/*
* BayesianSkylineGibbsOperatorParser.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.evomodelxml.coalescent.operators;
import dr.evomodel.coalescent.BayesianSkylineLikelihood;
import dr.evomodel.coalescent.operators.BayesianSkylineGibbsOperator;
import dr.inference.model.Parameter;
import dr.inference.operators.MCMCOperator;
import dr.xml.*;
/**
*/
public class BayesianSkylineGibbsOperatorParser extends AbstractXMLObjectParser {
public static final String BAYESIAN_SKYLINE_GIBBS_OPERATOR = "generalizedSkylineGibbsOperator";
public static final String POPULATION_SIZES = "populationSizes";
public static final String GROUP_SIZES = "groupSizes";
public static final String LOWER = "lower";
public static final String UPPER = "upper";
public static final String JEFFREYS = "Jeffreys";
public static final String EXPONENTIALMARKOV = "exponentialMarkov";
public static final String SHAPE = "shape";
public static final String REVERSE = "reverse";
public static final String ITERATIONS = "iterations";
public static final String TYPE = "type";
public static final String STEPWISE = "stepwise";
public static final String LINEAR = "linear";
public static final String EXPONENTIAL = "exponential";
public String getParserName() {
return BAYESIAN_SKYLINE_GIBBS_OPERATOR;
}
public Object parseXMLObject(XMLObject xo) throws XMLParseException {
double weight = xo.getDoubleAttribute(MCMCOperator.WEIGHT);
final double lowerBound = xo.getAttribute(LOWER, 0.0);
final double upperBound = xo.getAttribute(UPPER, Double.MAX_VALUE);
final boolean jeffreysPrior = xo.getAttribute(JEFFREYS, true);
boolean exponentialMarkovPrior = xo.getAttribute(EXPONENTIALMARKOV, false);
double shape = xo.getAttribute(SHAPE, 1.0);
boolean reverse = xo.getAttribute(REVERSE, false);
int iterations = xo.getAttribute(ITERATIONS, 1);
BayesianSkylineLikelihood bayesianSkylineLikelihood = (BayesianSkylineLikelihood) xo
.getChild(BayesianSkylineLikelihood.class);
// This is the parameter on which this operator acts
Parameter paramPops = (Parameter) xo.getChild(Parameter.class);
Parameter paramGroups = bayesianSkylineLikelihood.getGroupSizeParameter();
final int type = bayesianSkylineLikelihood.getType();
if (type != BayesianSkylineLikelihood.STEPWISE_TYPE) {
throw new XMLParseException(
"Need stepwise control points (set 'linear=\"false\"' in skyline Gibbs operator)");
}
return new BayesianSkylineGibbsOperator(bayesianSkylineLikelihood,
paramPops, paramGroups, type, weight, lowerBound,
upperBound, jeffreysPrior, exponentialMarkovPrior,
shape, reverse, iterations);
}
// ************************************************************************
// AbstractXMLObjectParser implementation
// ************************************************************************
public String getParserDescription() {
return "This element returns a Gibbs operator for the joint distribution of population sizes.";
}
public Class getReturnType() {
return BayesianSkylineGibbsOperator.class;
}
public XMLSyntaxRule[] getSyntaxRules() {
return rules;
}
private XMLSyntaxRule[] rules = new XMLSyntaxRule[]{
AttributeRule.newBooleanRule(LINEAR, true),
AttributeRule.newDoubleRule(MCMCOperator.WEIGHT),
AttributeRule.newDoubleRule(LOWER),
AttributeRule.newDoubleRule(UPPER),
AttributeRule.newBooleanRule(JEFFREYS, true),
AttributeRule.newBooleanRule(REVERSE, true),
AttributeRule.newBooleanRule(EXPONENTIALMARKOV, true),
AttributeRule.newDoubleRule(SHAPE),
new ElementRule(BayesianSkylineLikelihood.class),
new ElementRule(Parameter.class)
};
}