def bdnn_transform(inputs, w, u):

    # """
    # :param inputs. shape = (batch_size, feature_size)
    # :param w : decide neighbors
    # :param u : decide neighbors
    # :return: trans_inputs. shape = (batch_size, feature_size*len(neighbors))
    # """

    neighbors_1 = np.arange(-w, -u, u)
    neighbors_2 = np.array([-1, 0, 1])
    neighbors_3 = np.arange(1+u, w+1, u)

    neighbors = np.concatenate((neighbors_1, neighbors_2, neighbors_3), axis=0)

    pad_size = 2*w + inputs.shape[0]
    pad_inputs = np.zeros((pad_size, inputs.shape[1]))
    pad_inputs[0:inputs.shape[0], :] = inputs

    trans_inputs = [
        np.roll(pad_inputs, -1*neighbors[i], axis=0)[0:inputs.shape[0], :]
                    for i in range(neighbors.shape[0])]

    trans_inputs = np.asarray(trans_inputs)
    trans_inputs = np.transpose(trans_inputs, [1, 0, 2])
    trans_inputs = np.reshape(trans_inputs, (trans_inputs.shape[0], -1))

    return trans_inputs