custom_dr.py 文件源码

def sim_target_supervised(target_data, target_labels, sigma, idx, target_params):
    cur_labels = target_labels[idx]
    N = cur_labels.shape[0]

    N_labels = len(np.unique(cur_labels))

    Gt, mask = np.zeros((N, N)), np.zeros((N, N))

    for i in range(N):
        for j in range(N):
            if cur_labels[i] == cur_labels[j]:
                Gt[i, j] = 0.8
                mask[i, j] = 1
            else:
                Gt[i, j] = 0.1
                mask[i, j] = 0.8 / (N_labels - 1)

    return np.float32(Gt), np.float32(mask)