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 <tiago@skewed.de>