(WIP) Sparse models: S-LASSO and TV-l1

- Supports TV-l1 and S-LASSO priors
- Supports logistic and squared losses
- Has cross validation
- Can automatically select alpha by CV (+ automatic computation of useful alpha ranges for the CV)
- Warning: User must supply l1_ratio
After investigation, the mask returned by the strategy we have here is not sufficiently structured. Thus I suggest, first to change the default percentile to 20% of the brain, and second the following strategy, which works better:

<pre>
sX = ndimage.gaussian_filter(sX, (2, 2, 2, 0))
...
mask[mask] = (support > 0)
mask = ndimage.binary_dilation(mask).astype(np.bool)
</pre>