/*
* ARGRatePrior.java
*
* Copyright (c) 2002-2015 Alexei Drummond, Andrew Rambaut and Marc Suchard
*
* This file is part of BEAST.
* See the NOTICE file distributed with this work for additional
* information regarding copyright ownership and licensing.
*
* BEAST is free software; you can redistribute it and/or modify
* it under the terms of the GNU Lesser General Public License as
* published by the Free Software Foundation; either version 2
* of the License, or (at your option) any later version.
*
* BEAST is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public
* License along with BEAST; if not, write to the
* Free Software Foundation, Inc., 51 Franklin St, Fifth Floor,
* Boston, MA 02110-1301 USA
*/
package dr.evomodel.arg;
import dr.evomodel.arg.ARGModel.Node;
import dr.inference.model.AbstractModelLikelihood;
import dr.inference.model.Model;
import dr.inference.model.Parameter;
import dr.inference.model.Variable;
import dr.inference.model.Variable.ChangeType;
import dr.math.MathUtils;
import dr.math.distributions.GammaDistribution;
import dr.xml.*;
public class ARGRatePrior extends AbstractModelLikelihood {
public static final String ARG_RATE_PRIOR = "argRatePrior";
public static final String SIGMA = "sigma";
private final ARGModel arg;
private final Parameter logNormalSigma;
public ARGRatePrior(String name, ARGModel arg, Parameter sigma) {
super(name);
this.arg = arg;
this.logNormalSigma = sigma;
addModel(arg);
addVariable(sigma);
}
public double[] generateValues() {
double[] values = new double[arg.getNumberOfPartitions()];
double sigma = logNormalSigma.getParameterValue(0);
double oneOverSigma = 1.0 / sigma;
for (int i = 0; i < values.length; i++) {
values[i] = MathUtils.nextGamma(oneOverSigma, oneOverSigma);
}
return values;
}
public double getLogLikelihood() {
return calculateLogLikelihood();
}
public double getAddHastingsRatio(double[] values) {
return -calculateLogLikelihood(values);
}
private double calculateLogLikelihood(double[] values) {
double logLike = 0;
double sigma = logNormalSigma.getParameterValue(0);
double oneOverSigma = 1.0 / sigma;
for (double d : values) {
logLike += GammaDistribution.logPdf(d, oneOverSigma, sigma);
}
return logLike;
}
private double calculateLogLikelihood() {
double logLike = 0;
for (int i = 0, n = arg.getNodeCount(); i < n; i++) {
Node x = (Node) arg.getNode(i);
if (!x.isRoot() && x.isBifurcation()) {
double[] values = x.rateParameter.getParameterValues();
logLike += calculateLogLikelihood(values);
}
}
return logLike;
}
public Model getModel() {
return this;
}
public void makeDirty() {
}
public String getId() {
return super.getId();
}
public void setId(String id) {
super.setId(id);
}
protected void acceptState() {
}
protected void handleModelChangedEvent(Model model, Object object, int index) {
}
protected void handleVariableChangedEvent(Variable variable, int index,
ChangeType type) {
}
protected void restoreState() {
}
protected void storeState() {
}
public static XMLObjectParser PARSER = new AbstractXMLObjectParser() {
public String getParserDescription() {
return null;
}
public Class getReturnType() {
return ARGRatePrior.class;
}
public XMLSyntaxRule[] getSyntaxRules() {
return null;
}
public Object parseXMLObject(XMLObject xo) throws XMLParseException {
String id = xo.getAttribute(XMLParser.ID, "");
Parameter sigma = (Parameter) xo.getChild(Parameter.class);
ARGModel arg = (ARGModel) xo.getChild(ARGModel.class);
return new ARGRatePrior(id, arg, sigma);
}
public String getParserName() {
return ARG_RATE_PRIOR;
}
};
}