package dr.inferencexml.operators;
import dr.inference.operators.MCMCOperator;
import dr.inference.operators.TeamOperator;
import dr.xml.*;
/**
*
*/
public class TeamOperatorParser extends AbstractXMLObjectParser {
public static final String TEAM_OPERATOR = "teamOperator";
public static final String SUBSET_SIZE = "size";
// public static final String TARGET_ACCEPTANCE = "targetAcceptance";
public String getParserName() {
return TEAM_OPERATOR;
}
public Object parseXMLObject(XMLObject xo) throws XMLParseException {
final double weight = xo.getDoubleAttribute(MCMCOperator.WEIGHT);
final int size = xo.getIntegerAttribute(SUBSET_SIZE);
//final double targetProb = xo.getAttribute(TARGET_ACCEPTANCE, 0.2);
// if (targetProb <= 0.0 || targetProb >= 1.0)
// throw new RuntimeException("Target acceptance probability must be between 0.0 and 1.0");
MCMCOperator[] o = new MCMCOperator[xo.getChildCount()];
for (int i = 0; i < o.length; i++) {
o[i] = (MCMCOperator) xo.getChild(i);
}
return new TeamOperator(o, size, weight);
}
//************************************************************************
// AbstractXMLObjectParser implementation
//************************************************************************
public String getParserDescription() {
return "An arbitrary list of operators; A random subset of size N is aggregated in one operation." +
" Operators may have unequal weights - in that case a subset probability of selection is proportional to " +
"the sum of it's members weights.";
}
public Class getReturnType() {
return TeamOperator.class;
}
public XMLSyntaxRule[] getSyntaxRules() {
return rules;
}
private final XMLSyntaxRule[] rules = {
new ElementRule(MCMCOperator.class, 1, Integer.MAX_VALUE),
AttributeRule.newDoubleRule(MCMCOperator.WEIGHT),
AttributeRule.newIntegerRule(SUBSET_SIZE)
//AttributeRule.newDoubleRule(TARGET_ACCEPTANCE, true)
};
}