public Object parseXMLObject(XMLObject xo) throws XMLParseException {
Parameter weights = (Parameter) xo.getChild(Parameter.class);
List<AbstractModelLikelihood> likelihoodList = new ArrayList<AbstractModelLikelihood>();
for (int i = 0; i < xo.getChildCount(); i++) {
if (xo.getChild(i) instanceof Likelihood)
likelihoodList.add((AbstractModelLikelihood) xo.getChild(i));
}
if (weights.getDimension() != likelihoodList.size()) {
throw new XMLParseException(
"Dim of " + weights.getId() + " does not match the number of likelihoods");
}
if (xo.hasAttribute(NORMALIZE)) {
if (xo.getBooleanAttribute(NORMALIZE)) {
double sum = 0;
for (int i = 0; i < weights.getDimension(); i++) sum += weights.getParameterValue(i);
for (int i = 0; i < weights.getDimension(); i++)
weights.setParameterValue(i, weights.getParameterValue(i) / sum);
}
}
if (!normalized(weights))
throw new XMLParseException(
"Parameter +" + weights.getId() + " must lie on the simplex");
return new WeightedMixtureModel(likelihoodList, weights);
}
private boolean normalized(Parameter p) {
double sum = 0;
for (int i = 0; i < p.getDimension(); i++) sum += p.getParameterValue(i);
return (sum == 1.0);
}
public Object parseXMLObject(XMLObject xo) throws XMLParseException {
double[] shape;
double[] scale;
if (xo.hasChildNamed(MVGAMMA_SHAPE)) {
XMLObject cxo = xo.getChild(MVGAMMA_SHAPE);
shape = ((Parameter) cxo.getChild(Parameter.class)).getParameterValues();
cxo = xo.getChild(MVGAMMA_SCALE);
scale = ((Parameter) cxo.getChild(Parameter.class)).getParameterValues();
if (shape.length != scale.length)
throw new XMLParseException(
"Shape and scale have wrong dimensions in " + xo.getName() + " element");
} else {
XMLObject cxo = xo.getChild(MVN_MEAN);
double[] mean = ((Parameter) cxo.getChild(Parameter.class)).getParameterValues();
cxo = xo.getChild(MVN_CV);
double[] cv = ((Parameter) cxo.getChild(Parameter.class)).getParameterValues();
if (mean.length != cv.length)
throw new XMLParseException(
"Mean and CV have wrong dimensions in " + xo.getName() + " element");
final int dim = mean.length;
shape = new double[dim];
scale = new double[dim];
for (int i = 0; i < dim; i++) {
double c2 = cv[i] * cv[i];
shape[i] = 1.0 / c2;
scale[i] = c2 * mean[i];
}
}
MultivariateDistributionLikelihood likelihood =
new MultivariateDistributionLikelihood(
new MultivariateGammaDistribution(shape, scale));
XMLObject cxo = xo.getChild(DATA);
for (int j = 0; j < cxo.getChildCount(); j++) {
if (cxo.getChild(j) instanceof Parameter) {
Parameter data = (Parameter) cxo.getChild(j);
likelihood.addData(data);
if (data.getDimension() != shape.length)
throw new XMLParseException(
"dim("
+ data.getStatisticName()
+ ") != "
+ shape.length
+ " in "
+ xo.getName()
+ "element");
} else {
throw new XMLParseException("illegal element in " + xo.getName() + " element");
}
}
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;
}
public MixtureModelBranchRates(
TreeModel tree,
Parameter rateCategoryQuantilesParameter,
ParametricDistributionModel[] models,
Parameter distributionIndexParameter,
boolean useQuantilesForRates,
boolean normalize,
double normalizeBranchRateTo) {
super(MixtureModelBranchRatesParser.MIXTURE_MODEL_BRANCH_RATES);
this.useQuantilesForRates = useQuantilesForRates;
this.rateCategoryQuantiles =
new TreeParameterModel(tree, rateCategoryQuantilesParameter, false);
rates = new double[tree.getNodeCount()];
this.normalize = normalize;
this.treeModel = tree;
this.distributionModels = models;
this.normalizeBranchRateTo = normalizeBranchRateTo;
this.tree = new SimpleTree(tree);
this.distributionIndexParameter = distributionIndexParameter;
addVariable(this.distributionIndexParameter);
// Force the boundaries of rateCategoryParameter to match the category count
// d Parameter.DefaultBounds bound = new Parameter.DefaultBounds(categoryCount - 1, 0,
// rateCategoryParameter.getDimension());
// d rateCategoryParameter.addBounds(bound);
// rateCategoryQuantilesParameter.;
Parameter.DefaultBounds bound =
new Parameter.DefaultBounds(1.0, 0.0, rateCategoryQuantilesParameter.getDimension());
rateCategoryQuantilesParameter.addBounds(bound);
Parameter.DefaultBounds bound2 = new Parameter.DefaultBounds(models.length, 0.0, 1);
distributionIndexParameter.addBounds(bound2);
distributionIndexParameter.setParameterValue(0, 0);
// Parameter distributionIndexParameter;
for (ParametricDistributionModel distributionModel : distributionModels) {
addModel(distributionModel);
}
// AR - commented out: changes to the tree are handled by model changed events fired by
// rateCategories
// addModel(tree);
// d addModel(rateCategories);
addModel(rateCategoryQuantiles);
// addModel(treeModel); // Maybe
// AR - commented out: changes to rateCategoryParameter are handled by model changed events
// fired by rateCategories
// addVariable(rateCategoryParameter);
if (normalize) {
tree.addModelListener(
new ModelListener() {
public void modelChangedEvent(Model model, Object object, int index) {
computeFactor();
}
public void modelRestored(Model model) {
computeFactor();
}
});
}
setupRates();
}