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      THIS->clearDataSet();

int
SVM::_train(retrain)
     int retrain
    CODE:
      RETVAL = THIS->train(retrain);
    OUTPUT:
      RETVAL

double
SVM::_crossValidate(nfolds)
     int nfolds
    CODE:
      RETVAL = THIS->crossValidate(nfolds);
    OUTPUT:
      RETVAL

double
SVM::_predict_value(ds)
     DataSet *ds
    CODE:
      RETVAL = THIS->predict_value(ds);
    OUTPUT:
      RETVAL

double
SVM::_predict(ds)
     DataSet *ds
    CODE:
      RETVAL = THIS->predict(ds);
    OUTPUT:
      RETVAL

int
SVM::_saveModel(filename)
     char *filename
    CODE:
      RETVAL = THIS->saveModel(filename);
    OUTPUT:
      RETVAL

int
SVM::_loadModel(filename)
     char *filename
    CODE:
      RETVAL = THIS->loadModel(filename);
    OUTPUT:
      RETVAL

int
SVM::_getNRClass()
    CODE:
      RETVAL = THIS->getNRClass();
    OUTPUT:
      RETVAL

void
SVM::_getLabels(classes)
     int classes
    PPCODE:
     int i;
     int *labels;
     labels = new int[classes];
     if(THIS->getLabels(labels)) {
       for (i=0;i < classes; i++) {
	  XPUSHs(sv_2mortal(newSViv(labels[i])));
       }
     } else {
       XSRETURN_UNDEF;
     }

double
SVM::_getSVRProbability()
    CODE:
      RETVAL = THIS->getSVRProbability();
    OUTPUT:
      RETVAL

int
SVM::_checkProbabilityModel()
    CODE:
      RETVAL = THIS->checkProbabilityModel();
    OUTPUT:
      RETVAL

void
SVM::_setSVMType(st)
     int st
    CODE:
      THIS->setSVMType(st);

int
SVM::_getSVMType()
    CODE:
      RETVAL = THIS->getSVMType();
    OUTPUT:
      RETVAL

void
SVM::_setKernelType(kt)
     int kt
    CODE:
      THIS->setKernelType(kt);

int
SVM::_getKernelType()
    CODE:
      RETVAL = THIS->getKernelType();
    OUTPUT:
      RETVAL

void
SVM::_setGamma(g)
     double g
    CODE:
      THIS->setGamma(g);

double
SVM::_getGamma()
    CODE: