Examples with Parameter dr.inference.model.Parameter used on opensource projects

Example 31 with Parameter

use of dr.inference.model.Parameter in project beast-mcmc by beast-dev.

the class MicrosatelliteModelSelectOperator method doOperation.

public double doOperation() {
    int index = MathUtils.nextInt(indicators.length);
    //System.out.println(index);
    Parameter newModel = indicators[index];
    for (int i = 0; i < parameter.getDimension() - 1; i++) {
        parameter.setParameterValueQuietly(i, newModel.getParameterValue(i));
    }
    parameter.setParameterValueNotifyChangedAll(parameter.getDimension() - 1, newModel.getParameterValue(parameter.getDimension() - 1));
    //System.out.println(parameter+"; "+indicators[index]);
    return 0.0;
}

Example 32 with Parameter

use of dr.inference.model.Parameter 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 33 with Parameter

use of dr.inference.model.Parameter in project beast-mcmc by beast-dev.

the class BalancedBeagleTreeLikelihoodParser method parseXMLObject.

public Object parseXMLObject(XMLObject xo) throws XMLParseException {
    boolean useAmbiguities = xo.getAttribute(BeagleTreeLikelihoodParser.USE_AMBIGUITIES, false);
    /*int instanceCount = xo.getAttribute(INSTANCE_COUNT, 1);
        if (instanceCount < 1) {
            instanceCount = 1;
        }

        String ic = System.getProperty(BEAGLE_INSTANCE_COUNT);
        if (ic != null && ic.length() > 0) {
            instanceCount = Integer.parseInt(ic);
        }*/
    PatternList patternList = (PatternList) xo.getChild(PatternList.class);
    TreeModel treeModel = (TreeModel) xo.getChild(TreeModel.class);
    GammaSiteRateModel siteRateModel = (GammaSiteRateModel) xo.getChild(GammaSiteRateModel.class);
    FrequencyModel rootFreqModel = (FrequencyModel) xo.getChild(FrequencyModel.class);
    BranchModel branchModel = (BranchModel) xo.getChild(BranchModel.class);
    if (branchModel == null) {
        SubstitutionModel substitutionModel = (SubstitutionModel) xo.getChild(SubstitutionModel.class);
        if (substitutionModel == null) {
            substitutionModel = siteRateModel.getSubstitutionModel();
        }
        if (substitutionModel == null) {
            throw new XMLParseException("No substitution model available for TreeLikelihood: " + xo.getId());
        }
        branchModel = new HomogeneousBranchModel(substitutionModel, rootFreqModel);
    }
    BranchRateModel branchRateModel = (BranchRateModel) xo.getChild(BranchRateModel.class);
    TipStatesModel tipStatesModel = (TipStatesModel) xo.getChild(TipStatesModel.class);
    //        if (xo.getChild(TipStatesModel.class) != null) {
    //            throw new XMLParseException("Sequence Error Models are not supported under BEAGLE yet. Please use Native BEAST Likelihood.");
    //        }
    PartialsRescalingScheme scalingScheme = PartialsRescalingScheme.DEFAULT;
    if (xo.hasAttribute(BeagleTreeLikelihoodParser.SCALING_SCHEME)) {
        //            scalingScheme = PartialsRescalingScheme.parseFromString(xo.getStringAttribute(BeagleTreeLikelihoodParser.SCALING_SCHEME));
        if (scalingScheme == null)
            throw new XMLParseException("Unknown scaling scheme '" + xo.getStringAttribute(BeagleTreeLikelihoodParser.SCALING_SCHEME) + "' in " + "OldBeagleTreeLikelihood object '" + xo.getId());
    }
    boolean delayScaling = true;
    Map<Set<String>, Parameter> partialsRestrictions = null;
    if (xo.hasChildNamed(PARTIALS_RESTRICTION)) {
        XMLObject cxo = xo.getChild(PARTIALS_RESTRICTION);
        TaxonList taxonList = (TaxonList) cxo.getChild(TaxonList.class);
        //            Parameter parameter = (Parameter) cxo.getChild(Parameter.class);
        try {
            TreeUtils.getLeavesForTaxa(treeModel, taxonList);
        } catch (TreeUtils.MissingTaxonException e) {
            throw new XMLParseException("Unable to parse taxon list: " + e.getMessage());
        }
