/*
* AbstractPeriodPriorDistribution.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.epidemiology.casetocase.periodpriors;
import dr.inference.loggers.LogColumn;
import dr.inference.loggers.Loggable;
import dr.inference.model.AbstractModel;
import dr.inference.model.Model;
import dr.inference.model.Parameter;
import dr.inference.model.Variable;
import java.util.ArrayList;
/**
* Abstract class for the probability of a set of latent or infectious periods being drawn from an unknown probability
* distribution, given hyperpriors on the parameters of that distribution.
*/
public abstract class AbstractPeriodPriorDistribution extends AbstractModel implements Loggable {
// are we working on the logarithms of the values?
protected boolean log;
protected double logL;
protected double storedLogL;
public AbstractPeriodPriorDistribution(String name, boolean log) {
super(name);
this.log = log;
}
public double getLogLikelihood(double[] values){
if(!log){
return calculateLogLikelihood(values);
} else {
double[] logValues = new double[values.length];
for(int i=0; i<values.length; i++){
logValues[i] = Math.log(values[i]);
}
return calculateLogLikelihood(logValues);
}
}
public double getLogPosteriorProbability(double newValue, double minValue){
if(!log){
return calculateLogPosteriorProbability(newValue, minValue);
} else {
return calculateLogPosteriorProbability(Math.log(newValue), Math.log(minValue));
}
}
public double getLogPosteriorCDF(double limit, boolean upper){
if(!log){
return calculateLogPosteriorCDF(limit, upper);
} else {
return calculateLogPosteriorCDF(Math.log(limit), upper);
}
}
protected void handleModelChangedEvent(Model model, Object object, int index) {
//generally nothing to do
}
protected void handleVariableChangedEvent(Variable variable, int index, Parameter.ChangeType type) {
//generally nothing to do
}
protected void storeState() {
storedLogL = logL;
}
protected void restoreState() {
logL = storedLogL;
}
protected void acceptState() {
//generally nothing to do
}
public LogColumn[] getColumns() {
ArrayList<LogColumn> columns = new ArrayList<LogColumn>();
columns.add(new LogColumn.Abstract(getModelName()+"_LL"){
protected String getFormattedValue() {
return String.valueOf(logL);
}
});
return columns.toArray(new LogColumn[columns.size()]);
}
public abstract void reset();
public abstract double calculateLogPosteriorProbability(double newValue, double minValue);
public abstract double calculateLogPosteriorCDF(double limit, boolean upper);
public abstract double calculateLogLikelihood(double[] values);
}