Am 15.01.20 um 18:01 schrieb Davide Cittaro: > 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)?
The group labels of the projected state will match the node index of the original level. So you only need to look into the marginal distribution for that level, and copy it to the base level. -- Tiago de Paula Peixoto <[email protected]> _______________________________________________ graph-tool mailing list [email protected] https://lists.skewed.de/mailman/listinfo/graph-tool