        throw new XMLParseException("Restricting internal nodes is not yet implemented.  Contact Marc");
    }
    /*if (instanceCount == 1 || patternList.getPatternCount() < instanceCount) {
            return createTreeLikelihood(
                    patternList,
                    treeModel,
                    branchModel,
                    siteRateModel,
                    branchRateModel,
                    tipStatesModel,
                    useAmbiguities,
                    scalingScheme,
                    partialsRestrictions,
                    xo
            );
        }*/
    //first run a test for instanceCount == 1
    System.err.println("\nTesting instanceCount == 1");
    Likelihood baseLikelihood = createTreeLikelihood(patternList, treeModel, branchModel, siteRateModel, branchRateModel, tipStatesModel, useAmbiguities, scalingScheme, delayScaling, partialsRestrictions, xo);
    double start = System.nanoTime();
    for (int i = 0; i < TEST_RUNS; i++) {
        baseLikelihood.makeDirty();
        baseLikelihood.getLogLikelihood();
    }
    double end = System.nanoTime();
    double baseResult = end - start;
    System.err.println("Evaluation took: " + baseResult);
    if (!(patternList instanceof SitePatterns)) {
        throw new XMLParseException("BEAGLE_INSTANCES option cannot be used with BEAUti-selected codon partitioning.");
    }
    if (tipStatesModel != null) {
        throw new XMLParseException("BEAGLE_INSTANCES option cannot be used with a TipStateModel (i.e., a sequence error model).");
    }
    //List<Likelihood> likelihoods = new ArrayList<Likelihood>();
    List<Likelihood> likelihoods = null;
    CompoundLikelihood compound = null;
    int instanceCount = 2;
    boolean optimal = false;
    while (optimal == false) {
        System.err.println("\nCreating instanceCount == " + instanceCount);
        likelihoods = new ArrayList<Likelihood>();
        for (int i = 0; i < instanceCount; i++) {
            Patterns subPatterns = new Patterns((SitePatterns) patternList, 0, 0, 1, i, instanceCount);
            AbstractTreeLikelihood treeLikelihood = createTreeLikelihood(subPatterns, treeModel, branchModel, siteRateModel, branchRateModel, null, useAmbiguities, scalingScheme, delayScaling, partialsRestrictions, xo);
            treeLikelihood.setId(xo.getId() + "_" + instanceCount);
            likelihoods.add(treeLikelihood);
        }
        //construct compoundLikelihood
        compound = new CompoundLikelihood(instanceCount, likelihoods);
        //test timings 
        System.err.println("\nTesting instanceCount == " + instanceCount);
        start = System.nanoTime();
        for (int i = 0; i < TEST_RUNS; i++) {
            compound.makeDirty();
            compound.getLogLikelihood();
        }
        end = System.nanoTime();
        double newResult = end - start;
        System.err.println("Evaluation took: " + newResult);
        if (baseResult / newResult > TEST_CUTOFF) {
            instanceCount++;
            baseResult = newResult;
        } else {
            optimal = true;
            instanceCount--;
            System.err.println("\nCreating final BeagleTreeLikelihood with instanceCount: " + instanceCount);
            likelihoods = new ArrayList<Likelihood>();
            for (int i = 0; i < instanceCount; i++) {
                Patterns subPatterns = new Patterns((SitePatterns) patternList, 0, 0, 1, i, instanceCount);
                AbstractTreeLikelihood treeLikelihood = createTreeLikelihood(subPatterns, treeModel, branchModel, siteRateModel, branchRateModel, null, useAmbiguities, scalingScheme, delayScaling, partialsRestrictions, xo);
                treeLikelihood.setId(xo.getId() + "_" + instanceCount);
                likelihoods.add(treeLikelihood);
            }
            //construct compoundLikelihood
            compound = new CompoundLikelihood(instanceCount, likelihoods);
        }
    }
    return compound;
/*for (int i = 0; i < instanceCount; i++) {

