/*
* GaussianProcessDrawOperator.java
*
* Copyright (c) 2002-2016 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.inferencexml.operators.EllipticalSliceOperatorParser;
import dr.math.distributions.GaussianProcessRandomGenerator;
import dr.xml.*;
/**
* @author Marc Suchard
*/
public class GaussianProcessDrawOperator extends AbstractCoercableOperator {
public static final String GAUSSIAN_PROCESS_OPERATOR = "gaussianProcessOperator";
public static final String SCALE_FACTOR = "scaleFactor";
public static final String TRANSLATION_INVARIANT = EllipticalSliceOperatorParser.TRANSLATION_INVARIANT;
public static final String ROTATION_INVARIANT = EllipticalSliceOperatorParser.ROTATION_INVARIANT;
private double scaleFactor;
private final Parameter parameter;
private final GaussianProcessRandomGenerator gaussianProcess;
private final int dim;
private final boolean translationInvariant;
private final boolean rotationInvariant;
public GaussianProcessDrawOperator(Parameter parameter, double scaleFactor, double weight,
CoercionMode mode, GaussianProcessRandomGenerator gaussianProcess,
boolean translationInvariant, boolean rotationInvariant) {
super(mode);
this.scaleFactor = scaleFactor;
this.parameter = parameter;
this.gaussianProcess = gaussianProcess;
setWeight(weight);
this.translationInvariant = translationInvariant;
this.rotationInvariant = rotationInvariant;
if (gaussianProcess.getDimension() != parameter.getDimension()) {
throw new IllegalArgumentException("Dimension mismatch");
}
// if (!gaussianProcess.isTranslationInvariant()) {
// throw new IllegalArgumentException("Must be translationally invariant");
// }
this.dim = parameter.getDimension();
}
public double doOperation() {
double[] x = parameter.getParameterValues();
double[] epsilon = (double[]) gaussianProcess.nextRandom();
for (int i = 0; i < dim; ++i) {
x[i] += scaleFactor * epsilon[i];
}
EllipticalSliceOperator.transformPoint(x, translationInvariant, rotationInvariant,
2); // TODO Make generic for dim != 2
for (int i = 0; i < dim; i++) {
parameter.setParameterValueQuietly(i, x[i] + scaleFactor * epsilon[i]);
}
parameter.fireParameterChangedEvent();
// System.err.println("DONE");
return 0; // TODO Not 0 if gaussianProcess mean != 0
}
//MCMCOperator INTERFACE
public final String getOperatorName() {
return parameter.getParameterName();
}
public double getCoercableParameter() {
return Math.log(scaleFactor);
}
public void setCoercableParameter(double value) {
scaleFactor = Math.exp(value);
}
public double getRawParameter() {
return scaleFactor;
}
public double getScaleFactor() {
return scaleFactor;
}
public double getTargetAcceptanceProbability() {
return 0.234;
}
public double getMinimumAcceptanceLevel() {
return 0.1;
}
public double getMaximumAcceptanceLevel() {
return 0.4;
}
public double getMinimumGoodAcceptanceLevel() {
return 0.20;
}
public double getMaximumGoodAcceptanceLevel() {
return 0.30;
}
public final String getPerformanceSuggestion() {
double prob = Utils.getAcceptanceProbability(this);
double targetProb = getTargetAcceptanceProbability();
dr.util.NumberFormatter formatter = new dr.util.NumberFormatter(5);
double sf = OperatorUtils.optimizeWindowSize(scaleFactor, prob, targetProb);
if (prob < getMinimumGoodAcceptanceLevel()) {
return "Try setting scaleFactor to about " + formatter.format(sf);
} else if (prob > getMaximumGoodAcceptanceLevel()) {
return "Try setting scaleFactor to about " + formatter.format(sf);
} else return "";
}
public static XMLObjectParser PARSER = new AbstractXMLObjectParser() {
public String getParserName() {
return GAUSSIAN_PROCESS_OPERATOR;
}
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);
GaussianProcessRandomGenerator generator =
(GaussianProcessRandomGenerator) xo.getChild(GaussianProcessRandomGenerator.class);
boolean translationInvariant = xo.getAttribute(TRANSLATION_INVARIANT, false);
boolean rotationInvariant = xo.getAttribute(ROTATION_INVARIANT, false);
return new GaussianProcessDrawOperator(parameter, scaleFactor, weight, mode, generator,
translationInvariant, rotationInvariant);
}
//************************************************************************
// AbstractXMLObjectParser implementation
//************************************************************************
public String getParserDescription() {
return "This element returns a multivariate normal random walk operator on a given parameter.";
}
public Class getReturnType() {
return MCMCOperator.class;
}
public XMLSyntaxRule[] getSyntaxRules() {
return rules;
}
private final XMLSyntaxRule[] rules = {
AttributeRule.newDoubleRule(SCALE_FACTOR),
AttributeRule.newDoubleRule(WEIGHT),
AttributeRule.newBooleanRule(AUTO_OPTIMIZE, true),
AttributeRule.newBooleanRule(TRANSLATION_INVARIANT, true),
AttributeRule.newBooleanRule(ROTATION_INVARIANT, true),
new ElementRule(Parameter.class),
new ElementRule(GaussianProcessRandomGenerator.class),
};
};
}