/*
* NormalDistributionModel.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.NormalDistributionModelParser;
import dr.math.MathUtils;
import dr.math.UnivariateFunction;
import dr.math.distributions.GaussianProcessRandomGenerator;
import dr.math.distributions.NormalDistribution;
import org.w3c.dom.Document;
import org.w3c.dom.Element;
/**
* A class that acts as a model for normally distributed data.
*
* @author Alexei Drummond
* @version $Id: NormalDistributionModel.java,v 1.6 2005/05/24 20:25:59 rambaut Exp $
*/
public class NormalDistributionModel extends AbstractModel implements ParametricDistributionModel,
GaussianProcessRandomGenerator, GradientProvider {
/**
* Constructor.
*/
public NormalDistributionModel(Variable<Double> mean, Variable<Double> scale) {
super(NormalDistributionModelParser.NORMAL_DISTRIBUTION_MODEL);
this.mean = mean;
this.scale = scale;
addVariable(mean);
mean.addBounds(new Parameter.DefaultBounds(Double.POSITIVE_INFINITY, Double.NEGATIVE_INFINITY, 1));
addVariable(scale);
scale.addBounds(new Parameter.DefaultBounds(Double.POSITIVE_INFINITY, 0.0, 1));
}
public NormalDistributionModel(Parameter meanParameter, Parameter scale, boolean isPrecision) {
super(NormalDistributionModelParser.NORMAL_DISTRIBUTION_MODEL);
this.hasPrecision = isPrecision;
this.mean = meanParameter;
addVariable(meanParameter);
meanParameter.addBounds(new Parameter.DefaultBounds(Double.POSITIVE_INFINITY, Double.NEGATIVE_INFINITY, 1));
if (isPrecision) {
this.precision = scale;
this.scale = null; // todo why not keep the name scale to avoid confusion?? For whom?
} else {
this.scale = scale;
}
addVariable(scale);
scale.addBounds(new Parameter.DefaultBounds(Double.POSITIVE_INFINITY, 0.0, 1));
}
public double getStdev() {
return getScale();
}
public double getScale() {
if (hasPrecision)
return 1.0 / Math.sqrt(precision.getValue(0));
return scale.getValue(0);
}
public Variable<Double> getMean() {
return mean;
}
public Variable<Double> getPrecision() {
if (hasPrecision)
return precision;
return null;
}
// *****************************************************************
// Interface Distribution
// *****************************************************************
public double pdf(double x) {
return NormalDistribution.pdf(x, mean(), getScale());
}
public double logPdf(double x) {
return NormalDistribution.logPdf(x, mean(), getScale());
}
public double cdf(double x) {
return NormalDistribution.cdf(x, mean(), getScale());
}
public double quantile(double y) {
return NormalDistribution.quantile(y, mean(), getScale());
}
public double mean() {
return mean.getValue(0);
}
public double variance() {
if (hasPrecision)
return 1.0 / precision.getValue(0);
double sd = scale.getValue(0);
return sd * sd;
}
public final UnivariateFunction getProbabilityDensityFunction() {
return pdfFunction;
}
private final UnivariateFunction pdfFunction = new UnivariateFunction() {
public final double evaluate(double x) {
return pdf(x);
}
public final double getLowerBound() {
return Double.NEGATIVE_INFINITY;
}
public final double getUpperBound() {
return Double.POSITIVE_INFINITY;
}
};
// *****************************************************************
// Interface Model
// *****************************************************************
public void handleModelChangedEvent(Model model, Object object, int index) {
// no intermediates need to be recalculated...
}
protected final void handleVariableChangedEvent(Variable variable, int index, Parameter.ChangeType type) {
// no intermediates need to be recalculated...
}
protected void storeState() {
} // no additional state needs storing
protected void restoreState() {
} // no additional state needs restoring
protected void acceptState() {
} // no additional state needs accepting
public Element createElement(Document document) {
throw new RuntimeException("Not implemented!");
}
// **************************************************************
// Private instance variables
// **************************************************************
private final Variable<Double> mean;
private final Variable<Double> scale;
private Variable<Double> precision;
private boolean hasPrecision = false;
public Object nextRandom() {
double eps = MathUtils.nextGaussian();
eps *= getScale();
eps += mean();
return eps;
}
public double logPdf(Object x) {
double v = (Double) x;
return logPdf(v);
}
@Override
public Likelihood getLikelihood() {
return null;
}
@Override
public int getDimension() { return 1; }
@Override
public double[] getGradientLogDensity(Object x) {
double[] result = new double[1];
result[0] = NormalDistribution.gradLogPdf((Double) x, mean(), getScale());
return result;
}
@Override
public double[][] getPrecisionMatrix() {
double p = hasPrecision ?
precision.getValue(0) :
scale.getValue(0) * scale.getValue(0);
return new double[][]{{p}};
}
// *****************************************************************
// Interface DensityModel
// *****************************************************************
@Override
public double logPdf(double[] x) {
return logPdf(x[0]);
}
@Override
public Variable<Double> getLocationVariable() {
return mean;
}
}