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? 
Hmm. This is testing an object that has caps in its name.

See, e.g.:

https://github.com/nipy/dipy/blob/master/dipy/reconst/tests/test_dti.py#L53

On Thu, Dec 11, 2014 at 4:59 PM, Eleftherios Garyfallidis <
notifications@github.com> wrote:
>
> In dipy/core/tests/test_optimize.py
> <https://github.com/nipy/dipy/pull/460#discussion-diff-21722838>:
>
> > +        def fit(self, X, y):
> > +            self.coef_ = np.ones(X.shape[-1])
> > +
> > +
> > +    MySillySolver = SillySolver()
> > +    n_samples = 100
> > +    n_features = 20
> > +    y = np.random.rand(n_samples)
> > +    X = np.ones((n_samples, n_features))
> > +    MySillySolver.fit(X, y)
> > +    npt.assert_equal(MySillySolver.coef_, np.ones(n_features))
> > +    npt.assert_equal(MySillySolver.predict(X), np.ones(n_samples) * 20)
> > +
> > +
> > +def test_NNLS():
> > +
>
> Same here.
>
> —
> Reply to this email directly or view it on GitHub
> <https://github.com/nipy/dipy/pull/460/files#r21722838>.
>