/* * DiffusionModel.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.continuous; import dr.inference.model.AbstractModel; import dr.inference.model.Model; import dr.inference.model.Parameter; import dr.inference.model.Variable; import dr.math.MathUtils; import dr.xml.*; import org.w3c.dom.Document; import org.w3c.dom.Element; /** * A class that can calculate the likelihood of a diffusion rate given a distance and time. * * @author Alexei Drummond * @version $Id: DiffusionModel.java,v 1.5 2005/01/06 14:46:36 rambaut Exp $ */ public class DiffusionModel extends AbstractModel { public static final String DIFFUSION_PROCESS = "diffusionProcess"; public static final String DIFFUSION_CONSTANT = "D"; public static final String BIAS = "mu"; /** * Construct a diffusion model. */ public DiffusionModel(Parameter diffusionRateParameter) { super(DIFFUSION_PROCESS); this.diffusionRateParameter = diffusionRateParameter; addVariable(diffusionRateParameter); diffusionRateParameter.addBounds(new Parameter.DefaultBounds(Double.POSITIVE_INFINITY, 0.0, 1)); } /** * Construct a diffusion model. */ public DiffusionModel(Parameter diffusionRateParameter, Parameter biasParameter) { super(DIFFUSION_PROCESS); this.diffusionRateParameter = diffusionRateParameter; this.biasParameter = biasParameter; addVariable(diffusionRateParameter); addVariable(biasParameter); diffusionRateParameter.addBounds(new Parameter.DefaultBounds(Double.POSITIVE_INFINITY, 0.0, 1)); } public double getD() { return diffusionRateParameter.getParameterValue(0); } /** * @return the log likelihood of going from start to stop in the given time */ public double getLogLikelihood(double start, double stop, double time) { double D = diffusionRateParameter.getParameterValue(0); double bias = getBias(); // expected variance of distances of given time double Dtime = D * time; double unbiasedDistance = (stop - start) - (bias * time); //System.out.println("distance=" + unbiasedDistance + " time=" + time); // the log likelihood of travelling distance d, in time t given diffusion rate D return -0.5 * Math.log(Dtime) - ((unbiasedDistance * unbiasedDistance) / (Dtime)); } /** * simulate the diffusion process forward in time. * * @return the new value of the trait after given time. */ public double simulateForward(double value, double time) { double D = diffusionRateParameter.getParameterValue(0); double delta = MathUtils.nextGaussian(); delta *= Math.sqrt(D * time); delta += getBias() * time; return value + delta; } /** * @return the bias of this diffusion process. */ private double getBias() { if (biasParameter == null) return 0.0; return biasParameter.getParameterValue(0); } // ***************************************************************** // Interface Model // ***************************************************************** public void handleModelChangedEvent(Model model, Object object, int index) { // no intermediates need to be recalculated... } public void handleVariableChangedEvent(Variable variable, int index, Parameter.ChangeType type) { // no intermediates need to be recalculated... } protected void storeState() { } // no additional state needs storing protected void restoreState() { } // no additional state needs restoring protected void acceptState() { } // no additional state needs accepting // ************************************************************** // XMLElement IMPLEMENTATION // ************************************************************** public Element createElement(Document document) { throw new RuntimeException("Not implemented!"); } // ************************************************************** // XMLObjectParser // ************************************************************** public static XMLObjectParser PARSER = new AbstractXMLObjectParser() { public String getParserName() { return DIFFUSION_PROCESS; } public Object parseXMLObject(XMLObject xo) throws XMLParseException { XMLObject cxo = xo.getChild(DIFFUSION_CONSTANT); Parameter diffusionParam = (Parameter) cxo.getChild(Parameter.class); Parameter biasParam = null; if (xo.hasAttribute(BIAS)) { cxo = xo.getChild(BIAS); biasParam = (Parameter) cxo.getChild(Parameter.class); } if (biasParam == null) { return new DiffusionModel(diffusionParam); } return new DiffusionModel(diffusionParam, biasParam); } //************************************************************************ // AbstractXMLObjectParser implementation //************************************************************************ public String getParserDescription() { return "Describes a diffusion process."; } public XMLSyntaxRule[] getSyntaxRules() { return rules; } private final XMLSyntaxRule[] rules = { new ElementRule(DIFFUSION_CONSTANT, new XMLSyntaxRule[]{new ElementRule(Parameter.class)}), new ElementRule(BIAS, new XMLSyntaxRule[]{new ElementRule(Parameter.class)}) }; public Class getReturnType() { return DiffusionModel.class; } }; // ************************************************************** // Private instance variables // ************************************************************** private final Parameter diffusionRateParameter; private Parameter biasParameter; }