Examples with PartialsRescalingScheme dr.evomodel.treelikelihood.PartialsRescalingScheme used on opensource projects

Example 1 with PartialsRescalingScheme

use of dr.evomodel.treelikelihood.PartialsRescalingScheme 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 2 with PartialsRescalingScheme

use of dr.evomodel.treelikelihood.PartialsRescalingScheme 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);
    MutableTreeModel treeModel = (MutableTreeModel) xo.getChild(MutableTreeModel.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);
    if (branchModel instanceof EpochBranchModel && rootFreqModel != null) {
        EpochBranchModel epochBranchModel = (EpochBranchModel) branchModel;
        epochBranchModel.setRootFrequencyModel(rootFreqModel);
    }
    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");
    }
    int beagleThreadCount = -1;
    if (System.getProperty(BEAGLE_THREAD_COUNT) != null) {
        beagleThreadCount = Integer.parseInt(System.getProperty(BEAGLE_THREAD_COUNT));
    }
    if (beagleThreadCount == -1) {
        // the default is -1 threads (automatic thread pool size) but an XML attribute can override it
        int threadCount = xo.getAttribute(THREADS, -1);
        if (System.getProperty(THREAD_COUNT) != null) {
            threadCount = Integer.parseInt(System.getProperty(THREAD_COUNT));
        }
        // Todo: allow for different number of threads per beagle instance according to pattern counts
        if (threadCount >= 0) {
            System.setProperty(BEAGLE_THREAD_COUNT, Integer.toString(threadCount / instanceCount));
        }
    }
    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 3 with PartialsRescalingScheme

use of dr.evomodel.treelikelihood.PartialsRescalingScheme in project beast-mcmc by beast-dev.

the class TreeDataLikelihoodParser method parseXMLObject.

public Object parseXMLObject(XMLObject xo) throws XMLParseException {
    boolean useAmbiguities = xo.getAttribute(USE_AMBIGUITIES, false);
    boolean usePreOrder = xo.getAttribute(USE_PREORDER, false);
    boolean branchRateDerivative = xo.getAttribute(BRANCHRATE_DERIVATIVE, usePreOrder);
    boolean branchInfinitesimalDerivative = xo.getAttribute(BRANCHINFINITESIMAL_DERIVATIVE, false);
    if (usePreOrder != (branchRateDerivative || branchInfinitesimalDerivative)) {
        throw new RuntimeException("Need to specify derivative types.");
    }
    PreOrderSettings settings = new PreOrderSettings(usePreOrder, branchRateDerivative, branchInfinitesimalDerivative);
    // TreeDataLikelihood doesn't currently support Instances defined from the command line
    // 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);
    // }
    List<PatternList> patternLists = new ArrayList<PatternList>();
    List<SiteRateModel> siteRateModels = new ArrayList<SiteRateModel>();
    List<BranchModel> branchModels = new ArrayList<BranchModel>();
    boolean hasSinglePartition = false;
    PatternList patternList = (PatternList) xo.getChild(PatternList.class);
    if (patternList != null) {
        hasSinglePartition = true;
        patternLists.add(patternList);
        GammaSiteRateModel siteRateModel = (GammaSiteRateModel) xo.getChild(GammaSiteRateModel.class);
        siteRateModels.add(siteRateModel);
        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 partition in DataTreeLikelihood: " + xo.getId());
            }
            branchModel = new HomogeneousBranchModel(substitutionModel, rootFreqModel);
        }
        branchModels.add(branchModel);
    }
    int k = 0;
    for (int i = 0; i < xo.getChildCount(); i++) {
        if (xo.getChildName(i).equals(PARTITION)) {
            if (hasSinglePartition) {
                throw new XMLParseException("Either a single set of patterns should be given or multiple 'partitions' elements within DataTreeLikelihood: " + xo.getId());
            }
            k += 1;
            XMLObject cxo = (XMLObject) xo.getChild(i);
            patternList = (PatternList) cxo.getChild(PatternList.class);
            patternLists.add(patternList);
            GammaSiteRateModel siteRateModel = (GammaSiteRateModel) cxo.getChild(GammaSiteRateModel.class);
            siteRateModels.add(siteRateModel);
            FrequencyModel rootFreqModel = (FrequencyModel) xo.getChild(FrequencyModel.class);
            BranchModel branchModel = (BranchModel) cxo.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 partition " + k + " in DataTreeLikelihood: " + xo.getId());
                }
                branchModel = new HomogeneousBranchModel(substitutionModel, rootFreqModel);
            }
            branchModels.add(branchModel);
            BranchRateModel branchRateModel = (BranchRateModel) cxo.getChild(BranchRateModel.class);
            if (branchRateModel != null) {
                throw new XMLParseException("Partitions are not currently allowed their own BranchRateModel in TreeDataLikelihood object '" + xo.getId());
            }
        }
    }
    if (patternLists.size() == 0) {
        throw new XMLParseException("Either a single set of patterns should be given or multiple 'partitions' elements within DataTreeLikelihood: " + xo.getId());
    }
    Tree treeModel = (Tree) xo.getChild(Tree.class);
    BranchRateModel branchRateModel = (BranchRateModel) xo.getChild(BranchRateModel.class);
    if (branchRateModel == null) {
        throw new XMLParseException("BranchRateModel missing from TreeDataLikelihood object '" + xo.getId());
    // branchRateModel = new DefaultBranchRateModel();
    }
    TipStatesModel tipStatesModel = (TipStatesModel) xo.getChild(TipStatesModel.class);
    final boolean preferGPU = xo.getAttribute(PREFER_GPU, false);
    PartialsRescalingScheme scalingScheme = PartialsRescalingScheme.DEFAULT;
    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 " + "TreeDataLikelihood object '" + xo.getId());
    }
    final boolean delayScaling = xo.getAttribute(DELAY_SCALING, true);
    if (tipStatesModel != null) {
        throw new XMLParseException("BEAGLE_INSTANCES option cannot be used with a TipStateModel (i.e., a sequence error model).");
    }
    return createTreeDataLikelihood(patternLists, branchModels, siteRateModels, treeModel, branchRateModel, null, useAmbiguities, preferGPU, scalingScheme, delayScaling, settings);
}

