(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
No, will look at it asap (hopefully today).

On Wed, Oct 8, 2014 at 12:58 PM, Gael Varoquaux <notifications@github.com>
wrote:

> In nilearn/decoding/fista.py:
>
> > +        # ISTA is provably monotonous
> > +        if i > 0 and check_monotonous:
> > +            assert energy <= 1.1 * old_energy, (
> > +                "Oops! old_energy = %g < energy = %g. This is "
> > +                "unacceptable since ISTA and mFISTA are provably monotonous."
> > +                " The must be a bug in the code. For example, maybe "
> > +                "you are assuming a wrong lipschitz constant or you "
> > +                "have bugs in the way you compute the gradient of"
> > +                " the smooth part of the energy.") % (old_energy, energy)
> > +
> > +        # energy house-keeping
> > +        energy_delta = old_energy - energy
> > +        old_energy = energy
> > +
> > +        # z update
> > +        if energy_delta < 0.:
>
> Any update on that?
>
> —
> Reply to this email directly or view it on GitHub
> <https://github.com/nilearn/nilearn/pull/219/files#r18575016>.
>



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DED