/*
* RegressionMetropolizedIndicatorOperator.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.distribution.LinearRegression;
import dr.inference.distribution.MultivariateDistributionLikelihood;
import dr.inference.model.Parameter;
import dr.inferencexml.distribution.GeneralizedLinearModelParser;
import dr.inferencexml.model.MaskedParameterParser;
import dr.math.MathUtils;
import dr.math.distributions.MultivariateNormalDistribution;
import dr.xml.*;
/**
* @author Marc Suchard
*/
public class RegressionMetropolizedIndicatorOperator extends SimpleMCMCOperator {
public static final String MH_OPERATOR = "regressionMetropolizedIndicatorOperator";
public RegressionMetropolizedIndicatorOperator(LinearRegression linearModel, Parameter effect,
Parameter indicators, MultivariateDistributionLikelihood effectPrior,
Parameter mask) {
effectOperator = new RegressionGibbsEffectOperator(linearModel, effect, indicators, effectPrior);
this.effect = effect;
this.indicators = indicators;
this.mask = mask;
}
/**
* @return a short descriptive message of the performance of this operator.
*/
public String getPerformanceSuggestion() {
return null;
}
public String getOperatorName() {
return MH_OPERATOR;
}
public double doOperation() {
double logHastingsRatio = 0.0;
if (mask != null) {
int sum = 0;
for(int i=0; i<mask.getDimension(); i++)
sum += mask.getParameterValue(i);
if (sum == 0)
throw new RuntimeException("Mask parameter has all zeros");
}
if (mean == null) {
final int dim = effect.getDimension();
mean = new double[dim];
variance = new double[dim][dim];
precision = new double[dim][dim];
}
// Compute back transition probability
effectOperator.computeForwardDensity(mean, variance,precision);
logHastingsRatio += MultivariateNormalDistribution.logPdf(effect.getParameterValues(),
mean, precision,
Math.log(MultivariateNormalDistribution.calculatePrecisionMatrixDeterminate(precision)),
1.0);
// Do update
int index;
do {
index = MathUtils.nextInt(indicators.getDimension());
} while( (mask != null) && (mask.getParameterValue(index) == 0));
indicators.setParameterValue(index, 1 - indicators.getParameterValue(index));
effectOperator.doOperation(); // Gibbs sample new coefficients given updated indicator
// Get forward transition probability
mean = effectOperator.getLastMean();
variance = effectOperator.getLastVariance();
precision = effectOperator.getLastPrecision();
logHastingsRatio -= MultivariateNormalDistribution.logPdf(effect.getParameterValues(),
mean, precision,
Math.log(MultivariateNormalDistribution.calculatePrecisionMatrixDeterminate(precision)),
1.0);
return logHastingsRatio;
}
public static XMLObjectParser PARSER = new AbstractXMLObjectParser() {
public String getParserName() {
return MH_OPERATOR;
}
public Object parseXMLObject(XMLObject xo) throws XMLParseException {
double weight = xo.getDoubleAttribute(WEIGHT);
LinearRegression linearModel = (LinearRegression) xo.getChild(LinearRegression.class);
Parameter effect = (Parameter) xo.getChild(Parameter.class);
MultivariateDistributionLikelihood prior = (MultivariateDistributionLikelihood)
xo.getChild(MultivariateDistributionLikelihood.class);
if (prior.getDistribution().getType().compareTo(MultivariateNormalDistribution.TYPE) != 0)
throw new XMLParseException("Only a multivariate normal prior is conjugate");
XMLObject cxo = (XMLObject) xo.getChild(GeneralizedLinearModelParser.INDICATOR);
Parameter indicators = (Parameter) cxo.getChild(Parameter.class);
cxo = (XMLObject) xo.getChild(MaskedParameterParser.MASKING);
Parameter mask = null;
if (cxo != null) {
mask = (Parameter) cxo.getChild(Parameter.class);
if (mask.getDimension() != indicators.getDimension())
throw new XMLParseException("Indicator and mask parameter must have the same dimension");
}
RegressionMetropolizedIndicatorOperator operator = new RegressionMetropolizedIndicatorOperator(linearModel, effect,
indicators, prior, mask);
operator.setWeight(weight);
return operator;
}
//************************************************************************
// AbstractXMLObjectParser implementation
//************************************************************************
public String getParserDescription() {
return "This element returns a multivariate Gibbs operator on an internal node trait.";
}
public Class getReturnType() {
return MCMCOperator.class;
}
public XMLSyntaxRule[] getSyntaxRules() {
return rules;
}
private XMLSyntaxRule[] rules = new XMLSyntaxRule[]{
AttributeRule.newDoubleRule(WEIGHT),
new ElementRule(Parameter.class),
new ElementRule(MultivariateDistributionLikelihood.class),
new ElementRule(LinearRegression.class),
new ElementRule(GeneralizedLinearModelParser.INDICATOR,
new XMLSyntaxRule[]{
new ElementRule(Parameter.class)
}),
new ElementRule(MaskedParameterParser.MASKING,
new XMLSyntaxRule[]{
new ElementRule(Parameter.class)
}, true)
};
};
private Parameter mask;
private Parameter indicators;
private Parameter effect;
private RegressionGibbsEffectOperator effectOperator;
private double[] mean = null;
private double[][] variance = null;
private double[][] precision = null;
}