Examples with TreeModel dr.evomodel.tree.TreeModel used on opensource projects

Example 96 with TreeModel

use of dr.evomodel.tree.TreeModel in project beast-mcmc by beast-dev.

the class RelaxedDriftModelParser method parseXMLObject.

public Object parseXMLObject(XMLObject xo) throws XMLParseException {
    TreeModel tree = (TreeModel) xo.getChild(TreeModel.class);
    Parameter ratesParameter = (Parameter) xo.getElementFirstChild(RATES);
    Parameter rateIndicatorParameter = (Parameter) xo.getElementFirstChild(RATE_IND);
    Parameter driftRates = null;
    if (xo.hasChildNamed(DRIFT_RATES)) {
        driftRates = (Parameter) xo.getElementFirstChild(DRIFT_RATES);
    }
    Logger.getLogger("dr.evomodel").info("Using relaxed drift model.");
    return new RelaxedDriftModel(tree, rateIndicatorParameter, ratesParameter, driftRates);
}

Example 97 with TreeModel

use of dr.evomodel.tree.TreeModel in project beast-mcmc by beast-dev.

the class ScaledTreeLengthRateModelParser method parseXMLObject.

public Object parseXMLObject(XMLObject xo) throws XMLParseException {
    TreeModel tree = (TreeModel) xo.getChild(TreeModel.class);
    Parameter totalLength = (Parameter) xo.getElementFirstChild(SCALING_FACTOR);
    if (totalLength == null) {
        totalLength = new Parameter.Default(1, 1.0);
    }
    Logger.getLogger("dr.evomodel.branchratemodel").info("\n ---------------------------------\nCreating ScaledTreeLengthRateModel model.");
    Logger.getLogger("dr.evomodel.branchratemodel").info("\tTotal tree length will be scaled to " + totalLength.getParameterValue(0) + ".");
    Logger.getLogger("dr.evomodel.branchratemodel").info("\tIf you publish results using this rate model, please cite Alekseyenko, Lee and Suchard (2008) Syst. Biol 57: 772-784." + "\n---------------------------------\n");
    return new ScaledTreeLengthRateModel(tree, totalLength);
}

Example 98 with TreeModel

use of dr.evomodel.tree.TreeModel in project beast-mcmc by beast-dev.

the class GMRFSkyrideLikelihoodParser method parseXMLObject.

