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? 
why?

On Sat, Dec 13, 2014 at 1:43 PM, Eleftherios Garyfallidis <
notifications@github.com> wrote:
>
> In dipy/core/optimize.py
> <https://github.com/nipy/dipy/pull/460#discussion-diff-21791543>:
>
> > +
> > +        Parameters
> > +        ----------
> > +        X : array-like (n_samples, n_features)
> > +            Samples.
> > +
> > +        Returns
> > +        -------
> > +        C : array, shape = (n_samples,)
> > +            Predicted values.
> > +        """
> > +        X = np.asarray(X)
> > +        return np.dot(X, self.coef_.T)
> > +
> > +
> > +class NNLS(SKLearnLinearSolver):
>
> Write NonNegativeLeastSquares please.
>
> —
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> <https://github.com/nipy/dipy/pull/460/files#r21791543>.
>