Getting negative entropy/positive Log likelihood for a weighted network with positive edge weights.

Dear Tiago, I am trying to model a weighted dense network with edge weights
in the range 0 to 1, with the non-hierarchical SBM for both degree corrected
and non-degree corrected versions. However, I get negative entropy for both
dc and ndc SBMs. I have attached a sample network for your consideration
along with a minimal working example from my code. Thanks for your time!

Sukrit sukrit_network.gz
<http://main-discussion-list-for-the-graph-tool-project.982480.n3.nabble.com/file/t496012/sukrit_network.gz&gt;
working_example_W_SBM.py
<http://main-discussion-list-for-the-graph-tool-project.982480.n3.nabble.com/file/t496012/working_example_W_SBM.py&gt;

When using real edge covariates, the overall likelihood becomes a
probability *density*. Since the probability density can exceed 1 in value,
its log can be positive, and hence the entropy can be negative. This is normal.