Vector newVector = new Vector(5);
Vector newVector = new Vector(2);
  public Enumeration listOptions() {    Vector newVector = new Vector(5);    newVector.addElement(new Option(	      "\tTurn on debugging output.",	      "D", 0, "-D"));    newVector.addElement(new Option(	      "\tFull name of classifier to perform parameter selection on.\n"	      + "\teg: weka.classifiers.bayes.NaiveBayes",	      "W", 1, "-W <classifier class name>"));    newVector.addElement(new Option(	      "\tNumber of folds used for cross validation (default 10).",	      "X", 1, "-X <number of folds>"));    newVector.addElement(new Option(	      "\tClassifier parameter options.\n"	      + "\teg: \"N 1 5 10\" Sets an optimisation parameter for the\n"	      + "\tclassifier with name -N, with lower bound 1, upper bound\n"	      + "\t5, and 10 optimisation steps. The upper bound may be the\n"	      + "\tcharacter 'A' or 'I' to substitute the number of\n"	      + "\tattributes or instances in the training data,\n"	      + "\trespectively. This parameter may be supplied more than\n"	      + "\tonce to optimise over several classifier options\n"	      + "\tsimultaneously.",	      "P", 1, "-P <classifier parameter>"));    newVector.addElement(new Option(	      "\tSets the random number seed (default 1).",	      "S", 1, "-S <random number seed>"));    if ((m_Classifier != null) &&	(m_Classifier instanceof OptionHandler)) {      newVector.addElement(new Option("",	        "", 0,		"\nOptions specific to sub-classifier "	        + m_Classifier.getClass().getName()		+ ":\n(use -- to signal start of sub-classifier options)"));      Enumeration enum = ((OptionHandler)m_Classifier).listOptions();      while (enum.hasMoreElements()) {	newVector.addElement(enum.nextElement());      }    }