Sparse Fascicle Model

This is a start on an implementation of the model we describe here: 

http://arxiv.org/abs/1411.0721

The main current issue with this is that this will have a local dependency on scikit-learn, because it uses ElasticNet for the fit. How do people feel about that? 
No - I get something similar. I don't think there is any getting away from
it.

On Tue, Nov 25, 2014 at 5:43 PM, Stefan van der Walt <
notifications@github.com> wrote:

> In doc/examples/sfm_tracking.py:
>
> > +
> > +"""
> > +
> > +pnm = dpp.peaks_from_model(sf_model, maskdata, sphere,
> > +                            mask=mask,
> > +                            relative_peak_threshold=.5,
> > +                            min_separation_angle=25,
> > +                            return_sh=False)
> > +
> > +
> > +"""
> > +We will use the generalized fractional anisotropy (GFA) as a stopping
> > +criterion, and seed in 10k points
> > +"""
> > +
> > +eu = EuDX(pnm.gfa,
>
> I don't have a problem with the execution time--I just wanted to mention
> it in case it was different on my system (it was a bit unexpected, since I
> remember running the other examples in reasonable time before).
>
> —
> Reply to this email directly or view it on GitHub
> <https://github.com/nipy/dipy/pull/460/files#r20911556>.
>