(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 will fix asap.

On Tuesday, October 7, 2014, Gael Varoquaux <notifications@github.com>
wrote:

> In plot_oasis_smoothlasso_vbm.py:
>
> > +from nilearn.input_data import NiftiMasker
> > +
> > +n_subjects = 100   # more subjects requires more memory
> > +memory = Memory("cache")
> > +
> > +### Load Oasis dataset ########################################################
> > +dataset_files = datasets.fetch_oasis_vbm(n_subjects=n_subjects)
> > +age = dataset_files.ext_vars['age'].astype(float)
> > +
> > +
> > +### Fit and predict ###########################################################
> > +from nilearn.decoding import SpaceNet
> > +decoder = SpaceNet(memory=memory, screening_percentile=20., verbose=1,
> > +                   smoothing_fwhm=2.)
> > +decoder.fit(dataset_files.gray_matter_maps, age)
> > +coef_niimg = decoder.coef_img_
>
> The naming convention is to end names of variables that are niimgs with
> '_img', not '_niimg'. So that would be 'coeg_img'. Please also correct all
> the other variable names of this script.
>
> —
> Reply to this email directly or view it on GitHub
> <https://github.com/nilearn/nilearn/pull/219/files#r18521100>.
>


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DED