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);
}
/**
* construct an instance of a parameter type that has a name. the value of {@code params} should
* not contain a <em>null</em> instance, which is not a valid input when creating a {@link
* Parameter}.
*
* @param name name of the parameter.
* @param params parameters that will compose the parameters.
* @return a new {@code Parameter} instance.
*/
public static Parameter makeNamedParameter(String name, Object... params) {
final Parameter param = new SingleValueParameter(name);
for (Object each : params) {
param.setParameterValue(each, discoverClass(each));
}
return param;
}
public static void main(String[] args) {
final double l1 = -10;
final double l2 = -2;
AbstractModelLikelihood like1 =
new AbstractModelLikelihood("dummy") {
public Model getModel() {
return null;
}
public double getLogLikelihood() {
return l1;
}
public void makeDirty() {}
public String prettyName() {
return null;
}
public boolean isUsed() {
return false;
}
@Override
protected void handleModelChangedEvent(Model model, Object object, int index) {}
@Override
protected void handleVariableChangedEvent(
Variable variable, int index, Variable.ChangeType type) {}
@Override
protected void storeState() {}
@Override
protected void restoreState() {}
@Override
protected void acceptState() {}
public void setUsed() {}
public LogColumn[] getColumns() {
return new LogColumn[0];
}
public String getId() {
return null;
}
public void setId(String id) {}
};
AbstractModelLikelihood like2 =
new AbstractModelLikelihood("dummy") {
public Model getModel() {
return null;
}
public double getLogLikelihood() {
return l2;
}
public void makeDirty() {}
public String prettyName() {
return null;
}
public boolean isUsed() {
return false;
}
@Override
protected void handleModelChangedEvent(Model model, Object object, int index) {}
@Override
protected void handleVariableChangedEvent(
Variable variable, int index, Variable.ChangeType type) {}
@Override
protected void storeState() {}
@Override
protected void restoreState() {}
@Override
protected void acceptState() {}
public void setUsed() {}
public LogColumn[] getColumns() {
return new LogColumn[0];
}
public String getId() {
return null;
}
public void setId(String id) {}
};
List<AbstractModelLikelihood> likelihoodList = new ArrayList<AbstractModelLikelihood>();
likelihoodList.add(like1);
likelihoodList.add(like2);
Parameter weights = new Parameter.Default(2);
double p1 = 0.05;
weights.setParameterValue(0, p1);
weights.setParameterValue(1, 1.0 - p1);
WeightedMixtureModel mixture = new WeightedMixtureModel(likelihoodList, weights);
System.err.println("getLogLikelihood() = " + mixture.getLogLikelihood());
double test = Math.log(p1 * Math.exp(l1) + (1.0 - p1) * Math.exp(l2));
System.err.println("correct = " + test);
}
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();
}