Using IsingGlauberBlockState

first of all thank you for the great piece of software.

Second, I would like a clarification on the usage of the inference block models. I was trying to use the IsingGlauberBlockState class on some time series of a neural network. I used as a base the code shown for the Epidemic version on the cookbook (which works no problem).
However switching the EpidemicsBlockState class for either the continuous or discrete Ising results in an error:

AttributeError: module 'graph_tool.inference.libgraph_tool_inference' has no attribute 'mcmc_cising_glauber_sweep_h'

This doesn’t happen with the equilibrium Ising model classes, (however they do not fit my current dataset). Indeed it seems, by looking at the source code for the C++ side of the library, that the dynamic models do not have those functions implemented (my c++ is rusty tho), however the equilibrium ones have (for example Also those functions are explicitly called from the python code, so it seems they should be existing.

I am a bit confused if I am properly using the classes, if I stumbled on some unfinished functionality or there was a build accident in the last version, and if I should move this to the gitlab’s issues

Could you please:

  1. Open an issue in the issue tracker: Issues · Tiago Peixoto / graph-tool · GitLab
  2. Provide a minimal working example that shows the problem.

Many thanks!

I tried signing up for an account on your gitlab but it says it’s pending an administrator approval. It should be the same github account im using here, when you approve it I’ll open the issue :+1:

The account is approved.

linking the issue here for reference: Using IsingGlauberBlockState inference (#757) · Issues · Tiago Peixoto / graph-tool · GitLab