihood, simulationDelegate);
            treeDataLikelihood.addTraits(traitProvider.getTreeTraits());
            ProcessSimulationDelegate fullConditionalDelegate = new ProcessSimulationDelegate.TipRealizedValuesViaFullConditionalDelegate(traitName, treeModel, diffusionModel, dataModel, rootPrior, rateTransformation, rateModel, delegate);
            treeDataLikelihood.addTraits(new ProcessSimulation(("fc." + traitName), treeDataLikelihood, fullConditionalDelegate).getTreeTraits());
        //                String partialTraitName = getPartiallyMissingTraitName(traitName);
        //
        //                ProcessSimulationDelegate parialSimulationDelegate = new ProcessSimulationDelegate.ConditionalOnPartiallyMissingTipsDelegate(partialTraitName,
        //                        treeModel, diffusionModel, dataModel, rootPrior, rateTransformation, rateModel, delegate);
        //
        //                TreeTraitProvider partialTraitProvider = new ProcessSimulation(partialTraitName,
        //                        treeDataLikelihood, parialSimulationDelegate);
        //
        //                treeDataLikelihood.addTraits(partialTraitProvider.getTreeTraits());
        }
    }
    return treeDataLikelihood;
}

Example 2 with PrecisionType

use of dr.evomodel.treedatalikelihood.continuous.cdi.PrecisionType in project beast-mcmc by beast-dev.

the class ContinuousTraitDataModel method getTipPartial.

public double[] getTipPartial(int taxonIndex, boolean fullyObserved) {
    if (fullyObserved) {
        final PrecisionType precisionType = PrecisionType.SCALAR;
        final int offsetInc = dimTrait + precisionType.getMatrixLength(dimTrait);
        final double precision = precisionType.getObservedPrecisionValue(false);
        double[] tipPartial = getTipPartial(taxonIndex, precisionType);
        for (int i = 0; i < numTraits; ++i) {
            precisionType.fillPrecisionInPartials(tipPartial, i * offsetInc, 0, precision, dimTrait);
        }
        //            System.exit(-1);
        return tipPartial;
    } else {
        return getTipPartial(taxonIndex, precisionType);
    }
}