(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
Yes, indeed. Thus we need to do something like:
<pre>
new_mask = mask.copy()
new_mask[mask] = (support > 0)
new_mask = ndimage.binary_dilation(mask).astype(np.bool)
new_mask[np.logical_not(mask)] = 0
support = new_mask[mask]
</pre>

A little bit more work :)