/*
* MultivariateNormalDistributionModel.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 dr.inferencexml.distribution.MultivariateNormalDistributionModelParser;
import dr.math.distributions.GaussianProcessRandomGenerator;
import dr.math.distributions.MultivariateNormalDistribution;
/**
* A class that acts as a model for multivariate normally distributed data.
*
* @author Marc Suchard
* @author Max Tolkoff
*/
public class MultivariateNormalDistributionModel extends AbstractModel implements ParametricMultivariateDistributionModel,
GaussianProcessRandomGenerator, GradientProvider {
public MultivariateNormalDistributionModel(Parameter meanParameter, MatrixParameter precParameter) {
super(MultivariateNormalDistributionModelParser.NORMAL_DISTRIBUTION_MODEL);
this.mean = meanParameter;
addVariable(meanParameter);
if (!(meanParameter instanceof DuplicatedParameter)) {
meanParameter.addBounds(new Parameter.DefaultBounds(Double.POSITIVE_INFINITY, Double.NEGATIVE_INFINITY,
meanParameter.getDimension()));
}
this.precision = precParameter;
addVariable(precParameter);
Parameter single = null;
if (precParameter instanceof DiagonalMatrix) {
DiagonalMatrix dm = (DiagonalMatrix) precParameter;
if (dm.getDiagonalParameter() instanceof DuplicatedParameter) {
single = dm.getDiagonalParameter();
}
}
hasSinglePrecision = (single != null);
singlePrecision = single;
distribution = createNewDistribution();
distributionKnown = true;
}
public MatrixParameter getPrecisionMatrixParameter() {
return precision;
}
public Parameter getMeanParameter() {
return mean;
}
// *****************************************************************
// Interface MultivariateDistribution
// *****************************************************************
private void checkDistribution() {
if (!distributionKnown) {
distribution = createNewDistribution();
distributionKnown = true;
}
}
public double logPdf(double[] x) {
checkDistribution();
return distribution.logPdf(x);
}
public double[][] getScaleMatrix() {
return precision.getParameterAsMatrix();
}
public double[] getMean() {
return mean.getParameterValues();
}
public String getType() {
return distribution.getType();
}
// *****************************************************************
// Interface Model
// *****************************************************************
public void handleModelChangedEvent(Model model, Object object, int index) {
// no intermediates need to be recalculated...
}
public Likelihood getLikelihood() {
return null;
}
protected final void handleVariableChangedEvent(Variable variable, int index, Parameter.ChangeType type) {
distributionKnown = false;
}
protected void storeState() {
storedDistribution = distribution;
storedDistributionKnown = distributionKnown;
}
protected void restoreState() {
distributionKnown = storedDistributionKnown;
distribution = storedDistribution;
}
protected void acceptState() {
} // no additional state needs accepting
@Override
public int getDimension() {
return mean.getDimension();
}
@Override
public double[][] getPrecisionMatrix() {
return precision.getParameterAsMatrix();
}
// *****************************************************************
// Interface DensityModel
// *****************************************************************
@Override
public Variable<Double> getLocationVariable() {
return mean;
}
// **************************************************************
// Private instance variables and functions
// **************************************************************
private MultivariateNormalDistribution createNewDistribution() {
if (hasSinglePrecision) {
return new MultivariateNormalDistribution(getMean(), singlePrecision.getParameterValue(0));
} else {
return new MultivariateNormalDistribution(getMean(), getScaleMatrix());
}
}
private final Parameter mean;
private final MatrixParameter precision;
private final boolean hasSinglePrecision;
private final Parameter singlePrecision;
private MultivariateNormalDistribution distribution;
private MultivariateNormalDistribution storedDistribution;
private boolean distributionKnown;
private boolean storedDistributionKnown;
// RandomGenerator interface
public double[] nextRandom() {
checkDistribution();
return distribution.nextMultivariateNormal();
}
public double logPdf(Object x) {
checkDistribution();
return distribution.logPdf(x);
}
// GradientWrtParameterProvider interface
@Override
public double[] getGradientLogDensity(Object x) {
checkDistribution();
return distribution.getGradientLogDensity(x);
}
}