Example 4 with PartialsRescalingScheme

use of dr.evomodel.treelikelihood.PartialsRescalingScheme in project beast-mcmc by beast-dev.

the class MultiPartitionDataLikelihoodParser 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);
    }
    if (DEBUG) {
        System.out.println("instanceCount: " + instanceCount);
    }
    List<PatternList> patternLists = xo.getAllChildren(PatternList.class);
    TreeModel treeModel = (TreeModel) xo.getChild(TreeModel.class);
    List<SiteRateModel> siteRateModels = xo.getAllChildren(SiteRateModel.class);
    FrequencyModel rootFreqModel = (FrequencyModel) xo.getChild(FrequencyModel.class);
    List<BranchModel> branchModels = xo.getAllChildren(BranchModel.class);
    if (branchModels.size() == 0) {
        if (DEBUG) {
            System.out.println("branchModels == null");
        }
        branchModels = new ArrayList<BranchModel>();
        List<SubstitutionModel> substitutionModels = xo.getAllChildren(SubstitutionModel.class);
        if (substitutionModels.size() == 0) {
            if (DEBUG) {
                System.out.println("substitutionModels == null");
            }
            for (SiteRateModel siteRateModel : siteRateModels) {
                SubstitutionModel substitutionModel = ((GammaSiteRateModel) siteRateModel).getSubstitutionModel();
                if (substitutionModel == null) {
                    throw new XMLParseException("No substitution model available for TreeDataLikelihood: " + xo.getId());
                }
                branchModels.add(new HomogeneousBranchModel(substitutionModel, rootFreqModel));
            }
        }
        if (DEBUG) {
            System.out.println("branchModels size: " + branchModels.size());
        }
        for (BranchModel branchModel : branchModels) {
            System.out.println("  " + branchModel.getId() + "  " + branchModel.getModelName());
        }
    }
    BranchRateModel branchRateModel = (BranchRateModel) xo.getChild(BranchRateModel.class);
    if (branchRateModel == null) {
        branchRateModel = new DefaultBranchRateModel();
    }
    if (DEBUG) {
        System.out.println("BranchRateModel: " + branchRateModel.getId());
    }
    TipStatesModel tipStatesModel = (TipStatesModel) xo.getChild(TipStatesModel.class);
    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);
    }
    if (instanceCount == 1) {
        if (DEBUG) {
            System.out.println("instanceCount == 1");
        }
        return createTreeDataLikelihood(patternLists, treeModel, branchModels, siteRateModels, branchRateModel, tipStatesModel, useAmbiguities, scalingScheme, delayScaling, xo);
    }
    if (tipStatesModel != null) {
        throw new XMLParseException("BEAGLE_INSTANCES option cannot be used with a TipStateModel (i.e., a sequence error model).");
    }
    List<PatternList> patternInstanceLists = new ArrayList<PatternList>();
    for (int j = 0; j < patternLists.size(); j++) {
        for (int i = 0; i < instanceCount; i++) {
            patternInstanceLists.add(new Patterns(patternLists.get(j), i, instanceCount));
        }
    }
    return createTreeDataLikelihood(patternLists, treeModel, branchModels, siteRateModels, branchRateModel, null, useAmbiguities, scalingScheme, delayScaling, xo);
}

