(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
These are far-fetched considerations and I'm still not convinced :) In any
case, I've implemented the *random_state* arg so we can move on.

On Wed, Oct 22, 2014 at 3:11 PM, Gael Varoquaux <notifications@github.com>
wrote:

> In nilearn/decoding/fista.py:
>
> > +
> > +    n_trials : int,
> > +      Number of tests performed when assessing the Lipschitz continuity of
> > +      function `f`. The more tests, the more confident we are in the
> > +      Lipschitz continuity of `f` if the test passes.
> > +
> > +    err_msg : {str, or None},
> > +      String used to tune the output message when the test fails.
> > +      If `None`, we'll generate our own.
> > +
> > +    Raises
> > +    ------
> > +    RuntimeError
> > +    """
> > +
> > +    rng = check_random_state(42)
>
>  I don't mind implementing this. My point was I don't see what added value
> this modification has, appart from being a "'good" practice.
> The choice of 42 is arbitrary. To make a check more robust, one might want
> to marginalize it.
>
> —
> Reply to this email directly or view it on GitHub
> <https://github.com/nilearn/nilearn/pull/219/files#r19211812>.
>



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