/*
* AlloppNetworkPrior.java
*
* Copyright (c) 2002-2017 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.alloppnet.speciation;
import dr.inference.distribution.ParametricDistributionModel;
import dr.inference.model.Likelihood;
import dr.inference.model.Parameter;
import java.util.ArrayList;
/**
*
* Calculates prior likelihood for an allopolyploid network.
*
* @author Graham Jones
* Date: 01/07/2011
*/
public class AlloppNetworkPrior extends Likelihood.Abstract {
AlloppSpeciesNetworkModel asnm;
AlloppNetworkPriorModel prior;
double numHybsLogL[];
public AlloppNetworkPrior(AlloppNetworkPriorModel prior, AlloppSpeciesNetworkModel asnm) {
super(prior);
this.asnm = asnm;
this.prior = prior;
asnm.setHybPopModel(prior.getHybridPopModel());
int ndips = asnm.getNofDiploids();
numHybsLogL = new double[asnm.maxNumberOfHybPopParameters()+1];
for (int h = 0; h < numHybsLogL.length; h++) {
numHybsLogL[h] = -h * Math.log(7.0*Math.sqrt(ndips));
// grjtodo-soon the form of the function is experimental
}
}
@Override
protected boolean getLikelihoodKnown() {
return false;
}
@Override
protected double calculateLogLikelihood() {
double llhood = 0.0;
//network topology and times prior
llhood += loglikelihoodEvents();
//System.out.print(llhood); System.out.print(" ");
llhood += loglikeNumHybridizations();
//System.out.print(llhood); System.out.print(" ");
// population prior for tips
Parameter tippvals = asnm.getTipPopValues();
ParametricDistributionModel tipmodel = prior.getTipPopModel();
for (int i = 0; i < tippvals.getDimension(); i++) {
llhood += tipmodel.logPdf(tippvals.getParameterValue(i));
}
// population prior for root ends
Parameter rootpvals = asnm.getRootPopValues();
ParametricDistributionModel rootmodel = prior.getRootPopModel();
for (int i = 0; i < rootpvals.getDimension(); i++) {
llhood += rootmodel.logPdf(rootpvals.getParameterValue(i));
}
// population prior for new hybrids
ParametricDistributionModel hybmodel = prior.getHybridPopModel();
for (int i = 0; i < asnm.getNumberOfTetraTrees(); i++) {
llhood += hybmodel.logPdf(asnm.getOneHybPopValue(i));
}
//System.out.println(llhood);
return llhood;
}
private double loglikeNumHybridizations() {
int nhybs = asnm.getNumberOfTetraTrees();
//System.out.print(nhybs); System.out.print(" ");
return numHybsLogL[nhybs];
}
/*
* Going backwards in time this gives probabilities to three types
* of events: diploid-diploid joins, tet-tet joins, and hybridization events.
*/
private double loglikelihoodEvents() {
double lambda = prior.getRate().getParameterValue(0);
ArrayList<Double> heights = new ArrayList<Double>();
AlloppDiploidHistory adhist = asnm.getDiploidHistory();
adhist.collectInternalAndHybHeights(heights);
int nttrees = asnm.getNumberOfTetraTrees();
for (int tt = 0; tt < nttrees; tt++) {
AlloppLeggedTree ttree = asnm.getTetraploidTree(tt);
ttree.collectInternalHeights(heights);
}
double loglhood = 0.0;
for (double t : heights) {
loglhood += logexpPDF(t, lambda);
}
return loglhood;
}
private double logexpPDF(double x, double rate) {
return Math.log(rate) - rate*x;
}
}