String [] m_ClassifierOptions = Utils.partitionOptions(options);
m_ClassifierOptions = Utils.partitionOptions(options);
  public Enumeration listOptions() {    Vector newVector = new Vector(2);    newVector.addElement(new Option(	      "\tTurn on debugging output.",	      "D", 0, "-D"));    newVector.addElement(new Option(	      "\tFull name of the learner analysed.\n"	      +"\teg: weka.classifiers.NaiveBayes",	      "W", 1, "-W"));    if ((m_Classifier != null) 	&& (m_Classifier instanceof OptionHandler)) {      newVector.addElement(new Option("", "", 0, 				      "\nOptions specific to learner "				      + m_Classifier.getClass().getName()				      + ":"));      Enumeration enum = ((OptionHandler)m_Classifier).listOptions();      while (enum.hasMoreElements())	newVector.addElement(enum.nextElement());    }    return newVector.elements();  }  /**   * Parses a given list of options. Valid options are:<p>   *   * -D <br>   * Turn on debugging output.<p>   *   * -W classname <br>   * Specify the full class name of a learner to perform the    * tests on (required).<p>   *   * Options after -- are passed to the designated learner    *   * @param options the list of options as an array of strings   * @exception Exception if an option is not supported   */  public void setOptions(String[] options) throws Exception {    setDebug(Utils.getFlag('D', options));        String learnerString = Utils.getOption('W', options);    if (learnerString.length() == 0) {      throw new Exception("A learner must be specified with the -W option.");    }    setLearner(learnerString);    // Check the remaining options are valid for the specified classifier    if ((m_Classifier != null) 	&& (m_Classifier instanceof OptionHandler)) {      String [] m_ClassifierOptions = Utils.partitionOptions(options);      String [] tempOptions = (String [])m_ClassifierOptions.clone();      ((OptionHandler)m_Classifier).setOptions(tempOptions);      Utils.checkForRemainingOptions(tempOptions);    }  }