models_reservoirs.py 文件源码

def test_mixtureMV(args):
    """test_mixtureMV

    Test mixtureMV() by sampling from dummy statistics mu, S, p
    """

    mixcomps = 3
    d = 2
    mu = np.random.uniform(-1, 1, size = (mixcomps, d))
    S = np.array([np.eye(d) * 0.01 for c in range(mixcomps)])
    for s in S:
        s += 0.05 * np.random.uniform(-1, 1, size = s.shape)
        s[np.tril_indices(d, -1)] = s[np.triu_indices(d, 1)]
    pi = np.ones((mixcomps, )) * 1.0/mixcomps

    lr = LearningRules(ndim_out = d, dim = d)
    lr.learnFORCEmdn_setup(mixcomps = mixcomps)

    fig = plt.figure()
    gs = gridspec.GridSpec(2, mixcomps)
    for g in gs:
        fig.add_subplot(g)

    ax = fig.axes[0]
    ax.plot(mu[:,0], mu[:,1], "ko", alpha = 0.5)
    # ax.set_xlim((-1.1, 1.1))
    # ax.set_ylim((-1.1, 1.1))


    ax = fig.axes[0] # (2 * mixcomps) + c]
    for i in range(1000):
        y = lr.mixtureMV(mu, S, pi)
        print "y", y
        ax.plot([y[0]], [y[1]], "ro", alpha = 0.2, markersize = 3.0)

    ax.set_aspect(1)

    for c in range(mixcomps):
        ax = fig.axes[mixcomps + c]
        ax.imshow(S[c], cmap = plt.get_cmap('PiYG'))

    fig.show()
    plt.show()

    print "mu", mu.shape, "S", S.shape