float64_t C = 1.0;
  index_t num_samples = 50;
  CMath::init_random(5);
  SGMatrix<float64_t> data =
    CDataGenerator::generate_gaussians(num_samples, 2, 2);
  CDenseFeatures<float64_t> features(data);

  SGVector<index_t> train_idx(num_samples), test_idx(num_samples);
  SGVector<float64_t> labels(num_samples);
  for (index_t i = 0, j = 0; i < data.num_cols; ++i)
  {
    if (i % 2 == 0)
      train_idx[j] = i;
    else
      test_idx[j++] = i;

    labels[i/2] = (i < data.num_cols/2) ? 0.0 : 1.0;
  }

  CDenseFeatures<float64_t>* train_feats = (CDenseFeatures<float64_t>*)features.copy_subset(train_idx);
  CDenseFeatures<float64_t>* test_feats =  (CDenseFeatures<float64_t>*)features.copy_subset(test_idx);

  CMulticlassLabels* ground_truth = new CMulticlassLabels(labels);

  //CMulticlassOCAS* mocas = new CMulticlassOCAS(C, train_feats, ground_truth);
  CMulticlassLibLinear* mocas = new CMulticlassLibLinear(C, train_feats, ground_truth);
  mocas->io->set_loglevel(MSG_DEBUG);
  mocas->train();

  CLabels* pred = mocas->apply_multiclass(test_feats);
  for (int i = 0; i < num_samples; ++i)
    std::cout << ((CMulticlassLabels*)pred)->get_label(i) << " " << ((CMulticlassLabels)labels).get_label(i) << std::endl;

  SG_UNREF(mocas);
  SG_UNREF(train_feats);
  SG_UNREF(test_feats);
  SG_UNREF(pred);