Hi again,
I'm following the cookbook and I'm collecting vertex marginals during
mcmc_equilibrate, so
pv = [None] * len(state.get_levels())
def collect_marginals(s):
global pv
pv = [sl.collect_vertex_marginals(pv[l]) for l, sl in
enumerate(s.get_levels())]
gt.mcmc_equilibrate(state, force_niter=1000, mcmc_args=dict(niter=10),
wait=wait,
nbreaks=nbreaks, epsilon=epsilon, callback=collect_marginals
)
After this is done, every element of pv will be, basically, a list of arrays
with as many elements as many vertexs are at specific level, each with the
counts for memberships.
In fact, when I need to assign groups to the original vertexs to a blockstate
at level L > 0 (say, level 2) I can use
state.project_partition(2, 0)
the vertex map will contain as many elements as the number of vertex of my
graph, each labeled according to the blocks at level 2 of the hierarchy.
I cannot find a smart way to project the marginals I've collected, would it be
sufficient to sum numbers for pv[0] following the hierarchy (that is, summing
counts for all groups at level 0 which are included in the same group at level
2)?
Thanks
d
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