Example 5 with PartialsRescalingScheme

use of dr.evomodel.treelikelihood.PartialsRescalingScheme in project beast-mcmc by beast-dev.

the class OptimizedBeagleTreeLikelihoodParser method parseXMLObject.

public Object parseXMLObject(XMLObject xo) throws XMLParseException {
    // Default of 100 likelihood calculations for calibration
    int calibrate = 100;
    if (xo.hasAttribute(CALIBRATE)) {
        calibrate = xo.getIntegerAttribute(CALIBRATE);
    }
    // Default: only try the next split up, unless a RETRY value is specified in the XML
    int retry = 0;
    if (xo.hasAttribute(RETRY)) {
        retry = xo.getIntegerAttribute(RETRY);
    }
    int childCount = xo.getChildCount();
    List<Likelihood> likelihoods = new ArrayList<Likelihood>();
    // TEST
    List<Likelihood> originalLikelihoods = new ArrayList<Likelihood>();
    for (int i = 0; i < childCount; i++) {
        likelihoods.add((Likelihood) xo.getChild(i));
        originalLikelihoods.add((Likelihood) xo.getChild(i));
    }
    if (DEBUG) {
        System.err.println("-----");
        System.err.println(childCount + " BeagleTreeLikelihoods added.");
    }
    int[] instanceCounts = new int[childCount];
    for (int i = 0; i < childCount; i++) {
        instanceCounts[i] = 1;
    }
    int[] currentLocation = new int[childCount];
    for (int i = 0; i < childCount; i++) {
        currentLocation[i] = i;
    }
    int[] siteCounts = new int[childCount];
    // store everything for later use
    SitePatterns[] patterns = new SitePatterns[childCount];
    MutableTreeModel[] treeModels = new TreeModel[childCount];
    BranchModel[] branchModels = new BranchModel[childCount];
    GammaSiteRateModel[] siteRateModels = new GammaSiteRateModel[childCount];
    BranchRateModel[] branchRateModels = new BranchRateModel[childCount];
    boolean[] ambiguities = new boolean[childCount];
    PartialsRescalingScheme[] rescalingSchemes = new PartialsRescalingScheme[childCount];
    boolean[] isDelayRescalingUntilUnderflow = new boolean[childCount];
    List<Map<Set<String>, Parameter>> partialsRestrictions = new ArrayList<Map<Set<String>, Parameter>>();
    for (int i = 0; i < likelihoods.size(); i++) {
        patterns[i] = (SitePatterns) ((BeagleTreeLikelihood) likelihoods.get(i)).getPatternsList();
        siteCounts[i] = patterns[i].getPatternCount();
        treeModels[i] = ((BeagleTreeLikelihood) likelihoods.get(i)).getTreeModel();
        branchModels[i] = ((BeagleTreeLikelihood) likelihoods.get(i)).getBranchModel();
        siteRateModels[i] = (GammaSiteRateModel) ((BeagleTreeLikelihood) likelihoods.get(i)).getSiteRateModel();
        branchRateModels[i] = ((BeagleTreeLikelihood) likelihoods.get(i)).getBranchRateModel();
        ambiguities[i] = ((BeagleTreeLikelihood) likelihoods.get(i)).useAmbiguities();
        rescalingSchemes[i] = ((BeagleTreeLikelihood) likelihoods.get(i)).getRescalingScheme();
        isDelayRescalingUntilUnderflow[i] = ((BeagleTreeLikelihood) likelihoods.get(i)).isDelayRescalingUntilUnderflow();
        partialsRestrictions.add(i, ((BeagleTreeLikelihood) likelihoods.get(i)).getPartialsRestrictions());
    }
    if (DEBUG) {
        System.err.println("Pattern counts: ");
        for (int i = 0; i < siteCounts.length; i++) {
            System.err.println(siteCounts[i] + "   vs.    " + patterns[i].getPatternCount());
        }
        System.err.println();
        System.err.println("Instance counts: ");
        for (int i = 0; i < instanceCounts.length; i++) {
            System.err.print(instanceCounts[i] + " ");
        }
        System.err.println();
        System.err.println("Current locations: ");
        for (int i = 0; i < currentLocation.length; i++) {
            System.err.print(currentLocation[i] + " ");
        }
        System.err.println();
    }
    TestThreadedCompoundLikelihood compound = new TestThreadedCompoundLikelihood(likelihoods);
    if (DEBUG) {
        System.err.println("Timing estimates for each of the " + calibrate + " likelihood calculations:");
    }
    double start = System.nanoTime();
    for (int i = 0; i < calibrate; i++) {
        if (DEBUG) {
            // double debugStart = System.nanoTime();
            compound.makeDirty();
            compound.getLogLikelihood();
        // double debugEnd = System.nanoTime();
        // System.err.println(debugEnd - debugStart);
        } else {
            compound.makeDirty();
            compound.getLogLikelihood();
        }
    }
    double end = System.nanoTime();
    double baseResult = end - start;
    if (DEBUG) {
        System.err.println("Starting evaluation took: " + baseResult);
    }
    int longestIndex = 0;
    int longestSize = siteCounts[0];
    // START TEST CODE
    /*System.err.println("Detailed evaluation times: ");
        long[] evaluationTimes = compound.getEvaluationTimes();
        int[] evaluationCounts = compound.getEvaluationCounts();
        long longest = evaluationTimes[0];
        for (int i = 0; i < evaluationTimes.length; i++) {
        	System.err.println(i + ": time=" + evaluationTimes[i] + "   count=" + evaluationCounts[i]);
            if (evaluationTimes[i] > longest) {
            	longest = evaluationTimes[i];
        	}
        }*/
    // END TEST CODE
    /*if (SPLIT_BY_PATTERN_COUNT) {

