/*
* IndependentNormalDistributionSampler.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 cern.jet.random.Normal;
import cern.jet.random.engine.MersenneTwister;
import cern.jet.random.engine.RandomEngine;
import dr.inference.distribution.NormalDistributionModel;
import dr.inference.model.Bounds;
import dr.inference.model.Parameter;
import dr.inference.model.Variable;
import dr.math.MathUtils;
import dr.xml.AttributeRule;
import dr.xml.ElementRule;
import dr.xml.XMLObject;
import dr.xml.XMLParseException;
import dr.xml.XMLSyntaxRule;
/**
* An independent normal distribution sampler to propose new (independent) values from a provided normal distribution model.
*
* @author Guy Baele
*
*/
public class IndependentNormalDistributionSampler extends SimpleMCMCOperator {
public static final String OPERATOR_NAME = "independentNormalDistributionSampler";
private Variable<Double> variable = null;
private NormalDistributionModel model = null;
private boolean updateAllIndependently = true;
private static final boolean TRY_COLT = true;
private static RandomEngine randomEngine;
private static Normal coltNormal;
public IndependentNormalDistributionSampler(Variable variable, NormalDistributionModel model) {
this(variable, model, 1.0);
}
public IndependentNormalDistributionSampler(Variable variable, NormalDistributionModel model, double weight) {
this(variable, model, weight, true);
}
public IndependentNormalDistributionSampler(Variable variable, NormalDistributionModel model, double weight, boolean updateAllIndependently) {
this.variable = variable;
this.model = model;
this.updateAllIndependently = updateAllIndependently;
setWeight(weight);
if (TRY_COLT) {
randomEngine = new MersenneTwister(MathUtils.nextInt());
//create standard normal distribution, internal states will be bypassed anyway
coltNormal = new Normal(0.0, 1.0, randomEngine);
} else {
//no random draw with specified mean and stdev implemented in the normal distribution in BEAST (as far as I know)
throw new RuntimeException("Normal distribution in BEAST still needs a random sampler.");
}
}
public String getPerformanceSuggestion() {
return "";
}
public String getOperatorName() {
return "independentNormalDistribution(" + variable.getVariableName() + ")";
}
/**
* change the parameter and return the hastings ratio.
*/
public double doOperation() {
double logq = 0;
double currentValue;
double newValue;
final Bounds<Double> bounds = variable.getBounds();
final int dim = variable.getSize();
if (updateAllIndependently) {
for (int i = 0; i < dim; i++) {
//both current and new value of the variable needed for the hastings ratio
currentValue = variable.getValue(i);
//use the current mean and precision (standard deviation)
newValue = coltNormal.nextDouble(model.getMean().getValue(i), 1.0 / Math.sqrt(model.getPrecision().getValue(i)));
//System.out.println("normal distribution model: N(" + model.getMean().getValue(i) + "," + model.getPrecision().getValue(i) + ")");
//System.out.println("current value: " + currentValue + " -- new value: " + newValue);
logq += (model.logPdf(currentValue) - model.logPdf(newValue));
if (newValue < bounds.getLowerLimit(i) || newValue > bounds.getUpperLimit(i)) {
// throw new OperatorFailedException("proposed value outside boundaries");
return Double.NEGATIVE_INFINITY;
}
variable.setValue(i, newValue);
}
}
return logq;
}
public static dr.xml.XMLObjectParser PARSER = new dr.xml.AbstractXMLObjectParser() {
public String getParserName() {
return OPERATOR_NAME;
}
public Object parseXMLObject(XMLObject xo) throws XMLParseException {
double weight = xo.getDoubleAttribute(WEIGHT);
NormalDistributionModel model = (NormalDistributionModel) xo.getChild(NormalDistributionModel.class);
Parameter parameter = (Parameter) xo.getChild(Parameter.class);
return new IndependentNormalDistributionSampler(parameter, model, weight);
}
//************************************************************************
// AbstractXMLObjectParser implementation
//************************************************************************
public XMLSyntaxRule[] getSyntaxRules() {
return rules;
}
private final XMLSyntaxRule[] rules = {
AttributeRule.newDoubleRule(WEIGHT),
new ElementRule(NormalDistributionModel.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 MCMCOperator.class;
}
};
}