(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
OK, I'll look at your comments this morning. See you.

On Sat, Sep 13, 2014 at 8:38 PM, Gael Varoquaux <notifications@github.com>
wrote:

> In nilearn/decoding/fista.py:
>
> > +
> > +        if energy < best_energy:
> > +            best_energy = energy
> > +            best_w = w.copy()
> > +            best_z = z.copy()
> > +            best_t = t
> > +            best_dgap_tol = dgap_tol
> > +            best_dgap_tol = dgap_tol
> > +
> > +    return (best_w,
> > +            objective,
> > +            dict(w=best_w.copy(), z=best_z, t=best_t, dgap_tol=best_dgap_tol,
> > +                 stepsize=stepsize))
> > +
> > +# pure ISTA
> > +ista = partial(mfista, pure_ista=True)
>
> Let's just remove these 2 last lines: we don't need them.
>
> —
> Reply to this email directly or view it on GitHub
> <https://github.com/nilearn/nilearn/pull/219/files#r17513677>.
>



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DED