        	boolean notFinished = true;

        	while (notFinished) {

        		for (int i = 0; i < siteCounts.length; i++) {
        			if (siteCounts[i] > longestSize) {
        				longestIndex = i;
        				longestSize = siteCounts[longestIndex];
        			}
        		}
        		System.err.println("Split likelihood " + longestIndex + " with pattern count " + longestSize);

        		//split it in 2
        		int instanceCount = ++instanceCounts[longestIndex];

        		List<Likelihood> newList = new ArrayList<Likelihood>();
        		for (int i = 0; i < instanceCount; i++) {
        			Patterns subPatterns = new Patterns(patterns[longestIndex], 0, 0, 1, i, instanceCount);

        			BeagleTreeLikelihood treeLikelihood = createTreeLikelihood(
        					subPatterns, treeModels[longestIndex], branchModels[longestIndex], siteRateModels[longestIndex], branchRateModels[longestIndex],
        					null, 
        					ambiguities[longestIndex], rescalingSchemes[longestIndex], partialsRestrictions.get(longestIndex),
        					xo);

        			treeLikelihood.setId(xo.getId() + "_" + instanceCount);
        			newList.add(treeLikelihood);
        		}
        		for (int i = 0; i < newList.size()-1; i++) {
        			likelihoods.remove(currentLocation[longestIndex]);
        		}
        		//likelihoods.remove(longestIndex);
        		//likelihoods.add(longestIndex, new CompoundLikelihood(newList));
        		for (int i = 0; i < newList.size(); i++) {
        			likelihoods.add(currentLocation[longestIndex], newList.get(i));
        		}
        		for (int i = longestIndex+1; i < currentLocation.length; i++) {
        			currentLocation[i]++;
        		}
        		//compound = new ThreadedCompoundLikelihood(likelihoods);
        		compound = new CompoundLikelihood(likelihoods);
        		siteCounts[longestIndex] = (instanceCount-1)*siteCounts[longestIndex]/instanceCount;
        		longestSize = (instanceCount-1)*longestSize/instanceCount;

