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? 
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.