gplvm_aep_examples.py 文件源码

def run_xor():
    from operator import xor

    from scipy import special
    # create dataset
    print "generating dataset..."

    n = 25
    Y = np.zeros((0, 3))
    for i in [0, 1]:
        for j in [0, 1]:
            a = i * np.ones((n, 1))
            b = j * np.ones((n, 1))
            c = xor(bool(i), bool(j)) * np.ones((n, 1))
            Y_ij = np.hstack((a, b, c))
            Y = np.vstack((Y, Y_ij))

    Y = 2 * Y - 1

    # inference
    print "inference ..."
    M = 10
    D = 2
    lvm = aep.SGPLVM(Y, D, M, lik='Probit')
    lvm.optimise(method='L-BFGS-B', alpha=0.1, maxiter=200)

    # predict given inputs
    mx, vx = lvm.get_posterior_x()
    lims = [-1.5, 1.5]
    x = np.linspace(*lims, num=101)
    y = np.linspace(*lims, num=101)
    X, Y = np.meshgrid(x, y)
    X_ravel = X.ravel()
    Y_ravel = Y.ravel()
    inputs = np.vstack((X_ravel, Y_ravel)).T
    my, vy = lvm.predict_f(inputs)
    t = my / np.sqrt(1 + vy)
    Z = 0.5 * (1 + special.erf(t / np.sqrt(2)))
    for d in range(3):
        plt.figure()
        plt.scatter(mx[:, 0], mx[:, 1])
        zu = lvm.sgp_layer.zu
        plt.plot(zu[:, 0], zu[:, 1], 'ko')
        plt.contour(X, Y, np.log(Z[:, d] + 1e-16).reshape(X.shape))
        plt.xlim(*lims)
        plt.ylim(*lims)

    # Y_test = np.array([[1, -1, 1], [-1, 1, 1], [-1, -1, -1], [1, 1, -1]])
    # # impute missing data
    # for k in range(3):
    #   Y_test_k = Y_test
    #   missing_mask = np.ones_like(Y_test_k)
    #   missing_mask[:, k] = 0
    #   my_pred, vy_pred = lvm.impute_missing(
    #       Y_test_k, missing_mask,
    #       alpha=0.1, no_iters=100, add_noise=False)

    #   print k, my_pred, vy_pred, Y_test_k

    plt.show()