        		//check number of likelihoods
        		System.err.println("Number of BeagleTreeLikelihoods: " + compound.getLikelihoodCount());
        		System.err.println("Pattern counts: ");
        		for (int i = 0;i < siteCounts.length; i++) {
        			System.err.print(siteCounts[i] + " ");
        		}
        		System.err.println();
        		System.err.println("Instance counts: ");
        		for (int i = 0;i < instanceCounts.length; i++) {
        			System.err.print(instanceCounts[i] + " ");
        		}
        		System.err.println();
        		System.err.println("Current locations: ");
        		for (int i = 0;i < currentLocation.length; i++) {
        			System.err.print(currentLocation[i] + " ");
        		}
        		System.err.println();

        		//evaluate speed
        		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("New evaluation took: " + newResult + " vs. old evaluation: " + baseResult);

        		if (newResult < baseResult) {
            		baseResult = newResult;
            	} else {
            		notFinished = false;

            		//remove 1 instanceCount
            		System.err.print("Removing 1 instance count: " + instanceCount);
            		instanceCount = --instanceCounts[longestIndex];
            		System.err.println(" -> " + instanceCount + " for likelihood " + longestIndex);
            		newList = new ArrayList<Likelihood>();
                	for (int i = 0; i < instanceCount; i++) {
                		Patterns subPatterns = new Patterns(patterns[longestIndex], 0, 0, 1, i, instanceCount);

                		BeagleTreeLikelihood treeLikelihood = createTreeLikelihood(
                                subPatterns, treeModels[longestIndex], branchModels[longestIndex], siteRateModels[longestIndex], branchRateModels[longestIndex],
                                null, 
                                ambiguities[longestIndex], rescalingSchemes[longestIndex], partialsRestrictions.get(longestIndex),
                                xo);

                        treeLikelihood.setId(xo.getId() + "_" + instanceCount);
                        newList.add(treeLikelihood);
                	}
                	for (int i = 0; i < newList.size()+1; i++) {
            			likelihoods.remove(currentLocation[longestIndex]);
            		}
                	for (int i = 0; i < newList.size(); i++) {
                		likelihoods.add(currentLocation[longestIndex], newList.get(i));
                	}
                	for (int i = longestIndex+1; i < currentLocation.length; i++) {
            			currentLocation[i]--;
            		}
                	//likelihoods.remove(longestIndex);
                	//likelihoods.add(longestIndex, new CompoundLikelihood(newList));

                	//compound = new ThreadedCompoundLikelihood(likelihoods);
                	compound = new CompoundLikelihood(likelihoods);
                	siteCounts[longestIndex] = (instanceCount+1)*siteCounts[longestIndex]/instanceCount;
                	longestSize = (instanceCount+1)*longestSize/instanceCount;

                	System.err.println("Pattern counts: ");
                	for (int i = 0;i < siteCounts.length; i++) {
                		System.err.print(siteCounts[i] + " ");
                	}
                	System.err.println();
                	System.err.println("Instance counts: ");
                	for (int i = 0;i < instanceCounts.length; i++) {
                		System.err.print(instanceCounts[i] + " ");
                	}
                	System.err.println();
                	System.err.println("Current locations: ");
            		for (int i = 0;i < currentLocation.length; i++) {
            			System.err.print(currentLocation[i] + " ");
            		}
            		System.err.println();

