/* * GreatCircleDiffusionModel.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.geo.math.SphericalPolarCoordinates; import dr.inference.model.Parameter; import dr.xml.*; /** * @author Marc A. Suchard */ public class GreatCircleDiffusionModel extends MultivariateDiffusionModel { public static final String DIFFUSION_PROCESS = "greatCircleDiffusionModel"; public static final String DIFFUSION_CONSTANT = "precision"; public static final String COEFFICIENT = "diffusionCoefficient"; // public static final String BIAS = "mu"; // public static final String PRECISION_TREE_ATTRIBUTE = "precision"; public GreatCircleDiffusionModel(Parameter precision, Parameter coefficient) { super(); this.precision = precision; addVariable(precision); this.coefficient = coefficient; if (coefficient != null) addVariable(coefficient); } public GreatCircleDiffusionModel(Parameter precision) { this(precision, null); } protected double calculateLogDensity(double[] start, double[] stop, double time) { SphericalPolarCoordinates coord1 = new SphericalPolarCoordinates(start[0], start[1]); SphericalPolarCoordinates coord2 = new SphericalPolarCoordinates(stop[0], stop[1]); double distance = coord1.distance(coord2); double inverseVariance = precision.getParameterValue(0) / time; // TODO Check! // I believe this is a 2D (not 1D) Normal diffusion approx; hence the precision is squared (compared to 1D) // in the normalization constant if (coefficient == null) return -LOG2PI + Math.log(inverseVariance) - 0.5 * (distance * distance * inverseVariance); double coef = -coefficient.getParameterValue(0); return -LOG2PI + coef * Math.log(inverseVariance) - 0.5 * (distance * distance * Math.pow(inverseVariance, coef)); } protected void calculatePrecisionInfo() { } public static XMLObjectParser PARSER = new AbstractXMLObjectParser() { public String getParserName() { return DIFFUSION_PROCESS; } public Object parseXMLObject(XMLObject xo) throws XMLParseException { Parameter diffusionParam = (Parameter) xo.getChild(Parameter.class); Parameter coefficient = null; if (xo.hasChildNamed(COEFFICIENT)) coefficient = (Parameter) xo.getChild(COEFFICIENT).getChild(Parameter.class); return new GreatCircleDiffusionModel(diffusionParam, coefficient); } //************************************************************************ // AbstractXMLObjectParser implementation //************************************************************************ public String getParserDescription() { return "Describes a bivariate diffusion process using great circle distances."; } public XMLSyntaxRule[] getSyntaxRules() { return rules; } private final XMLSyntaxRule[] rules = { new ElementRule(Parameter.class), new ElementRule(COEFFICIENT, new XMLSyntaxRule[]{new ElementRule(Parameter.class)}, true), }; public Class getReturnType() { return MultivariateDiffusionModel.class; } }; private Parameter precision; private Parameter coefficient; }