gpr_aep_examples.py 文件源码

def run_step_1D():
    np.random.seed(42)

    print "create dataset ..."
    N = 200
    X = np.random.rand(N, 1) * 3 - 1.5
    Y = step(X)
    # plt.plot(X, Y, 'kx', mew=2)

    def plot(m):
        xx = np.linspace(-3, 3, 100)[:, None]
        mean, var = m.predict_y(xx)
        zu = m.sgp_layer.zu
        mean_u, var_u = m.predict_f(zu)
        plt.figure()
        plt.plot(X, Y, 'kx', mew=2)
        plt.plot(xx, mean, 'b', lw=2)
        plt.fill_between(
            xx[:, 0],
            mean[:, 0] - 2 * np.sqrt(var[:, 0]),
            mean[:, 0] + 2 * np.sqrt(var[:, 0]),
            color='blue', alpha=0.2)
        plt.errorbar(zu, mean_u, yerr=2 * np.sqrt(var_u), fmt='ro')

        no_samples = 20
        xx = np.linspace(-3, 3, 500)[:, None]
        f_samples = m.sample_f(xx, no_samples)
        for i in range(no_samples):
            plt.plot(xx, f_samples[:, :, i], linewidth=0.5, alpha=0.5)

        plt.xlim(-3, 3)

    # inference
    print "create model and optimize ..."
    M = 20
    model = aep.SGPR(X, Y, M, lik='Gaussian')
    model.optimise(method='L-BFGS-B', alpha=0.9, maxiter=2000)
    plot(model)
    plt.savefig('/tmp/aep_gpr_step.pdf')
    # plt.show()