package dr.inferencexml; import dr.inference.loggers.Logger; import dr.inference.ml.MLOptimizer; import dr.inference.model.Likelihood; import dr.inference.operators.OperatorSchedule; import dr.xml.*; import java.util.ArrayList; /** * */ public class MLOptimizerParser extends AbstractXMLObjectParser { public static final String CHAIN_LENGTH = "chainLength"; public static final String OPTIMIZER = "optimizer"; public String getParserName() { return OPTIMIZER; } public Object parseXMLObject(XMLObject xo) throws XMLParseException { int chainLength = xo.getIntegerAttribute(CHAIN_LENGTH); OperatorSchedule opsched = null; dr.inference.model.Likelihood likelihood = null; ArrayList<Logger> loggers = new ArrayList<Logger>(); for (int i = 0; i < xo.getChildCount(); i++) { Object child = xo.getChild(i); if (child instanceof dr.inference.model.Likelihood) { likelihood = (dr.inference.model.Likelihood)child; } else if (child instanceof OperatorSchedule) { opsched = (OperatorSchedule)child; } else if (child instanceof Logger) { loggers.add((Logger)child); } else { throw new XMLParseException("Unrecognized element found in optimizer element:" + child); } } Logger[] loggerArray = new Logger[loggers.size()]; loggers.toArray(loggerArray); return new MLOptimizer("optimizer1", chainLength, likelihood, opsched, loggerArray); } //************************************************************************ // AbstractXMLObjectParser implementation //************************************************************************ public String getParserDescription() { return "This element returns a maximum likelihood heuristic optimizer and runs the optimization as a side effect."; } public Class getReturnType() { return MLOptimizer.class; } public XMLSyntaxRule[] getSyntaxRules() { return rules; } private final XMLSyntaxRule[] rules = { AttributeRule.newIntegerRule(CHAIN_LENGTH), new ElementRule(OperatorSchedule.class ), new ElementRule(Likelihood.class ), new ElementRule(Logger.class, 1, Integer.MAX_VALUE ) }; }