Clean PyMC tests

- Deleted: pymc tests, they are now in another folder.
Cleaning, range to xrange

- Small changes, switched all range to xrange.
Conversion between stddev and kappa

- Added: stddev_to_kappa function.
Hierarchical Random Network initial commit

- Added: Hierarchical random network implementation. Uses a two layer network, lower one being a RandomFactorialNetwork, top one sampling it randomly.
- Supports either conjunctive or feature code
- Uses the new automatic rc_scale setting.
Tweaks, automatic rc_scale, modified theoretical network statistics

- Fixed: optimized get_network_response when no specific_neurons are provided, 3x faster now…
- Automatic rc_scale based on optimal coverage of space given provided number of neurons.
- Fix: computation of theoretical network statistics is now fixed (but may be the wrong one anyway)
Inverted behaviour of ratio in Feature RandomFactorialNetwork

- Fix: ratio is now -ratio, to cope with lack of rotation.
Generator for Stimuli values

- Added: supports now lambda functions to generate stimulus. More general and cool overall.
Refactor np.arange -> xrange

- Fixed: converted all for in np.arange to for in xrange, faster.
Nose testing

- Added: small nose unit test for randomfactorialnetwork
Compare von mises versus wrapped gaussian parameters

- Tried to account for issue when fitting a von mises to posterior errors. Seems that this only comes from the posterior having baseline activation (using approximate unimodal instead of multimodal). This does not stem from mismatch between gaussian and von mises. "Bad" for us.