pred_S_y = knn.predict(S_x)
// Find the misclassified S_y
sel_x = np.squeeze(S_x[np.nonzero(pred_S_y != S_y), :])
sel_y = S_y[np.nonzero(pred_S_y != S_y)]
// If we need to offer support for the indices selected
// We concatenate the misclassified samples with the seed and the
// minority samples
if self.return_indices:
idx_tmp = np.nonzero(y == key)[0][np.nonzero(pred_S_y != S_y)]
idx_under = np.concatenate((idx_under,
idx_maj_sample,
idx_tmp),
After Change
// We concatenate the misclassified samples with the seed and the
// minority samples
if self.return_indices:
idx_tmp = np.flatnonzero(y == key)[
np.flatnonzero(pred_S_y != S_y)]
idx_under = np.concatenate((idx_under,
idx_maj_sample,
