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? 
OK - done. And rebased on recent master

On Thu, Dec 11, 2014 at 5:14 PM, Ariel Rokem <arokem@gmail.com> wrote:
>
> OK - will correct these here.
>
> On Thu, Dec 11, 2014 at 5:13 PM, Eleftherios Garyfallidis <
> notifications@github.com> wrote:
>>
>> In dipy/core/tests/test_optimize.py
>> <https://github.com/nipy/dipy/pull/460#discussion-diff-21723363>:
>>
>> > +        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():
>> > +
>>
>> We should correct also this test too. Sorry that we missed it.
>>
>> —
>> Reply to this email directly or view it on GitHub
>> <https://github.com/nipy/dipy/pull/460/files#r21723363>.
>>
>