bayesquad.py 文件源码

def plot_gp_model(self, f, unit_sp, args, test_range=(-5, 5, 50), plot_dims=(0, 0)):
        # plot out_dim vs. in_dim
        in_dim, out_dim = plot_dims
        # test input must have the same dimension as specified in kernel
        test = np.linspace(*test_range)
        test_pts = np.zeros((self.d, len(test)))
        test_pts[in_dim, :] = test
        # function value observations at training points (unit sigma-points)
        y = np.apply_along_axis(f, 0, unit_sp, args)
        fx = np.apply_along_axis(f, 0, test_pts, args)  # function values at test points
        K = self.kern.K(unit_sp.T)  # covariances between sigma-points
        k = self.kern.K(test_pts.T, unit_sp.T)  # covariance between test inputs and sigma-points
        kxx = self.kern.Kdiag(test_pts.T)  # prior predictive variance
        k_iK = cho_solve(cho_factor(K), k.T).T
        gp_mean = k_iK.dot(y[out_dim, :])  # GP mean
        gp_var = np.diag(np.diag(kxx) - k_iK.dot(k.T))  # GP predictive variance
        # plot the GP mean, predictive variance and the true function
        plt.figure()
        plt.plot(test, fx[out_dim, :], color='r', ls='--', lw=2, label='true')
        plt.plot(test, gp_mean, color='b', ls='-', lw=2, label='GP mean')
        plt.fill_between(test, gp_mean + 2 * np.sqrt(gp_var), gp_mean - 2 * np.sqrt(gp_var),
                         color='b', alpha=0.25, label='GP variance')
        plt.plot(unit_sp[in_dim, :], y[out_dim, :],
                 color='k', ls='', marker='o', ms=8, label='data')
        plt.legend()
        plt.show()