            	}

        	}

        } else {*/
    // Try splitting the same likelihood until no further improvement, then move on towards the next one
    boolean notFinished = true;
    // construct list with likelihoods to split up
    List<Integer> splitList = new ArrayList<Integer>();
    for (int i = 0; i < siteCounts.length; i++) {
        int top = 0;
        for (int j = 0; j < siteCounts.length; j++) {
            if (siteCounts[j] > siteCounts[top]) {
                top = j;
            }
        }
        siteCounts[top] = 0;
        splitList.add(top);
    }
    for (int i = 0; i < likelihoods.size(); i++) {
        siteCounts[i] = patterns[i].getPatternCount();
        if (DEBUG) {
            System.err.println("Site count " + i + " = " + siteCounts[i]);
        }
    }
    if (DEBUG) {
        // print list
        System.err.print("Ordered list of likelihoods to be evaluated: ");
        for (int i = 0; i < splitList.size(); i++) {
            System.err.print(splitList.get(i) + " ");
        }
        System.err.println();
    }
    int timesRetried = 0;
    while (notFinished) {
        // split it in 1 more piece
        longestIndex = splitList.get(0);
        int instanceCount = ++instanceCounts[longestIndex];
        List<Likelihood> newList = new ArrayList<Likelihood>();
        for (int i = 0; i < instanceCount; i++) {
            Patterns subPatterns = new Patterns(patterns[longestIndex], 0, 0, 1, i, instanceCount);
            BeagleTreeLikelihood treeLikelihood = createTreeLikelihood(subPatterns, treeModels[longestIndex], branchModels[longestIndex], siteRateModels[longestIndex], branchRateModels[longestIndex], null, ambiguities[longestIndex], rescalingSchemes[longestIndex], isDelayRescalingUntilUnderflow[longestIndex], partialsRestrictions.get(longestIndex), xo);
            treeLikelihood.setId(xo.getId() + "_" + longestIndex + "_" + i);
            System.err.println(treeLikelihood.getId() + " created.");
            newList.add(treeLikelihood);
        }
        for (int i = 0; i < newList.size() - 1; i++) {
            likelihoods.remove(currentLocation[longestIndex]);
        }
        // likelihoods.add(longestIndex, new CompoundLikelihood(newList));
        for (int i = 0; i < newList.size(); i++) {
            likelihoods.add(currentLocation[longestIndex], newList.get(i));
        }
        for (int i = longestIndex + 1; i < currentLocation.length; i++) {
            currentLocation[i]++;
        }
        compound = new TestThreadedCompoundLikelihood(likelihoods);
        // compound = new CompoundLikelihood(likelihoods);
        // compound = new ThreadedCompoundLikelihood(likelihoods);
        siteCounts[longestIndex] = (instanceCount - 1) * siteCounts[longestIndex] / instanceCount;
        longestSize = (instanceCount - 1) * longestSize / instanceCount;
        if (DEBUG) {
            // check number of likelihoods
            System.err.println("Number of BeagleTreeLikelihoods: " + compound.getLikelihoodCount());
            System.err.println("Pattern counts: ");
            for (int i = 0; i < siteCounts.length; i++) {
                System.err.print(siteCounts[i] + " ");
            }
            System.err.println();
            System.err.println("Instance counts: ");
            for (int i = 0; i < instanceCounts.length; i++) {
                System.err.print(instanceCounts[i] + " ");
            }
            System.err.println();
            System.err.println("Current locations: ");
            for (int i = 0; i < currentLocation.length; i++) {
                System.err.print(currentLocation[i] + " ");
            }
            System.err.println();
        }
        // evaluate speed
        if (DEBUG) {
            System.err.println("Timing estimates for each of the " + calibrate + " likelihood calculations:");
        }
        start = System.nanoTime();
        for (int i = 0; i < calibrate; i++) {
            if (DEBUG) {
                // double debugStart = System.nanoTime();
                compound.makeDirty();
                compound.getLogLikelihood();
            // double debugEnd = System.nanoTime();
            // System.err.println(debugEnd - debugStart);
            } else {
                compound.makeDirty();
                compound.getLogLikelihood();
            }
        }
        end = System.nanoTime();
        double newResult = end - start;
        if (DEBUG) {
            System.err.println("New evaluation took: " + newResult + " vs. old evaluation: " + baseResult);
        }
        if (newResult < baseResult) {
            // new partitioning is faster, so partition further
            baseResult = newResult;
            // reorder split list
            if (DEBUG) {
                System.err.print("Current split list: ");
                for (int i = 0; i < splitList.size(); i++) {
                    System.err.print(splitList.get(i) + "  ");
                }
                System.err.println();
                System.err.print("Current pattern counts: ");
                for (int i = 0; i < splitList.size(); i++) {
                    System.err.print(siteCounts[splitList.get(i)] + "  ");
                }
                System.err.println();
            }
            int currentPatternCount = siteCounts[longestIndex];
            int findIndex = 0;
            for (int i = 0; i < splitList.size(); i++) {
                if (siteCounts[splitList.get(i)] > currentPatternCount) {
                    findIndex = i;
                }
            }
            if (DEBUG) {
