/* * LinearBiasModelParser.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.oldevomodelxml.substmodel; import dr.evolution.datatype.Microsatellite; import dr.oldevomodel.substmodel.FrequencyModel; import dr.oldevomodel.substmodel.LinearBiasModel; import dr.oldevomodel.substmodel.OnePhaseModel; import dr.inference.model.Parameter; import dr.xml.*; /** * @author Chieh-Hsi Wu * * Parser for LinearBiasModel of Microsatellite. */ public class LinearBiasModelParser extends AbstractXMLObjectParser { public static final String SUBMODEL = "Submodel"; public static final String BIAS_CONSTANT = "BiasConstant"; public static final String BIAS_LINEAR = "BiasLinear"; public static final String ESTIMATE_SUBMODEL_PARAMS = "estimateSubmodelParameters"; public static final String LOGISTICS = "logistics"; public static final String IS_SUBMODEL = "isSubmodel"; public String getParserName() { return LinearBiasModel.LINEAR_BIAS_MODEL; } //AbstractXMLObjectParser implementation public Object parseXMLObject(XMLObject xo) throws XMLParseException { OnePhaseModel subModel = (OnePhaseModel) xo.getElementFirstChild(SUBMODEL); Microsatellite dataType = (Microsatellite)subModel.getDataType(); Parameter biasConst = null; if(xo.hasChildNamed(BIAS_CONSTANT)){ biasConst =(Parameter) xo.getElementFirstChild(BIAS_CONSTANT); } Parameter biasLin = null; if(xo.hasChildNamed(BIAS_LINEAR)){ biasLin = (Parameter) xo.getElementFirstChild(BIAS_LINEAR); } //get FrequencyModel FrequencyModel freqModel = null; if(xo.hasChildNamed(FrequencyModelParser.FREQUENCIES)){ freqModel = (FrequencyModel)xo.getElementFirstChild(FrequencyModelParser.FREQUENCIES); } boolean estimateSubmodelParams = false; if(xo.hasAttribute(ESTIMATE_SUBMODEL_PARAMS)){ estimateSubmodelParams = xo.getBooleanAttribute(ESTIMATE_SUBMODEL_PARAMS); } System.out.println("Is estimating submodel parameter(s): "+estimateSubmodelParams); boolean logistics = false; if(xo.hasAttribute(LOGISTICS)){ logistics = xo.getBooleanAttribute(LOGISTICS); } System.out.println("Using logistic regression: "+ logistics); boolean isSubmodel = false; if(xo.hasAttribute(IS_SUBMODEL)){ isSubmodel = xo.getBooleanAttribute(IS_SUBMODEL); } System.out.println("Is a submodel: "+isSubmodel); return new LinearBiasModel( dataType, freqModel, subModel, biasConst, biasLin, logistics, estimateSubmodelParams, isSubmodel ); } public String getParserDescription() { return "This element represents an instance of the stepwise mutation model of microsatellite evolution."; } public XMLSyntaxRule[] getSyntaxRules() { return rules; } private XMLSyntaxRule[] rules = new XMLSyntaxRule[]{ new ElementRule(FrequencyModelParser.FREQUENCIES, new XMLSyntaxRule[]{ new ElementRule(FrequencyModel.class)},true), new ElementRule(SUBMODEL,new XMLSyntaxRule[]{new ElementRule(OnePhaseModel.class)}), new ElementRule(Microsatellite.class), new ElementRule(BIAS_CONSTANT,new XMLSyntaxRule[]{new ElementRule(Parameter.class)},true), new ElementRule(BIAS_LINEAR,new XMLSyntaxRule[]{new ElementRule(Parameter.class)},true), new StringAttributeRule(ESTIMATE_SUBMODEL_PARAMS,"whether or not to esitmate the parameters of the submodel",true), AttributeRule.newBooleanRule(LOGISTICS,true), AttributeRule.newBooleanRule(IS_SUBMODEL,true) }; public Class getReturnType() { return LinearBiasModel.class; } }