/* * 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; } }; }