for (int i = FIRST_CLASSTYPE; i <= LAST_CLASSTYPE; i++) {  if (!canPredict(i)) continue;   m_handleMissingPredictors[i] = checkMissingPredictors(i, 20, false); m_handleMissingClass[i]      = checkMissingClass(i, 20, false); }
  protected void setUp() throws Exception {    m_Classifier   = getClassifier();    m_Tester       = getTester();    m_OptionTester = getOptionTester();    m_updateableClassifier         = m_Tester.updateableClassifier()[0];    m_weightedInstancesHandler     = m_Tester.weightedInstancesHandler()[0];    m_multiInstanceHandler         = m_Tester.multiInstanceHandler()[0];    m_NominalPredictors            = new boolean[LAST_CLASSTYPE + 1];    m_NumericPredictors            = new boolean[LAST_CLASSTYPE + 1];    m_StringPredictors             = new boolean[LAST_CLASSTYPE + 1];    m_DatePredictors               = new boolean[LAST_CLASSTYPE + 1];    m_RelationalPredictors         = new boolean[LAST_CLASSTYPE + 1];    m_handleMissingPredictors      = new boolean[LAST_CLASSTYPE + 1];    m_handleMissingClass           = new boolean[LAST_CLASSTYPE + 1];    m_handleClassAsFirstAttribute  = new boolean[LAST_CLASSTYPE + 1];    m_handleClassAsSecondAttribute = new boolean[LAST_CLASSTYPE + 1];    m_RegressionResults            = new FastVector[LAST_CLASSTYPE + 1];    m_NClasses                     = 4;    // initialize attributes    checkAttributes(true,  false, false, false, false, false);    checkAttributes(false, true,  false, false, false, false);    checkAttributes(false, false, true,  false, false, false);    checkAttributes(false, false, false, true,  false, false);    checkAttributes(false, false, false, false, true,  false);  }