(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
You're looking at a typo. Thanks for catching.

On Fri, Oct 17, 2014 at 2:38 PM, eickenberg <notifications@github.com>
wrote:

> In nilearn/decoding/fista.py:
>
> > +            b = lipschitz_constant * linalg.norm(x - y, 2)
> > +            assert a <= b, err_msg + ("(a = %g >= %g)" % (a, b))
> > +
> > +
> > +def mfista(f1_grad, f2_prox, total_energy, lipschitz_constant, w_size,
> > +           dgap_tol=None, init=None, max_iter=1000, tol=1e-4,
> > +           check_lipschitz=False, dgap_factor=None, callback=None,
> > +           verbose=2):
> > +    """
> > +
> > +    Parameters
> > +    ----------
> > +    f1_grad : callable(w) -> np.array
> > +        Gradient of smooth part of energy
> > +
> > +    f2_prox : callable(w, stepsize, dgap_tol, init?) -> float, dict
>
> What is the question mark for?
>
> —
> Reply to this email directly or view it on GitHub
> <https://github.com/nilearn/nilearn/pull/219/files#r19014904>.
>



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