(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
It would at least help my allergy against functions containing the particle
/my/ ... :)

On Wed, Oct 1, 2014 at 2:41 PM, dohmatob elvis dopgima <
notifications@github.com> wrote:

> In nilearn/decoding/space_net.py:
>
> > +        """Unmasks the input vector `w`, according to the mask learned by
> > +        Univariate screening.
> > +
> > +        """
> > +
> > +        if self.support_.sum() < len(self.support_):
> > +            w_ = np.zeros(len(self.support_))
> > +            w_ = np.append(w_, w[-1])
> > +            w_[:-1][self.support_] = w[:-1]
> > +        else:
> > +            w_ = w
> > +
> > +        return w_
> > +
> > +
> > +def _my_alpha_grid(X, y, eps=1e-3, n_alphas=10, l1_ratio=1., alpha_min=0.,
>
> Is it really reasonable to have a function called *space_net** in a named
> space_net.py ?
>
> —
> Reply to this email directly or view it on GitHub
> <https://github.com/nilearn/nilearn/pull/219/files#r18275441>.
>