public Object parseXMLObject(XMLObject xo) throws XMLParseException {
    XMLObject cxo = xo.getChild(POPULATION_PARAMETER);
    Parameter popParameter = (Parameter) cxo.getChild(Parameter.class);
    cxo = xo.getChild(PRECISION_PARAMETER);
    Parameter precParameter = (Parameter) cxo.getChild(Parameter.class);
    cxo = xo.getChild(POPULATION_TREE);
    List<Tree> treeList = new ArrayList<Tree>();
    for (int i = 0; i < cxo.getChildCount(); i++) {
        Object testObject = cxo.getChild(i);
        if (testObject instanceof Tree) {
            treeList.add((TreeModel) testObject);
        }
    }
    //        TreeModel treeModel = (TreeModel) cxo.getChild(TreeModel.class);
    cxo = xo.getChild(GROUP_SIZES);
    Parameter groupParameter = null;
    if (cxo != null) {
        groupParameter = (Parameter) cxo.getChild(Parameter.class);
        if (popParameter.getDimension() != groupParameter.getDimension())
            throw new XMLParseException("Population and group size parameters must have the same length");
    }
    Parameter lambda;
    if (xo.getChild(LAMBDA_PARAMETER) != null) {
        cxo = xo.getChild(LAMBDA_PARAMETER);
        lambda = (Parameter) cxo.getChild(Parameter.class);
    } else {
        lambda = new Parameter.Default(LAMBDA_PARAMETER, 1.0);
    }
    Parameter gridPoints = null;
    if (xo.getChild(GRID_POINTS) != null) {
        cxo = xo.getChild(GRID_POINTS);
        gridPoints = (Parameter) cxo.getChild(Parameter.class);
    }
    Parameter numGridPoints = null;
    if (xo.getChild(NUM_GRID_POINTS) != null) {
        cxo = xo.getChild(NUM_GRID_POINTS);
        numGridPoints = (Parameter) cxo.getChild(Parameter.class);
    }
    Parameter cutOff = null;
    if (xo.getChild(CUT_OFF) != null) {
        cxo = xo.getChild(CUT_OFF);
        cutOff = (Parameter) cxo.getChild(Parameter.class);
    }
    Parameter phi = null;
    if (xo.getChild(PHI_PARAMETER) != null) {
        cxo = xo.getChild(PHI_PARAMETER);
        phi = (Parameter) cxo.getChild(Parameter.class);
    }
    List<Parameter> lastObservedIndex = null;
    if (xo.hasChildNamed(LAST_OBSERVED_INDEX)) {
        lastObservedIndex = new ArrayList<Parameter>();
        cxo = xo.getChild(LAST_OBSERVED_INDEX);
        final int numObsInd = cxo.getChildCount();
        for (int i = 0; i < numObsInd; ++i) {
            lastObservedIndex.add((Parameter) cxo.getChild(i));
        }
    }
    Parameter ploidyFactors = null;
    if (xo.getChild(PLOIDY) != null) {
        cxo = xo.getChild(PLOIDY);
        ploidyFactors = (Parameter) cxo.getChild(Parameter.class);
    } else {
        ploidyFactors = new Parameter.Default(PLOIDY, treeList.size());
        for (int i = 0; i < treeList.size(); i++) {
            ploidyFactors.setParameterValue(i, 1.0);
        }
    }
    Parameter betaParameter = null;
    if (xo.hasChildNamed(SINGLE_BETA)) {
        betaParameter = (Parameter) xo.getElementFirstChild(SINGLE_BETA);
    }
    List<Parameter> betaList = null;
    if (xo.getChild(BETA_PARAMETER) != null) {
        betaList = new ArrayList<Parameter>();
        cxo = xo.getChild(BETA_PARAMETER);
        final int numBeta = cxo.getChildCount();
        for (int i = 0; i < numBeta; ++i) {
            betaList.add((Parameter) cxo.getChild(i));
        }
    }
    MatrixParameter dMatrix = null;
    if (xo.getChild(COVARIATE_MATRIX) != null) {
        cxo = xo.getChild(COVARIATE_MATRIX);
        dMatrix = (MatrixParameter) cxo.getChild(MatrixParameter.class);
    }
    boolean timeAwareSmoothing = GMRFSkyrideLikelihood.TIME_AWARE_IS_ON_BY_DEFAULT;
    if (xo.hasAttribute(TIME_AWARE_SMOOTHING)) {
        timeAwareSmoothing = xo.getBooleanAttribute(TIME_AWARE_SMOOTHING);
    }
    if (dMatrix != null) {
        if (dMatrix.getRowDimension() != popParameter.getDimension())
            throw new XMLParseException("Design matrix row dimension must equal the population parameter length.");
        if (dMatrix.getColumnDimension() != betaParameter.getDimension())
            throw new XMLParseException("Design matrix column dimension must equal the regression coefficient length.");
    }
    List<Parameter> covPrecParam = null;
    if (xo.hasChildNamed(COV_PREC_PARAM)) {
        covPrecParam = new ArrayList<Parameter>();
        cxo = xo.getChild(COV_PREC_PARAM);
        final int numCovPrec = cxo.getChildCount();
        for (int i = 0; i < numCovPrec; ++i) {
            covPrecParam.add((Parameter) cxo.getChild(i));
        }
    }
    List<MatrixParameter> covariates = null;
    if (xo.hasChildNamed(COVARIATES)) {
        covariates = new ArrayList<MatrixParameter>();
        cxo = xo.getChild(COVARIATES);
        final int numCov = cxo.getChildCount();
        for (int i = 0; i < numCov; ++i) {
            covariates.add((MatrixParameter) cxo.getChild(i));
        }
    }
    if ((covariates != null && betaList == null) || (covariates == null && betaList != null))
        throw new XMLParseException("Must specify both a set of regression coefficients and a design matrix.");
    if (xo.getAttribute(RANDOMIZE_TREE, false)) {
        for (Tree tree : treeList) {
            if (tree instanceof TreeModel) {
                GMRFSkyrideLikelihood.checkTree((TreeModel) tree);
            } else {
                throw new XMLParseException("Can not randomize a fixed tree");
            }
        }
    }
    boolean rescaleByRootHeight = xo.getAttribute(RESCALE_BY_ROOT_ISSUE, true);
    Logger.getLogger("dr.evomodel").info("The " + SKYLINE_LIKELIHOOD + " has " + (timeAwareSmoothing ? "time aware smoothing" : "uniform smoothing"));
    if (xo.getAttribute(OLD_SKYRIDE, true) && xo.getName().compareTo(SKYGRID_LIKELIHOOD) != 0) {
        return new GMRFSkyrideLikelihood(treeList, popParameter, groupParameter, precParameter, lambda, betaParameter, dMatrix, timeAwareSmoothing, rescaleByRootHeight);
    } else {
        if (xo.getChild(GRID_POINTS) != null) {
            return new GMRFMultilocusSkyrideLikelihood(treeList, popParameter, groupParameter, precParameter, lambda, betaParameter, dMatrix, timeAwareSmoothing, gridPoints, covariates, ploidyFactors, lastObservedIndex, covPrecParam, betaList);
        } else {
            return new GMRFMultilocusSkyrideLikelihood(treeList, popParameter, groupParameter, precParameter, lambda, betaParameter, dMatrix, timeAwareSmoothing, cutOff.getParameterValue(0), (int) numGridPoints.getParameterValue(0), phi, ploidyFactors);
        }
    }
}

