Hello,
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 graph_blockmodel_dynamics_pseudo_cising_mcmc_h.cc). 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
