Hi again,
I believe I've answered my first question (clumsily I suppose) by taking the
state inferred from the layered model, then taking e.g.
submodels=[]
lgs = state.get_levels()[0].gs
for l,lg in enumerate(lgs):
submodels.append(gt.NestedBlockState(lg,bs=[lvl.layer_states[l].b for
lvl in state.get_levels()]
but presumably there's a cleaner way of accessing these values.
For my second question, when you say in the paper to apply agglomerative
hierarchical clustering, is there a cleaner way of doing this through
graph-tool than just brute force? I.e. fitting the model for different bins
then comparing description lengths (including subtracting log eqn 18 or 19)
for each possible combination? This seems like it would be quite slow for
non-small networks, especially for a reasonable initial number of layers, so
again I assume there's an alternative.
If not then my third question is answered, as I can just suitably modify the
prior then only trial merges for property Y.
Thanks,
John
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