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 )
};
}