            Patterns subPatterns = new Patterns((SitePatterns)patternList, 0, 0, 1, i, instanceCount);

            AbstractTreeLikelihood treeLikelihood = createTreeLikelihood(
                    subPatterns,
                    treeModel,
                    branchModel,
                    siteRateModel,
                    branchRateModel,
                    null,
                    useAmbiguities,
                    scalingScheme,
                    partialsRestrictions,
                    xo);
            treeLikelihood.setId(xo.getId() + "_" + instanceCount);
            likelihoods.add(treeLikelihood);
        }

        return new CompoundLikelihood(likelihoods);*/
}

Example 34 with Parameter

use of dr.inference.model.Parameter in project beast-mcmc by beast-dev.

the class BeagleTreeLikelihoodParser method parseXMLObject.

public Object parseXMLObject(XMLObject xo) throws XMLParseException {
    boolean useAmbiguities = xo.getAttribute(USE_AMBIGUITIES, false);
    int instanceCount = xo.getAttribute(INSTANCE_COUNT, 1);
    if (instanceCount < 1) {
        instanceCount = 1;
    }
    String ic = System.getProperty(BEAGLE_INSTANCE_COUNT);
    if (ic != null && ic.length() > 0) {
        instanceCount = Integer.parseInt(ic);
    }
    PatternList patternList = (PatternList) xo.getChild(PatternList.class);
    TreeModel treeModel = (TreeModel) xo.getChild(TreeModel.class);
    GammaSiteRateModel siteRateModel = (GammaSiteRateModel) xo.getChild(GammaSiteRateModel.class);
    FrequencyModel rootFreqModel = (FrequencyModel) xo.getChild(FrequencyModel.class);
    BranchModel branchModel = (BranchModel) xo.getChild(BranchModel.class);
    if (branchModel == null) {
        SubstitutionModel substitutionModel = (SubstitutionModel) xo.getChild(SubstitutionModel.class);
        if (substitutionModel == null) {
            substitutionModel = siteRateModel.getSubstitutionModel();
        }
        if (substitutionModel == null) {
            throw new XMLParseException("No substitution model available for TreeLikelihood: " + xo.getId());
        }
        branchModel = new HomogeneousBranchModel(substitutionModel, rootFreqModel);
    }
    BranchRateModel branchRateModel = (BranchRateModel) xo.getChild(BranchRateModel.class);
    TipStatesModel tipStatesModel = (TipStatesModel) xo.getChild(TipStatesModel.class);
    //        if (xo.getChild(TipStatesModel.class) != null) {
    //            throw new XMLParseException("Sequence Error Models are not supported under BEAGLE yet. Please use Native BEAST Likelihood.");
    //        }
    PartialsRescalingScheme scalingScheme = PartialsRescalingScheme.DEFAULT;
    boolean delayScaling = true;
    if (xo.hasAttribute(SCALING_SCHEME)) {
        scalingScheme = PartialsRescalingScheme.parseFromString(xo.getStringAttribute(SCALING_SCHEME));
        if (scalingScheme == null)
            throw new XMLParseException("Unknown scaling scheme '" + xo.getStringAttribute(SCALING_SCHEME) + "' in " + "OldBeagleTreeLikelihood object '" + xo.getId());
    }
    if (xo.hasAttribute(DELAY_SCALING)) {
        delayScaling = xo.getBooleanAttribute(DELAY_SCALING);
    }
    Map<Set<String>, Parameter> partialsRestrictions = null;
    if (xo.hasChildNamed(PARTIALS_RESTRICTION)) {
        XMLObject cxo = xo.getChild(PARTIALS_RESTRICTION);
        TaxonList taxonList = (TaxonList) cxo.getChild(TaxonList.class);
        //            Parameter parameter = (Parameter) cxo.getChild(Parameter.class);
        try {
            TreeUtils.getLeavesForTaxa(treeModel, taxonList);
        } catch (TreeUtils.MissingTaxonException e) {
            throw new XMLParseException("Unable to parse taxon list: " + e.getMessage());
        }
        throw new XMLParseException("Restricting internal nodes is not yet implemented.  Contact Marc");
    }
    if (instanceCount == 1 || patternList.getPatternCount() < instanceCount) {
        return createTreeLikelihood(patternList, treeModel, branchModel, siteRateModel, branchRateModel, tipStatesModel, useAmbiguities, scalingScheme, delayScaling, partialsRestrictions, xo);
    }
    if (tipStatesModel != null) {
        throw new XMLParseException("BEAGLE_INSTANCES option cannot be used with a TipStateModel (i.e., a sequence error model).");
    }
    List<Likelihood> likelihoods = new ArrayList<Likelihood>();
    for (int i = 0; i < instanceCount; i++) {
        Patterns subPatterns = new Patterns(patternList, i, instanceCount);
        AbstractTreeLikelihood treeLikelihood = createTreeLikelihood(subPatterns, treeModel, branchModel, siteRateModel, branchRateModel, null, useAmbiguities, scalingScheme, delayScaling, partialsRestrictions, xo);
        treeLikelihood.setId(xo.getId() + "_" + instanceCount);
        likelihoods.add(treeLikelihood);
    }
    return new CompoundLikelihood(likelihoods);
}

Example 35 with Parameter

use of dr.inference.model.Parameter in project beast-mcmc by beast-dev.

the class MicrosatelliteSamplerTreeModelParser method parseXMLObject.

public Object parseXMLObject(XMLObject xo) throws XMLParseException {
    TreeModel tree = (TreeModel) xo.getElementFirstChild(TREE);
    Parameter internalVal = (Parameter) xo.getElementFirstChild(INTERNAL_VALUES);
    Patterns microsatellitePattern = (Patterns) xo.getElementFirstChild(EXTERNAL_VALUES);
    int[] externalValues = microsatellitePattern.getPattern(0);
    HashMap<String, Integer> taxaMap = new HashMap<String, Integer>(externalValues.length);
    boolean internalValuesProvided = false;
    if (xo.hasAttribute(USE_PROVIDED_INTERNAL_VALUES)) {
        internalValuesProvided = xo.getBooleanAttribute(USE_PROVIDED_INTERNAL_VALUES);
    }
    for (int i = 0; i < externalValues.length; i++) {
        taxaMap.put(microsatellitePattern.getTaxonId(i), i);
    }
    String modelId = xo.getAttribute("id", "treeMicrosatelliteSamplerModel");
    return new MicrosatelliteSamplerTreeModel(modelId, tree, internalVal, microsatellitePattern, externalValues, taxaMap, internalValuesProvided);
}