Consider Normalisation

This release fixes a bug when using normalise=True in SLISE-regression.

Furthermore, the impact calculated from normalised and unnormalised values tells different stories. Unnormalised impact lets you reconstruct the original prediction, while normalised impact is more of a comparison to the rest of the data. Thus, they are given separate rows when using the built in print and plot_dist functions.

Improved Examples

Some of the built-in plotting functions have been improved. Warnings from intermediate optimisation steps are now hidden by default (warnings from the last optimisation step are still shown). Finally, the examples have been extended and improved, especially in regards to how to interpret the results.

The Python version of SLISE

First public release of the Python version of the SLISE algorithm.