(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
@agramfort this typo was fixed a long time ago.

On Sun, Oct 19, 2014 at 9:51 PM, Alexandre Gramfort <
notifications@github.com> wrote:

> In plot_haxby_space_net.py:
>
> > +_, target = np.unique(labels['labels'], return_inverse=True)
> > +
> > +# make X (design matrix) and y (response variable)
> > +import nibabel
> > +niimgs  = nibabel.load(data_files.func[0])
> > +X_train = nibabel.Nifti1Image(niimgs.get_data()[:, :, :, condition_mask_train],
> > +                        niimgs.get_affine())
> > +y_train = target[condition_mask_train]
> > +X_test = nibabel.Nifti1Image(niimgs.get_data()[:, :, :, condition_mask_test],
> > +                        niimgs.get_affine())
> > +y_test = target[condition_mask_test]
> > +
> > +
> > +### Fit and predict ##########################################################
> > +from nilearn.decoding import SpaceNet
> > +decoder = SpaceNet(memory="cache", is_classif=True, penalty="smooth-lasso",
>
> you call it smooth-lasso and below you say tv-l1 weights
>
> —
> Reply to this email directly or view it on GitHub
> <https://github.com/nilearn/nilearn/pull/219/files#r19062860>.
>



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