public Object parseXMLObject(XMLObject xo) throws XMLParseException {
MultivariateDistributionLikelihood likelihood;
if (xo.hasAttribute(NON_INFORMATIVE) && xo.getBooleanAttribute(NON_INFORMATIVE)) {
// Make non-informative settings
XMLObject cxo = xo.getChild(DATA);
int dim = ((MatrixParameter) cxo.getChild(0)).getColumnDimension();
likelihood = new MultivariateDistributionLikelihood(new WishartDistribution(dim));
} else {
if (!xo.hasAttribute(DF) || !xo.hasChildNamed(SCALE_MATRIX)) {
throw new XMLParseException("Must specify both a df and scaleMatrix");
}
double df = xo.getDoubleAttribute(DF);
XMLObject cxo = xo.getChild(SCALE_MATRIX);
MatrixParameter scaleMatrix = (MatrixParameter) cxo.getChild(MatrixParameter.class);
likelihood =
new MultivariateDistributionLikelihood(
new WishartDistribution(df, scaleMatrix.getParameterAsMatrix()));
}
XMLObject cxo = xo.getChild(DATA);
for (int j = 0; j < cxo.getChildCount(); j++) {
if (cxo.getChild(j) instanceof MatrixParameter) {
likelihood.addData((MatrixParameter) cxo.getChild(j));
} else {
throw new XMLParseException(
"illegal element in " + xo.getName() + " element " + cxo.getName());
}
}
return likelihood;
}
public Object parseXMLObject(XMLObject xo) throws XMLParseException {
int df = xo.getIntegerAttribute(DF);
XMLObject cxo = xo.getChild(SCALE_MATRIX);
MatrixParameter scaleMatrix = (MatrixParameter) cxo.getChild(MatrixParameter.class);
InverseWishartDistribution invWishart =
new InverseWishartDistribution(df, scaleMatrix.getParameterAsMatrix());
MultivariateDistributionLikelihood likelihood =
new MultivariateDistributionLikelihood(invWishart);
cxo = xo.getChild(DATA);
for (int j = 0; j < cxo.getChildCount(); j++) {
if (cxo.getChild(j) instanceof MatrixParameter) {
likelihood.addData((MatrixParameter) cxo.getChild(j));
} else {
throw new XMLParseException(
"illegal element in " + xo.getName() + " element " + cxo.getName());
}
}
return likelihood;
}
public Object parseXMLObject(XMLObject xo) throws XMLParseException {
XMLObject cxo = xo.getChild(MVN_MEAN);
Parameter mean = (Parameter) cxo.getChild(Parameter.class);
cxo = xo.getChild(MVN_PRECISION);
MatrixParameter precision = (MatrixParameter) cxo.getChild(MatrixParameter.class);
if (mean.getDimension() != precision.getRowDimension()
|| mean.getDimension() != precision.getColumnDimension())
throw new XMLParseException(
"Mean and precision have wrong dimensions in " + xo.getName() + " element");
Transform[] transforms = parseListOfTransforms(xo, mean.getDimension());
MultivariateDistributionLikelihood likelihood =
new MultivariateDistributionLikelihood(
new MultivariateNormalDistribution(
mean.getParameterValues(), precision.getParameterAsMatrix()),
transforms);
cxo = xo.getChild(DATA);
if (cxo != null) {
for (int j = 0; j < cxo.getChildCount(); j++) {
if (cxo.getChild(j) instanceof Parameter) {
Parameter data = (Parameter) cxo.getChild(j);
if (data instanceof MatrixParameter) {
MatrixParameter matrix = (MatrixParameter) data;
if (matrix.getParameter(0).getDimension() != mean.getDimension())
throw new XMLParseException(
"dim("
+ data.getStatisticName()
+ ") = "
+ matrix.getParameter(0).getDimension()
+ " is not equal to dim("
+ mean.getStatisticName()
+ ") = "
+ mean.getDimension()
+ " in "
+ xo.getName()
+ "element");
for (int i = 0; i < matrix.getParameterCount(); i++) {
likelihood.addData(matrix.getParameter(i));
}
} else {
if (data.getDimension() != mean.getDimension())
throw new XMLParseException(
"dim("
+ data.getStatisticName()
+ ") = "
+ data.getDimension()
+ " is not equal to dim("
+ mean.getStatisticName()
+ ") = "
+ mean.getDimension()
+ " in "
+ xo.getName()
+ "element");
likelihood.addData(data);
}
} else {
throw new XMLParseException("illegal element in " + xo.getName() + " element");
}
}
}
return likelihood;
}
public Object parseXMLObject(XMLObject xo) throws XMLParseException {
XMLObject cxo = xo.getChild(DistributionLikelihoodParser.DISTRIBUTION);
ParametricMultivariateDistributionModel distribution =
(ParametricMultivariateDistributionModel)
cxo.getChild(ParametricMultivariateDistributionModel.class);
// Parse transforms here
int maxDim = distribution.getMean().length;
Transform[] transforms = parseListOfTransforms(xo, maxDim);
MultivariateDistributionLikelihood likelihood =
new MultivariateDistributionLikelihood(xo.getId(), distribution, transforms);
boolean dataAsMatrix = xo.getAttribute(DATA_AS_MATRIX, false);
cxo = xo.getChild(DATA);
if (cxo != null) {
for (int j = 0; j < cxo.getChildCount(); j++) {
if (cxo.getChild(j) instanceof Parameter) {
Parameter data = (Parameter) cxo.getChild(j);
if (data instanceof MatrixParameter) {
MatrixParameter matrix = (MatrixParameter) data;
if (dataAsMatrix) {
likelihood.addData(matrix);
} else {
if (matrix.getParameter(0).getDimension() != distribution.getMean().length)
throw new XMLParseException(
"dim("
+ data.getStatisticName()
+ ") = "
+ matrix.getParameter(0).getDimension()
+ " is not equal to dim("
+ distribution.getType()
+ ") = "
+ distribution.getMean().length
+ " in "
+ xo.getName()
+ "element");
for (int i = 0; i < matrix.getParameterCount(); i++) {
likelihood.addData(matrix.getParameter(i));
}
}
} else {
if (data.getDimension() != distribution.getMean().length)
throw new XMLParseException(
"dim("
+ data.getStatisticName()
+ ") = "
+ data.getDimension()
+ " is not equal to dim("
+ distribution.getType()
+ ") = "
+ distribution.getMean().length
+ " in "
+ xo.getName()
+ "element");
likelihood.addData(data);
}
} else {
throw new XMLParseException("illegal element in " + xo.getName() + " element");
}
}
}
return likelihood;
}