(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
We should make sure we are all talking about the same rescaling if/when we are talking about rescaling. The default of `rescale_alpha` is set to `True` which I fully endorse, because we do not want different numbers of samples changing the effective regularization: This parameter indicates whether or not we put `1. / (2 * n_samples)` in front of the loss instead of `1. / 2.`

We should discuss a) whether `False` for this parameter is ever useful (AFAIK, sklearn does this scaling by default) and b) whether we should talk about a much more interesting scaling which is a scaling as a fraction of `alpha_max`. The only reason not to do this is that we only have an upper bound for `alpha_max` and not the exact value. The upper bound is not unreasonable, though, as it is the `alpha_max` of the corresponding tvl1 variation lasso.