Am 09.11.18 um 16:30 schrieb Tzu-Chi Yen: > Now I understand that `edges_dl` specifically encodes the flat prior. I have > 2 following questions: > > 🤔- How could I access the terms in Eq.(41) of the PRE paper, i.e. each term > is the level-wise entropy of edge counts, as Eq.(42) describes?
These are given by the different hierarchy levels, level_entropy(1), level_entropy(2), etc. > For the "lesmis" dataset, the bottom-most layer has the entropy: >>> nested_state.level_entropy(0) > Out[•]: 630.133156768878 > > This is exactly the sum of these three entropic terms: "adjacency" > (332.24632), "degree_dl" (170.10951), and "partition_dl" (127.77732). I > could not find a rationale about the missing entropy for edge counts. This is given by the upper layers, as answered above. > 🤔- I found that `nested_state.levels[0].entropy(deg_entropy=True) - > nested_state.levels[0].entropy(deg_entropy=False) < 0`. This command is > expected to print the negative logarithm of Eq.(28) of the paper, which is > positive. I am not sure what went wrong. No, 'deg_entropy` controls the degree part of the likelihood, not the prior. The parameter you want is `degree_dl`. Best, Tiago -- Tiago de Paula Peixoto <[email protected]>
signature.asc
Description: OpenPGP digital signature
_______________________________________________ graph-tool mailing list [email protected] https://lists.skewed.de/mailman/listinfo/graph-tool
