Choose Interpolation for pcolor_2d_data

- Add: Option to choose the interpolation style. Usually nearest
Ignore cache of Network statistics

- Add: Ignore cache for Network statistics if desired. Useful when trying multiple theoretical FI computations.
Different kappa for FI, mixed/feature pop fixes, Plotting neurons receptive fields improvements

- Add: FI computation now handles different kappa per neuron. Allows to use the same function to compute the FI for mixed/feature codes. Early, should be checked (issue #32)
- Add: Mixed/feature pop code now use bivariate fisher neurons. Should be checked, the relation between kappa and their actual coverage might be more tricky. But did some work and seems to match properly.
- Fix: Can control if endpoint should be included when initialising matrices and 1D vectors. Should be careful.
- Add: Control the extent around plots for Neuron coverage
- Add: Try to convert kappa into an actual stddev… Going through A_k(.), as found in book. But not really working… #33
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New stimuli generation options

- Add: Generate stimuli in small ranges or constant separated. Do not give this to a StatMeasurer, you will freak it out… (more precisely, because \theta is not supported everywhere, then you do too well and get weird likelihoods as well)
Error message tweak
Handle feat_ratio option
New options

- Add: Option to control the Ratio between Feature neurons receptive fields kappas.
- Add: Control the minimum enforced distance. Does have a weird effect, not sure what.
- Add: Use the theoretical covariance in FI computations. Should make it usable for sampling as well. #34
- Add: Use the say command to talk when work is completed. Super cool when working late and waiting on runs on your Macbook. Yes.
Tweaks
First commit Launcher_Multipleobjectchecker

Holds multiple function to verify what happens when T>1 for the FI / Sampler match. Not suuuper conclusive...
Tweaks
Compute FI easily, use random subsets for var/curv FI estimation, tweaks

- Add: Compute the FI, using the sum definition in RandomFactorialNetwork.  Use the KL covariance or not.
- Add: Estimate_fi_from_posterior and Estimate_precision_from_posterior can now be averaged over random subset of datapoints. Faster to compute and not too bad.
- Fix: Plot_liklihood_twoangles can now remove the mean or not, for comparison.
Tweaks
Conversion function from kappa to gaussian standard dev

- Add: A function to convert kappa into a wrapped gaussian standard deviation. Very good for kappa > 3.0.
Fixed ellipse sizes for receptive fields, added 1D neuron plot, refactor

- Fix: the receptive field shown with an ellipse now correctly takes the Kappa converted to std dev.
- Add: Plot the response of a neuron along 1D, using its preferred stimulus as cue for the other dimensions.
- Add: New creation method for create_full_conjunctive, only provide rcscale, assumes ratio of 1 with very low noise. To be verified.
- Add: Function init_feature_space to create a 1D vector of the possible angles.
New experiment descriptions, new reloading methods

- Fix: slightly clearer definition of spaces and parameters.
- Add: Multiple dataset reloaders.
- Add: Plots for memory curves fits. Compares FI/Var/Experim curves
- Add: Plots for 3D volumes. Some weird parameter mixups to fix…
New launchers and plots

- Add: Launcher to get memory curves for different sigma/rcscale. Either gets the memory curves from the posterior variance, curvature, precision and FI, or from the theoretical FI, using the computed KL covariance.
- Add: PBS version of the launcher above, given a specific sigma/rcscale.
- Add: PBS Launcher only getting theoretical FI.
- Add: Launcher for 3D volume for memory curves. Similar to above.
- Add: Appropriate reloaders
- Add: Plotting for 3D volume. Multiple Pcolor 2D and a 3D contour plot using Mayavi (which requires some post-processing, need to automate it)