/*
* ACLikelihood.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.oldevomodel.clock;
import dr.evomodel.tree.TreeModel;
import dr.inference.model.Parameter;
import dr.math.MathUtils;
import dr.math.distributions.InverseGaussianDistribution;
import dr.math.distributions.LogNormalDistribution;
import dr.math.distributions.NormalDistribution;
/**
* Calculates the likelihood of a set of rate changes in a tree, assuming a (log)normal or inverse gaussian distributed
* change in rate at each node, with a mean of the previous (log) rate and a variance proportional to branch length.
* cf Yang and Rannala 2006
*
* @author Michael Defoin Platel
* @author Wai Lok Sibon Li
*/
public class ACLikelihood extends RateEvolutionLikelihood {
public static final String LOGNORMAL = "logNormal";
public static final String NORMAL = "normal";
public static final String INVERSEGAUSSIAN = "inverseGaussian";
public ACLikelihood(TreeModel tree, Parameter ratesParameter, Parameter variance, Parameter rootRate,
boolean isEpisodic, String distribution) {
//super((isLogSpace) ? "LogNormally Distributed" : "Normally Distributed", tree, ratesParameter, rootRate, isEpisodic);
super(distribution, tree, ratesParameter, rootRate, isEpisodic);
//this.isLogSpace = isLogSpace;
this.variance = variance;
this.distribution = distribution;
addVariable(variance);
}
/**
* @return the log likelihood of the rate change from the parent to the child.
*/
double branchRateChangeLogLikelihood(double parentRate, double childRate, double time) {
double var = variance.getParameterValue(0);
if (!isEpisodic())
var *= time;
//if (isLogSpace) {
// double logParentRate = Math.log(parentRate);
// double logChildRate = Math.log(childRate);
// return NormalDistribution.logPdf(logChildRate, logParentRate - (var / 2.), Math.sqrt(var)) - logChildRate;
//} else {
// return NormalDistribution.logPdf(childRate, parentRate, Math.sqrt(var));
//}
if(distribution.equals(LOGNORMAL)) {
return LogNormalDistribution.logPdf(childRate, Math.log(parentRate) - (var / 2.), Math.sqrt(var));
}
else if(distribution.equals(NORMAL)) {
return NormalDistribution.logPdf(childRate, parentRate, Math.sqrt(var));
}
else if(distribution.equals(INVERSEGAUSSIAN)) { /* Inverse Gaussian */
double shape = (parentRate * parentRate * parentRate) / var;
return InverseGaussianDistribution.logPdf(childRate, parentRate, shape);
}
else {
throw new RuntimeException ("Parameter for distribution is not recognised");
}
}
double branchRateSample(double parentRate, double time) {
double var = variance.getParameterValue(0);
if (!isEpisodic())
var *= time;
//if (isLogSpace) {
// final double logParentRate = Math.log(parentRate);
// return Math.exp(MathUtils.nextGaussian() * Math.sqrt(var) + logParentRate - (var / 2.));
//} else {
// return MathUtils.nextGaussian() * Math.sqrt(var) + parentRate;
//}
if(distribution.equals(LOGNORMAL)) {
final double logParentRate = Math.log(parentRate);
return Math.exp(MathUtils.nextGaussian() * Math.sqrt(var) + logParentRate - (var / 2.));
}
else if(distribution.equals(NORMAL)) {
return MathUtils.nextGaussian() * Math.sqrt(var) + parentRate;
}
else { /* Inverse Gaussian */
//return Math.random()
//Random rand = new Random();
double lambda = (parentRate * parentRate * parentRate) / var;
return MathUtils.nextInverseGaussian(parentRate, lambda);
}
}
private Parameter variance;
//boolean isLogSpace = false;
String distribution;
}