/* * 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)}; }; }