Am 19.03.20 um 18:33 schrieb Deklan Webster: > I'm attempting to use get_edges_prob to find the most likely missing > edges out of every possible non-edge. I know every possible edge is O(n^2). > > Currently I'm sampling the like this: > > non_edges_probs = [[] for _ in range(len(non_edges))] > > def collect_edge_probs(s): > s = s.levels[0] > > for i, non_edge in enumerate(non_edges): > p = s.get_edges_prob([non_edge], [], > entropy_args=dict(partition_dl=False)) > non_edges_probs[i].append(p) > > gt.mcmc_equilibrate(nested_state, > force_niter=100, > mcmc_args=dict(niter=10), > callback=collect_edge_probs, > verbose=True) > > Is there a way to speed this up at all? If not, is there a heuristic I > can use to reduce the number of possibilities?
There is no way to avoid looking at all possibilities, but you could pass the actual list at once, instead of iterating through it and passing lists of size 1. The reason get_edges_prob() exists and accepts lists is precisely to speed things up in this case. Best, Tiago -- Tiago de Paula Peixoto <ti...@skewed.de>
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