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? 
Will do ASAP.

On Tue, Dec 16, 2014 at 4:48 PM, Eleftherios Garyfallidis <
notifications@github.com> wrote:
>
> In doc/examples/sfm_tracking.py
> <https://github.com/nipy/dipy/pull/460#discussion-diff-21945407>:
>
> > +For the sake of brevity, we will take only the first 1000 seeds, generating
> > +only 1000 streamlines. Remove this line to track from many more points in all of
> > +the white matter
> > +"""
> > +
> > +seeds = seeds[:1000]
> > +
> > +"""
> > +We now have the necessary components to construct a tracking pipeline and
> > +execute the tracking
> > +"""
> > +
> > +from dipy.tracking.local import LocalTracking
> > +streamlines = LocalTracking(pnm, classifier, seeds, affine, step_size=.5)
> > +
> > +streamlines = list(streamlines)
>
> If someone does here what you suggest and remove that line it will crash
> his memory because later in fvtk.streamtube the program will try to create
> streamtubes for too many streamlines. What I would do is to use all the
> seeds here but later in the visualization I would use
> select_random_set_of_streamlines from dipy.tracking.streamline and
> visualize only a random set of all the streamlines generated. If you agree
> with the suggestion and change this I will merge this PR. Thx again.
>
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> Reply to this email directly or view it on GitHub
> <https://github.com/nipy/dipy/pull/460/files#r21945407>.
>