Fisher information for 2 stimuli

- Added: started implementing the effect of a second stimulus on the Fisher information (inverse actually) of one object.
FI 2 stimuli, working min_dist and kappa effect

- Added: Effect of min_dist and kappa seem to work properly.
Correct average, computation of average of density directly

- Fix: Was not taking the average properly. Need to average over true support of theta_2, not full space
- Added: Estimate p(r | theta_1, theta_2) from samples and directly average out theta_2. For a specific min_distance for now only.
Fisher information computations

- Added: compute FI from estimated posterior. Actually wrong here, see next commit.
- Added: Estimate posterior from samples, put distance constraint into prior
Fisher information from estimated posterior

- Wrong again, but kept for reference
Fisher information from marginal probabilities

- Added: fixed the sampling and computation. Now works, but FI wrongly computed (from average posterior and not per sample)
Fisher information marginal and marginal probabilities

- Moved: Refactored into its own file.
- Added: Now works well, just need to compute it for different minimum distance, can then make claims about FI dependence and interactions
Script to recreate all plots in PLOS paper

- Added: plots for the networks
Updates for Pcolor_2d_data

- Added: Handles ticks interpolation, cool now.
- Added: A mouse over function to show the appropriate x and y values, as well as the corresponding data item
Fisher info computation

- Added: new plot and saving some figures
Fisher information computations 1D

- Added: Modified to compute FI better. Not sure if 100% correct though...
Say finished / email finished

- Added: Function 'say'ing or emailing when the computation is done.
Fisher information 2D

- Added: Compute the effect of another stimulus on the FI of a specific item. 2D version. Should do ND version next.
Commit pre-computer mod

- Many changes, check source code. Nothing that specific.
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.