/*
* RandomWalkModel.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.inference.distribution;
import dr.inference.model.*;
import java.util.logging.Logger;
/**
* @author Marc A. Suchard
*/
public class RandomWalkModel extends AbstractModelLikelihood {
public RandomWalkModel(
ParametricDistributionModel distribution,
Parameter data, boolean forwardOrder, boolean logScale) {
super(null);
this.distribution = distribution;
this.forwardOrder = forwardOrder;
this.logScale = logScale;
this.data = data;
if (distribution != null) {
addModel(distribution);
}
double lower = Double.NEGATIVE_INFINITY;
if (logScale)
lower = 0.0;
addVariable(data);
if (data instanceof CompoundParameter) {
CompoundParameter cp = (CompoundParameter) data;
for (int i = 0; i < cp.getParameterCount(); i++) {
Parameter p = cp.getParameter(i);
p.addBounds(new Parameter.DefaultBounds(Double.POSITIVE_INFINITY, lower, p.getDimension()));
}
} else
data.addBounds(new Parameter.DefaultBounds(Double.POSITIVE_INFINITY, lower, data.getDimension()));
Logger.getLogger("dr.inference").info("Setting up a first-order random walk:");
Logger.getLogger("dr.inference").info("\tData parameter: " + data.getId());
Logger.getLogger("dr.inference").info("\tOn scale: " + (logScale ? "log" : "real"));
Logger.getLogger("dr.inference").info("\tDistribution: " + distribution.getId());
Logger.getLogger("dr.inference").info("\tIf you publish results using this model, please cite Suchard and Lemey (in preparation)\n");
}
protected double calculateLogLikelihood() {
final int dim = data.getDimension();
double logLikelihood = 0;
double previous = data.getParameterValue(0);
if (logScale)
previous = Math.log(previous);
for (int i = 1; i < dim; i++) {
double current = data.getParameterValue(i);
if (logScale)
current = Math.log(current);
logLikelihood += distribution.logPdf(current - previous);
if (logScale)
logLikelihood -= current;
previous = current;
}
return logLikelihood;
}
protected void handleModelChangedEvent(Model model, Object object, int index) {
likelihoodKnown = false;
}
protected void handleVariableChangedEvent(Variable variable, int index, Parameter.ChangeType type) {
likelihoodKnown = false;
}
protected void storeState() {
}
protected void restoreState() {
}
protected void acceptState() {
}
private final ParametricDistributionModel distribution;
private final boolean logScale;
private Parameter data;
protected boolean likelihoodKnown;
private boolean forwardOrder;
public Model getModel() {
return this;
}
public double getLogLikelihood() {
return calculateLogLikelihood();
}
public void makeDirty() {
}
}