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

Example 1 with DiscretizedBranchRates

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

the class PartitionData method createClockRateModel.

public BranchRateModel createClockRateModel() {
    BranchRateModel branchRateModel = null;
    if (this.clockModelIndex == 0) {
        // Strict Clock
        Parameter rateParameter = new Parameter.Default(1, clockParameterValues[0]);
        branchRateModel = new StrictClockBranchRates(rateParameter);
    } else if (this.clockModelIndex == LRC_INDEX) {
        // Lognormal relaxed clock
        double numberOfBranches = 2 * (createTreeModel().getTaxonCount() - 1);
        Parameter rateCategoryParameter = new Parameter.Default(numberOfBranches);
        Parameter mean = new Parameter.Default(LogNormalDistributionModelParser.MEAN, 1, clockParameterValues[1]);
        Parameter stdev = new Parameter.Default(LogNormalDistributionModelParser.STDEV, 1, clockParameterValues[2]);
        //TODO: choose between log scale / real scale
        ParametricDistributionModel distributionModel = new LogNormalDistributionModel(mean, stdev, clockParameterValues[3], lrcParametersInRealSpace, lrcParametersInRealSpace);
        branchRateModel = new //
        DiscretizedBranchRates(//
        createTreeModel(), //
        rateCategoryParameter, //
        distributionModel, // 
        1, // 
        false, //
        Double.NaN, //randomizeRates
        true, // keepRates
        false, // cacheRates
        false);
    } else if (this.clockModelIndex == 2) {
        // Exponential relaxed clock
        double numberOfBranches = 2 * (createTreeModel().getTaxonCount() - 1);
        Parameter rateCategoryParameter = new Parameter.Default(numberOfBranches);
        Parameter mean = new Parameter.Default(DistributionModelParser.MEAN, 1, clockParameterValues[4]);
        ParametricDistributionModel distributionModel = new ExponentialDistributionModel(mean, clockParameterValues[5]);
        //	        branchRateModel = new DiscretizedBranchRates(createTreeModel(), rateCategoryParameter, 
        //	                distributionModel, 1, false, Double.NaN);
        branchRateModel = new //
        DiscretizedBranchRates(//
        createTreeModel(), //
        rateCategoryParameter, //
        distributionModel, // 
        1, // 
        false, //
        Double.NaN, //randomizeRates
        true, // keepRates
        false, // cacheRates
        false);
    } else if (this.clockModelIndex == 3) {
        // Inverse Gaussian
        double numberOfBranches = 2 * (createTreeModel().getTaxonCount() - 1);
        Parameter rateCategoryParameter = new Parameter.Default(numberOfBranches);
        Parameter mean = new Parameter.Default(InverseGaussianDistributionModelParser.MEAN, 1, clockParameterValues[6]);
        Parameter stdev = new Parameter.Default(InverseGaussianDistributionModelParser.STDEV, 1, clockParameterValues[7]);
        ParametricDistributionModel distributionModel = new InverseGaussianDistributionModel(mean, stdev, clockParameterValues[8], false);
        branchRateModel = new //
        DiscretizedBranchRates(//
        createTreeModel(), //
        rateCategoryParameter, //
        distributionModel, // 
        1, // 
        false, //
        Double.NaN, //randomizeRates
        true, // keepRates
        false, // cacheRates
        false);
    } else {
        System.out.println("Not yet implemented");
    }
    return branchRateModel;
}

Example 2 with DiscretizedBranchRates

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

the class DiscretizedBranchRatesParser method parseXMLObject.

public Object parseXMLObject(XMLObject xo) throws XMLParseException {
    final int overSampling = xo.getAttribute(OVERSAMPLING, 1);
    //final boolean normalize = xo.getBooleanAttribute(NORMALIZE, false);
    final boolean normalize = xo.getAttribute(NORMALIZE, false);
    /*if(xo.hasAttribute(NORMALIZE))
            normalize = xo.getBooleanAttribute(NORMALIZE);
        }*/
    //final double normalizeBranchRateTo = xo.getDoubleAttribute(NORMALIZE_BRANCH_RATE_TO);
    final double normalizeBranchRateTo = xo.getAttribute(NORMALIZE_BRANCH_RATE_TO, Double.NaN);
    TreeModel tree = (TreeModel) xo.getChild(TreeModel.class);
    ParametricDistributionModel distributionModel = (ParametricDistributionModel) xo.getElementFirstChild(DISTRIBUTION);
    Parameter rateCategoryParameter = (Parameter) xo.getElementFirstChild(RATE_CATEGORIES);
    Logger.getLogger("dr.evomodel").info("\nUsing discretized relaxed clock model.");
    Logger.getLogger("dr.evomodel").info("  over sampling = " + overSampling);
    Logger.getLogger("dr.evomodel").info("  parametric model = " + distributionModel.getModelName());
    Logger.getLogger("dr.evomodel").info("   rate categories = " + rateCategoryParameter.getDimension());
    if (normalize) {
        Logger.getLogger("dr.evomodel").info("   mean rate is normalized to " + normalizeBranchRateTo);
    }
    if (xo.hasAttribute(SINGLE_ROOT_RATE)) {
        //singleRootRate = xo.getBooleanAttribute(SINGLE_ROOT_RATE);
        Logger.getLogger("dr.evomodel").warning("   WARNING: single root rate is not implemented!");
    }
    final boolean randomizeRates = xo.getAttribute(RANDOMIZE_RATES, true);
    final boolean keepRates = xo.getAttribute(KEEP_RATES, false);
    final boolean cachedRates = xo.getAttribute(CACHED_RATES, false);
    if (randomizeRates && keepRates) {
        throw new XMLParseException("Unable to both randomize and keep current rate categories");
    }
    return new DiscretizedBranchRates(tree, rateCategoryParameter, distributionModel, overSampling, normalize, normalizeBranchRateTo, randomizeRates, keepRates, cachedRates);
}