                System.err.println("Current pattern count: " + currentPatternCount);
                System.err.println("Index found: " + findIndex + " with pattern count: " + siteCounts[findIndex]);
                System.err.println("Moving 0 to " + findIndex);
            }
            for (int i = 0; i < findIndex; i++) {
                int temp = splitList.get(i);
                splitList.set(i, splitList.get(i + 1));
                splitList.set(i + 1, temp);
            }
            if (DEBUG) {
                System.err.print("New split list: ");
                for (int i = 0; i < splitList.size(); i++) {
                    System.err.print(splitList.get(i) + "  ");
                }
                System.err.println();
                System.err.print("New pattern counts: ");
                for (int i = 0; i < splitList.size(); i++) {
                    System.err.print(siteCounts[splitList.get(i)] + "  ");
                }
                System.err.println();
            }
            timesRetried = 0;
        } else {
            if (DEBUG) {
                System.err.println("timesRetried = " + timesRetried + " vs. retry = " + retry);
            }
            // new partitioning is slower, so reinstate previous state unless RETRY is specified
            if (timesRetried < retry) {
                // try splitting further any way
                // do not set baseResult
                timesRetried++;
                if (DEBUG) {
                    System.err.println("RETRY number " + timesRetried);
                }
            } else {
                splitList.remove(0);
                if (splitList.size() == 0) {
                    notFinished = false;
                }
                // remove timesTried instanceCount(s)
                if (DEBUG) {
                    System.err.print("Removing " + (timesRetried + 1) + " instance count(s): " + instanceCount);
                }
                // instanceCount = --instanceCounts[longestIndex];
                instanceCounts[longestIndex] = instanceCounts[longestIndex] - (timesRetried + 1);
                instanceCount = instanceCounts[longestIndex];
                if (DEBUG) {
                    System.err.println(" -> " + instanceCount + " for likelihood " + longestIndex);
                }
                newList = new ArrayList<Likelihood>();
                for (int i = 0; i < instanceCount; i++) {
                    Patterns subPatterns = new Patterns(patterns[longestIndex], 0, 0, 1, i, instanceCount);
                    BeagleTreeLikelihood treeLikelihood = createTreeLikelihood(subPatterns, treeModels[longestIndex], branchModels[longestIndex], siteRateModels[longestIndex], branchRateModels[longestIndex], null, ambiguities[longestIndex], rescalingSchemes[longestIndex], isDelayRescalingUntilUnderflow[longestIndex], partialsRestrictions.get(longestIndex), xo);
                    treeLikelihood.setId(xo.getId() + "_" + longestIndex + "_" + i);
                    System.err.println(treeLikelihood.getId() + " created.");
                    newList.add(treeLikelihood);
                }
                /*for (int i = 0; i < newList.size()+1; i++) {
                        likelihoods.remove(currentLocation[longestIndex]);
                    }*/
                for (int i = 0; i < newList.size() + timesRetried + 1; i++) {
                    // TEST CODE START
                    unregisterAllModels((BeagleTreeLikelihood) likelihoods.get(currentLocation[longestIndex]));
                    // TEST CODE END
                    likelihoods.remove(currentLocation[longestIndex]);
                }
                for (int i = 0; i < newList.size(); i++) {
                    likelihoods.add(currentLocation[longestIndex], newList.get(i));
                }
                for (int i = longestIndex + 1; i < currentLocation.length; i++) {
                    currentLocation[i] -= (timesRetried + 1);
                }
                // likelihoods.remove(longestIndex);
                // likelihoods.add(longestIndex, new CompoundLikelihood(newList));
                compound = new TestThreadedCompoundLikelihood(likelihoods);
                // compound = new CompoundLikelihood(likelihoods);
                // compound = new ThreadedCompoundLikelihood(likelihoods);
                siteCounts[longestIndex] = (instanceCount + timesRetried + 1) * siteCounts[longestIndex] / instanceCount;
                longestSize = (instanceCount + timesRetried + 1) * longestSize / instanceCount;
                if (DEBUG) {
                    System.err.println("Pattern counts: ");
                    for (int i = 0; i < siteCounts.length; i++) {
                        System.err.print(siteCounts[i] + " ");
                    }
                    System.err.println();
                    System.err.println("Instance counts: ");
                    for (int i = 0; i < instanceCounts.length; i++) {
                        System.err.print(instanceCounts[i] + " ");
                    }
                    System.err.println();
                    System.err.println("Current locations: ");
                    for (int i = 0; i < currentLocation.length; i++) {
                        System.err.print(currentLocation[i] + " ");
                    }
                    System.err.println();
                }
                timesRetried = 0;
            }
        }
    }
    for (int i = 0; i < originalLikelihoods.size(); i++) {
        unregisterAllModels((BeagleTreeLikelihood) originalLikelihoods.get(i));
    }
    return compound;
}