@Override public Enumeration<Option> listOptions(){
  Vector<Option> newVector=new Vector<Option>();
  newVector.addElement(new Option("\tApplies a Markov Blanket correction to the network structure, \n" + "\tafter a network structure is learned. This ensures that all \n" + "\tnodes in the network are part of the Markov blanket of the \n"+ "\tclassifier node.","mbc",0,"-mbc"));
  newVector.addElement(new Option("\tScore type (LOO-CV,k-Fold-CV,Cumulative-CV)","S",1,"-S [LOO-CV|k-Fold-CV|Cumulative-CV]"));
  newVector.addElement(new Option("\tUse probabilistic or 0/1 scoring.\n\t(default probabilistic scoring)","Q",0,"-Q"));
  newVector.addAll(Collections.list(super.listOptions()));
  return newVector.elements();
}

@ Override public Enumeration < Option > listOptions ( ) { Vector < Option > newVector = new Vector < Option > ( ) ; newVector . addElement ( new Option ( "\tApplies a Markov Blanket correction to the network structure, \n" + "\tafter a network structure is learned. This ensures that all \n" + "\tnodes in the network are part of the Markov blanket of the \n" + "\tclassifier node." , "mbc" , 0 , "-mbc" ) ) ; newVector . addElement ( new Option ( "\tScore type (LOO-CV,k-Fold-CV,Cumulative-CV)" , "S" , 1 , "-S [LOO-CV|k-Fold-CV|Cumulative-CV]" ) ) ; newVector . addElement ( new Option ( "\tUse probabilistic or 0/1 scoring.\n\t(default probabilistic scoring)" , "Q" , 0 , "-Q" ) ) ; newVector . addAll ( Collections . list ( super . listOptions ( ) ) ) ; return newVector . elements ( ) ; }
returns an enumeration describing the available options
