/*
* MultivariateNormalIndependenceSampler.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.operators;
import dr.inference.model.Parameter;
import dr.inference.regression.SelfControlledCaseSeries;
import dr.math.MathUtils;
import dr.math.Poisson;
import dr.math.distributions.NormalDistribution;
import dr.xml.*;
import java.util.HashSet;
import java.util.Set;
/**
* An independent normal distribution sampler to propose new (independent) values from a provided normal distribution model.
*
* @author Marc Suchard
* @author Guy Baele
*/
public class MultivariateNormalIndependenceSampler extends AbstractCoercableOperator {
public static final String OPERATOR_NAME = "multivariateNormalIndependenceSampler";
public static final String SCALE_FACTOR = "scaleFactor";
public static final String SET_SIZE_MEAN = "setSizeMean";
private double scaleFactor;
private final Parameter parameter;
private final int dim;
private double setSizeMean;
private final SelfControlledCaseSeries sccs;
public MultivariateNormalIndependenceSampler(Parameter parameter,
SelfControlledCaseSeries sccs,
double setSizeMean,
double weight, double scaleFactor, CoercionMode mode) {
super(mode);
this.scaleFactor = scaleFactor;
this.parameter = parameter;
setWeight(weight);
dim = parameter.getDimension();
setWeight(weight);
this.sccs = sccs;
this.setSizeMean = setSizeMean;
}
public String getPerformanceSuggestion() {
return "";
}
public String getOperatorName() {
return "independentNormalDistribution(" + parameter.getVariableName() + ")";
}
/**
* change the parameter and return the hastings ratio.
*/
public double doOperation() {
double[] mean = sccs.getMode();
double[] currentValue = parameter.getParameterValues();
double[] newValue = new double[dim];
Set<Integer> updateSet = new HashSet<Integer>();
if (setSizeMean != -1.0) {
final int listLength = Poisson.nextPoisson(setSizeMean);
while (updateSet.size() < listLength) {
int newInt = MathUtils.nextInt(parameter.getDimension());
if (!updateSet.contains(newInt)) {
updateSet.add(newInt);
}
}
} else {
for (int i = 0; i < dim; ++i) {
updateSet.add(i);
}
}
double logq = 0;
for (Integer i : updateSet) {
newValue[i] = mean[i] + scaleFactor * MathUtils.nextGaussian();
if (UPDATE_ALL) {
parameter.setParameterValueQuietly(i, newValue[i]);
} else {
parameter.setParameterValue(i, newValue[i]);
}
logq += (NormalDistribution.logPdf(currentValue[i], mean[i], scaleFactor) -
NormalDistribution.logPdf(newValue[i], mean[i], scaleFactor));
}
// for (Integer i : updateSet) {
// parameter.setParameterValueQuietly(i, newValue[i]);
// }
if (UPDATE_ALL) {
parameter.setParameterValueNotifyChangedAll(0, parameter.getParameterValue(0));
}
return logq;
}
private static final boolean UPDATE_ALL = false;
public static XMLObjectParser PARSER = new AbstractXMLObjectParser() {
public String getParserName() {
return OPERATOR_NAME;
}
public Object parseXMLObject(XMLObject xo) throws XMLParseException {
CoercionMode mode = CoercionMode.parseMode(xo);
double weight = xo.getDoubleAttribute(WEIGHT);
double scaleFactor = xo.getDoubleAttribute(SCALE_FACTOR);
if (scaleFactor <= 0.0) {
throw new XMLParseException("scaleFactor must be greater than 0.0");
}
Parameter parameter = (Parameter) xo.getChild(Parameter.class);
SelfControlledCaseSeries sccs = (SelfControlledCaseSeries) xo.getChild(SelfControlledCaseSeries.class);
double setSizeMean = xo.getAttribute(SET_SIZE_MEAN, -1.0);
return new MultivariateNormalIndependenceSampler(parameter, sccs, setSizeMean, weight, scaleFactor, mode);
}
//************************************************************************
// AbstractXMLObjectParser implementation
//************************************************************************
public XMLSyntaxRule[] getSyntaxRules() {
return rules;
}
private final XMLSyntaxRule[] rules = {
AttributeRule.newDoubleRule(SCALE_FACTOR),
AttributeRule.newDoubleRule(WEIGHT),
AttributeRule.newBooleanRule(AUTO_OPTIMIZE, true),
AttributeRule.newDoubleRule(SET_SIZE_MEAN, true),
new ElementRule(SelfControlledCaseSeries.class),
new ElementRule(Parameter.class),
};
public String getParserDescription() {
return "This element returns an independence sampler from a provided normal distribution model.";
}
public Class getReturnType() {
return MultivariateNormalIndependenceSampler.class;
}
};
public double getCoercableParameter() {
return Math.log(scaleFactor);
}
public void setCoercableParameter(double value) {
scaleFactor = Math.exp(value);
}
public double getRawParameter() {
return scaleFactor;
}
}