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

Example 6 with LatentFactorModel

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

the class LoadingsIndependenceOperatorParser method parseXMLObject.

@Override
public Object parseXMLObject(XMLObject xo) throws XMLParseException {
    CoercionMode mode = CoercionMode.parseMode(xo);
    String scaleFactorTemp = (String) xo.getAttribute(SCALE_FACTOR);
    double scaleFactor = Double.parseDouble(scaleFactorTemp);
    String weightTemp = (String) xo.getAttribute(WEIGHT);
    double weight = Double.parseDouble(weightTemp);
    LatentFactorModel LFM = (LatentFactorModel) xo.getChild(LatentFactorModel.class);
    DistributionLikelihood prior = (DistributionLikelihood) xo.getChild(DistributionLikelihood.class);
    boolean randomScan = xo.getAttribute(RANDOM_SCAN, true);
    //To change body of implemented methods use File | Settings | File Templates.
    return new LoadingsIndependenceOperator(LFM, prior, weight, randomScan, scaleFactor, mode);
}

Example 7 with LatentFactorModel

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

the class LatentFactorHamiltonianMCParser method parseXMLObject.

@Override
public Object parseXMLObject(XMLObject xo) throws XMLParseException {
    LatentFactorModel lfm = (LatentFactorModel) xo.getChild(LatentFactorModel.class);
    FullyConjugateMultivariateTraitLikelihood tree = (FullyConjugateMultivariateTraitLikelihood) xo.getChild(FullyConjugateMultivariateTraitLikelihood.class);
    double weight = xo.getDoubleAttribute(WEIGHT);
    AdaptationMode mode = AdaptationMode.parseMode(xo);
    int nSteps = xo.getIntegerAttribute(N_STEPS);
    double stepSize = xo.getDoubleAttribute(STEP_SIZE);
    double momentumSd = xo.getDoubleAttribute(MOMENTUM_SD);
    return new LatentFactorHamiltonianMC(lfm, tree, weight, mode, stepSize, nSteps, momentumSd);
}

Example 8 with LatentFactorModel

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

the class FactorTreeGibbsOperatorParser method parseXMLObject.

@Override
public Object parseXMLObject(XMLObject xo) throws XMLParseException {
    String weightTemp = (String) xo.getAttribute(WEIGHT);
    double weight = Double.parseDouble(weightTemp);
    LatentFactorModel lfm = (LatentFactorModel) xo.getChild(LatentFactorModel.class);
    GibbsSampleFromTreeInterface tree = (GibbsSampleFromTreeInterface) xo.getChild(GibbsSampleFromTreeInterface.class);
    GibbsSampleFromTreeInterface workingTree = null;
    if (xo.getChild(WORKING_PRIOR) != null) {
        workingTree = (GibbsSampleFromTreeInterface) xo.getChild(WORKING_PRIOR).getChild(GibbsSampleFromTreeInterface.class);
    }
    boolean randomScan = xo.getAttribute(RANDOM_SCAN, true);
    FactorTreeGibbsOperator lfmOp = new FactorTreeGibbsOperator(weight, lfm, tree, randomScan);
    if (xo.hasAttribute(PATH_PARAMETER)) {
        System.out.println("WARNING: Setting Path Parameter is intended for debugging purposes only!");
        lfmOp.setPathParameter(xo.getDoubleAttribute(PATH_PARAMETER));
    }
    return lfmOp;
}

Example 9 with LatentFactorModel

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

the class LatentFactorLiabilityGibbsOperatorParser method parseXMLObject.

@Override
public Object parseXMLObject(XMLObject xo) throws XMLParseException {
    double weight = xo.getDoubleAttribute(WEIGHT);
    LatentFactorModel lfm = (LatentFactorModel) xo.getChild(LatentFactorModel.class);
    OrderedLatentLiabilityLikelihood liabilityLikelihood = (OrderedLatentLiabilityLikelihood) xo.getChild(OrderedLatentLiabilityLikelihood.class);
    return new LatentFactorLiabilityGibbsOperator(weight, lfm, liabilityLikelihood);
}

Example 10 with LatentFactorModel

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

the class FactorIndependenceOperatorParser method parseXMLObject.

@Override
public Object parseXMLObject(XMLObject xo) throws XMLParseException {
    AdaptationMode mode = AdaptationMode.parseMode(xo);
    String scaleFactorTemp = (String) xo.getAttribute(SCALE_FACTOR);
    double scaleFactor = Double.parseDouble(scaleFactorTemp);
    String weightTemp = (String) xo.getAttribute(WEIGHT);
    double weight = Double.parseDouble(weightTemp);
    DiagonalMatrix diffusionMatrix;
    diffusionMatrix = (DiagonalMatrix) xo.getChild(DiagonalMatrix.class);
    LatentFactorModel LFM = (LatentFactorModel) xo.getChild(LatentFactorModel.class);
    boolean randomScan = xo.getAttribute(RANDOM_SCAN, true);
    return new FactorIndependenceOperator(LFM, weight, randomScan, diffusionMatrix, scaleFactor, mode);
}