(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
Thanks. Fixing right away.

On Wed, Oct 22, 2014 at 11:14 AM, Loïc Estève <notifications@github.com>
wrote:

> In nilearn/decoding/fista.py:
>
> > +    Lipschitz continuity of the function with respect to the given
> > +    constant `L`. This confidence increases with the `n_trials` parameter.
> > +
> > +    Parameters
> > +    ----------
> > +    f : callable,
> > +      The function to be checked for Lipschitz continuity.
> > +      `f` takes a vector of float as unique argument.
> > +      The size of the input vector is determined by `ndim`.
> > +
> > +    ndim : int,
> > +      Dimension of the input of the function to be checked for Lipschitz
> > +      continuity (i.e. it corresponds to the size of the vector that `f`
> > +      takes as an argument).
> > +
> > +    lischitz_constant : float,
>
> li**p**schitz
>
> —
> Reply to this email directly or view it on GitHub
> <https://github.com/nilearn/nilearn/pull/219/files#r19201962>.
>



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DED