/*
* HierarchicalPartitionLikelihood.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.arg;
import dr.inference.model.Model;
import dr.inference.model.Parameter;
import dr.inference.model.Variable;
import dr.inference.model.Variable.ChangeType;
import dr.math.MathUtils;
import dr.xml.*;
public class HierarchicalPartitionLikelihood extends ARGPartitionLikelihood {
public static final String HIERARCHICAL_PARTITION_LIKELIHOOD = "hierarchicalPartitionLikelihood";
private Parameter probabilities;
public HierarchicalPartitionLikelihood(String id, ARGModel arg, Parameter probs) {
super(id, arg);
this.probabilities = probs;
addVariable(probs);
addModel(arg);
}
public double[] generatePartition() {
double[] partition = new double[getNumberOfPartitionsMinusOne() + 1];
partition[0] = 0.0;
for (int i = 0; i < partition.length; i++)
partition[i] = 0.0;
while (UniformPartitionLikelihood.arraySum(partition) == 0.0) {
for (int i = 1; i < partition.length; i++) {
if (MathUtils.nextDouble() < probabilities.getParameterValue(i - 1)) {
partition[i] = 1.0;
} else {
partition[i] = 0.0;
}
}
}
return partition;
}
public double getLogLikelihood(double[] partition) {
double logLike = 0;
for (int i = 1; i < partition.length; i++) {
if (partition[i] == 1.0) {
logLike += Math.log(probabilities.getParameterValue(i - 1));
} else {
logLike += Math.log(1 - probabilities.getParameterValue(i - 1));
}
}
// return 1;
return logLike;
}
protected void acceptState() {
// nothing to do!
}
protected void handleModelChangedEvent(Model model, Object object, int index) {
// i'm lazy
}
protected void handleVariableChangedEvent(Variable variable, int index,
ChangeType type) {
// I'm lazy, so I compute after each step :)
}
protected void restoreState() {
//nothing to restore!
}
@Override
protected void storeState() {
//nothing to store
}
public static XMLObjectParser PARSER = new AbstractXMLObjectParser() {
public String getParserDescription() {
return null;
}
public Class getReturnType() {
return PoissonPartitionLikelihood.class;
}
public XMLSyntaxRule[] getSyntaxRules() {
return new XMLSyntaxRule[]{
new ElementRule(ARGModel.class, false),
};
}
public Object parseXMLObject(XMLObject xo) throws XMLParseException {
String id = "";
if (xo.hasId())
id = xo.getId();
ARGModel arg = (ARGModel) xo.getChild(ARGModel.class);
Parameter values = (Parameter) xo.getChild(Parameter.class);
if (values.getDimension() != arg.getNumberOfPartitions() - 1) {
throw new XMLParseException("The dimension of the parameter must equal the number of partitions minus 1 ");
}
if (arg.isRecombinationPartitionType()) {
throw new XMLParseException(ARGModel.TREE_MODEL + " must be of type " + ARGModel.REASSORTMENT_PARTITION);
}
return new HierarchicalPartitionLikelihood(id, arg, values);
}
public String getParserName() {
return HIERARCHICAL_PARTITION_LIKELIHOOD;
}
};
}