/*
* CCPImportanceDistributionOperator.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.evomodel.operators;
import dr.evomodel.tree.ConditionalCladeFrequency;
import dr.evomodel.tree.TreeModel;
import dr.xml.*;
/**
* @author Sebastian Hoehna
*/
// Cleaning out untouched stuff. Can be resurrected if needed
@Deprecated
public class CCPImportanceDistributionOperator extends
AbstractImportanceDistributionOperator {
public static final String CCP_IMPORTANCE_DISTRIBUTION_OPERATOR = "CCPImportanceDistributionOperator";
/**
*
*/
public CCPImportanceDistributionOperator(TreeModel tree, double weight,
int samples, int sampleEvery, double epsilon) {
super(tree, weight, samples, sampleEvery);
probabilityEstimater = new ConditionalCladeFrequency(tree, epsilon);
}
/**
*
*/
public CCPImportanceDistributionOperator(TreeModel tree, double weight) {
super(tree, weight);
double epsilon = 1 - Math.pow(0.5, 1.0 / 10000);
probabilityEstimater = new ConditionalCladeFrequency(tree, epsilon);
}
/*
* (non-Javadoc)
*
* @see dr.inference.operators.AbstractImportanceSampler#getOperatorName()
*/
@Override
public String getOperatorName() {
return CCP_IMPORTANCE_DISTRIBUTION_OPERATOR;
}
/*
* (non-Javadoc)
*
* @see
* dr.inference.operators.AbstractImportanceSampler#getPerformanceSuggestion
* ()
*/
@Override
public String getPerformanceSuggestion() {
if (getAcceptanceProbability() < getMinimumGoodAcceptanceLevel()) {
return "Try to increase the sample size and/or the steps between each sample.";
}
return "";
}
// Sebastian
// public void printClades(){
// probabilityEstimater.printClades();
// }
// Sebastian
public static XMLObjectParser CCP_IMPORTANCE_DISTRIBUTION_OPERATOR_PARSER = new AbstractXMLObjectParser() {
public String getParserName() {
return CCP_IMPORTANCE_DISTRIBUTION_OPERATOR;
}
public Object parseXMLObject(XMLObject xo) throws XMLParseException {
TreeModel treeModel = (TreeModel) xo.getChild(TreeModel.class);
double weight = xo.getDoubleAttribute("weight");
int samples = xo.getIntegerAttribute("samples");
double epsilon = 1 - Math.pow(0.5, 1.0 / samples);
if (xo.hasAttribute("epsilon")) {
epsilon = xo.getDoubleAttribute("epsilon");
}
int sampleEvery = 10;
if (xo.hasAttribute("sampleEvery")) {
sampleEvery = xo.getIntegerAttribute("sampleEvery");
}
return new CCPImportanceDistributionOperator(treeModel, weight,
samples, sampleEvery, epsilon);
}
//**********************************************************************
// **
// AbstractXMLObjectParser implementation
//**********************************************************************
// **
public String getParserDescription() {
return "This element represents an operator proposing trees from an importance distribution which is created by the conditional clade probabilities.";
}
public Class getReturnType() {
return CCPImportanceDistributionOperator.class;
}
public XMLSyntaxRule[] getSyntaxRules() {
return rules;
}
private XMLSyntaxRule[] rules = new XMLSyntaxRule[]{
AttributeRule.newDoubleRule("weight"),
AttributeRule.newIntegerRule("samples"),
AttributeRule.newIntegerRule("sampleEvery", true),
AttributeRule.newDoubleRule("epsilon", true),
new ElementRule(TreeModel.class)};
};
}