Refactor input conjunctive scale
Highly refactored RandomFactorialNetwork, for K>2

- Add: first version, get_network_response works, but not the statistics gathering subroutines.
Modified most functions to make them compatble with R>2
First fix for Sampler with R>2, not enough

- Bug: the whole inference fails for R>2, as we do not cue correctly...
Merge branch 'develop' into highdimensionnetwork
Merge branch 'master' into highdimensionnetwork
tweaks
Converting sampler to High dimensions

- Converted the sampler, now need to handle multiple angles sampling, need to fix the Gibbs sampler. Or cue all non-targets, other possibility
tweak
Plotting FI to see if this predicts the kappa well.

- Add: this should help respond to the reviewer. No clear connection could be found between precision and a function of the subpopulation sizes though. But with kappa this should work (it did before actually, that's how I compute the fisher info...).
Merge branch 'develop' into highdimensionnetwork
Fix
Sampling fixed for highdimensional
Support for cueing all other features

- Add: Can now set either one feature as cue, or all non-sampled (i.e. different to R=0).
fix
Fisher information computation for R>2

- Add: compute fisher information for highdimensional network. Works as usual, removed mention to kappa_different as they should all be...
Generator/reloader for highdimensional tests
Network output now max to 1

Maybe this is way better?
Actually seem to have better normalising effect, e.g. conjunctive and feature neurons have the same scale now.
Should have done that way earlier...
Made network max output an option
Experimental bias, paper comment