Example 99 with TreeModel

use of dr.evomodel.tree.TreeModel in project beast-mcmc by beast-dev.

the class WilsonBaldingParser method parseXMLObject.

public Object parseXMLObject(XMLObject xo) throws XMLParseException {
    final double weight = xo.getDoubleAttribute(MCMCOperator.WEIGHT);
    final TreeModel treeModel = (TreeModel) xo.getChild(TreeModel.class);
    return new WilsonBalding(treeModel, weight);
}

Example 100 with TreeModel

use of dr.evomodel.tree.TreeModel in project beast-mcmc by beast-dev.

the class GaussianProcessSkytrackTreeOperatorParser method parseXMLObject.

public Object parseXMLObject(XMLObject xo) throws XMLParseException {
    //        boolean logRecord = xo.getAttribute(KEEP_LOG_RECORD, false);
    //        Handler gmrfHandler;
    //        Logger gmrfLogger = Logger.getLogger("dr.evomodel.coalescent.operators.GaussianProcessSkytrackBlockUpdateOperator");
    //        gmrfLogger.setUseParentHandlers(false);
    //        if (logRecord) {
    //            gmrfLogger.setLevel(Level.FINE);
    //
    //            try {
    //                gmrfHandler = new FileHandler("GPBlockUpdate.log." + MathUtils.getSeed());
    //            } catch (IOException e) {
    //                throw new RuntimeException(e.getMessage());
    //            }
    //            gmrfHandler.setLevel(Level.FINE);
    //
    //            gmrfHandler.setFormatter(new XMLFormatter() {
    //                public String format(LogRecord record) {
    //                    return "<record>\n \t<message>\n\t" + record.getMessage()
    //                            + "\n\t</message>\n<record>\n";
    //                }
    //            });
    //
    //            gmrfLogger.addHandler(gmrfHandler);
    //        }
    CoercionMode mode = CoercionMode.parseMode(xo);
    if (mode == CoercionMode.DEFAULT)
        mode = CoercionMode.COERCION_ON;
    double weight = xo.getDoubleAttribute(MCMCOperator.WEIGHT);
    double scaleFactor = xo.getDoubleAttribute(SCALE_FACTOR);
    //            if (scaleFactor <= 0.0) {
    //                throw new XMLParseException("scaleFactor must be greater than 0.0");
    //        if (scaleFactor < 1.0) {
    //            throw new XMLParseException("scaleFactor must be greater than or equal to 1.0");
    //        }
    //        int maxIterations = xo.getAttribute(MAX_ITERATIONS, 200);
    //        double stopValue = xo.getAttribute(STOP_VALUE, 0.01);
    GaussianProcessSkytrackLikelihood gpLikelihood = (GaussianProcessSkytrackLikelihood) xo.getChild(GaussianProcessSkytrackLikelihood.class);
    TreeModel treeModel = (TreeModel) xo.getChild(TreeModel.class);
    return new GaussianProcessSkytrackTreeOperator(treeModel, gpLikelihood, weight, scaleFactor, mode);
}