Examples with CompoundGaussianProcess dr.math.distributions.CompoundGaussianProcess used on opensource projects

Example 1 with CompoundGaussianProcess

use of dr.math.distributions.CompoundGaussianProcess in project beast-mcmc by beast-dev.

the class CompoundGaussianProcessParser method parseXMLObject.

public Object parseXMLObject(XMLObject xo) throws XMLParseException {
    List<GaussianProcessRandomGenerator> gpList = new ArrayList<GaussianProcessRandomGenerator>();
    List<Likelihood> likelihoodList = new ArrayList<Likelihood>();
    List<Integer> copyList = new ArrayList<Integer>();
    for (int i = 0; i < xo.getChildCount(); ++i) {
        Object obj = xo.getChild(i);
        GaussianProcessRandomGenerator gp = null;
        Likelihood likelihood = null;
        int copies = -1;
        if (obj instanceof DistributionLikelihood) {
            DistributionLikelihood dl = (DistributionLikelihood) obj;
            if (!(dl.getDistribution() instanceof GaussianProcessRandomGenerator)) {
                throw new XMLParseException("Not a Gaussian process");
            }
            likelihood = dl;
            gp = (GaussianProcessRandomGenerator) dl.getDistribution();
            copies = 0;
            for (Attribute<double[]> datum : dl.getDataList()) {
                // Double draw = (Double) gp.nextRandom();
                // System.err.println("DL: " + datum.getAttributeName() + " " + datum.getAttributeValue().length + " " + "1");
                copies += datum.getAttributeValue().length;
            }
        } else if (obj instanceof MultivariateDistributionLikelihood) {
            MultivariateDistributionLikelihood mdl = (MultivariateDistributionLikelihood) obj;
            if (!(mdl.getDistribution() instanceof GaussianProcessRandomGenerator)) {
                throw new XMLParseException("Not a Gaussian process");
            }
            likelihood = mdl;
            gp = (GaussianProcessRandomGenerator) mdl.getDistribution();
            copies = 0;
            double[] draw = (double[]) gp.nextRandom();
            for (Attribute<double[]> datum : mdl.getDataList()) {
                // System.err.println("ML: " + datum.getAttributeName() + " " + datum.getAttributeValue().length + " " + draw.length);
                copies += datum.getAttributeValue().length / draw.length;
            }
        } else if (obj instanceof GaussianProcessRandomGenerator) {
            gp = (GaussianProcessRandomGenerator) obj;
            likelihood = gp.getLikelihood();
            copies = 1;
        } else {
            throw new XMLParseException("Not a Gaussian process");
        }
        gpList.add(gp);
        likelihoodList.add(likelihood);
        copyList.add(copies);
    }
    // System.exit(-1);
    return new CompoundGaussianProcess(gpList, likelihoodList, copyList);
}