Questions about herding procedure

[QiaoZhongzheng]

I am a little confused about the realization of exemplar selection with herding. In main_resnet_tf.py , from line 202 to 217:

    print('Exemplars selection starting ...')
    for iter_dico in range(nb_cl):
        ind_cl     = np.where(label_dico == order[iter_dico+itera*nb_cl])[0]
        D          = Dtot[:,ind_cl]
        files_iter = processed_files[ind_cl]
        mu         = np.mean(D,axis=1)
        w_t        = mu
        step_t     = 0
        while not(len(files_protoset[itera*nb_cl+iter_dico]) == nb_protos_cl) and step_t<1.1*nb_protos_cl:
            tmp_t   = np.dot(w_t,D)
            ind_max = np.argmax(tmp_t)
            w_t     = w_t + mu - D[:,ind_max]
            step_t  += 1
            if files_iter[ind_max] not in files_protoset[itera*nb_cl+iter_dico]:
              files_protoset[itera*nb_cl+iter_dico].append(files_iter[ind_max])

The key of these codes is w_t = w_t + mu - D[:,ind_max], which lets w_t move in the direction of mu - D[:,ind_max], away from the current nearest sample. However, following Algorithm 4 in the paper, the final object of herding is to select m samples, making the mean of the exemplars closest to the class mean in the feature space. I am not sure whether these codes achieve the same goal by using such an iteration way. Anyone can explain or prove this? Thanks in advance : )