kink_exp.py 文件源码

def plot_prediction_MC(model, y_train, y_test, plot_title=''):
    T = y_test.shape[0]
    x_samples, my, vy = model.predict_forward(T, prop_mode=PROP_MC)
    T_train = y_train.shape[0]
    fig = plt.figure()
    ax = fig.add_subplot(111)
    ax.plot(np.arange(T_train), y_train[:, 0], 'k+-')
    ttest = np.arange(T_train, T_train+T)
    ttest = np.reshape(ttest, [T, 1])
    loglik, ranks = compute_log_lik(np.exp(2*model.sn), y_test, my[:, :, 0].T)
    red = 0.1
    green = 0. * red
    blue = 1. - red
    color = np.array([red, green, blue]).T
    for k in np.argsort(ranks):
        ax.plot(ttest, my[:, k, 0], '-', color=color*ranks[k], alpha=0.5)
    # ax.plot(np.tile(ttest, [1, my.shape[1]]), my[:, :, 0], '-x', color='r', alpha=0.3)
    # ax.plot(np.tile(ttest, [1, my.shape[1]]), x_samples[:, :, 0], 'x', color='m', alpha=0.3)
    ax.plot(ttest, y_test, 'ro')
    ax.set_xlim([T_train-5, T_train + T])
    plt.title(plot_title)
    plt.savefig('/tmp/kink_pred_MC_'+plot_title+'.pdf')
    # plt.savefig('/tmp/kink_pred_MC_'+plot_title+'.png')


# generate a dataset from the kink function above