Adjacent nodes jump from positive to negative function values

[mochar]

Hi, thanks for the great library. When drawing random function values from GraphDiffusionKernel I observe a weird pattern where adjacent nodes jump from negative to positive. You can see what I mean in this image:

kernel = graph_matern.kernels.graph_diffusion_kernel.GraphDiffusionKernel((eigenvectors, eigenvalues), kappa=4, sigma_f=1.0,
                             dtype=tf.float64)
K = kernel.K(np.arange(N).reshape(-1, 1)).numpy()
prior = pm.MvNormal.dist(mu=0, cov=K, shape=(N, )) # pm = PyMC3
fig, axs = plt.subplots(figsize=(13, 3), ncols=3, sharex=True, sharey=True)

for i in range(3):
    draw = prior.random()
#     draw = np.abs(draw)
    vmin = draw.min()
    vmax = draw.max()
    cmap = plt.cm.coolwarm
    nx.draw(G, pos=pos, ax=axs[i], node_color=draw.tolist(),
        width=1, node_size=20, cmap=cmap, vmin=vmin, vmax=vmax)
    sm = plt.cm.ScalarMappable(cmap=cmap, norm=plt.Normalize(vmin=vmin, vmax=vmax))
    sm.set_array([])
    cbar = plt.colorbar(sm, ax=axs[i])

I have no issues with the Matern kernel:

I can make a gist file with all the code if necessary.
Thnx!

[stoprightthere]

Thank you @mochar ! This is indeed an unintended behavior. Please check out the recent fix.

[mochar]

Works great, thanks for the quick fix. Do you mind explaining how you went from dividing by 4 to multiplying by -0.5?

[stoprightthere]

The correct formula is with the multiplication by -0.5. Check out equation (12) in the paper. The previous version apparently appeared due to interference with equation (11). Thank you again for noticing that!

[mochar]

Yes I see. Thanks!

[aterenin]

@mochar Would you mind sending me an e-mail about this? We've discovered that this bug affects one of our experiments, and would thus like to add a thank you note in the acknowledgments section of our paper, which is to be updated shortly.

[mochar]

Just did!