/*
* 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;
}