/*
* 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;
}
}