(WIP) Sparse models: S-LASSO and TV-l1

- Supports TV-l1 and S-LASSO priors
- Supports logistic and squared losses
- Has cross validation
- Can automatically select alpha by CV (+ automatic computation of useful alpha ranges for the CV)
- Warning: User must supply l1_ratio
One things that makes the haxby dataset unusual is that it has a much larger number of samples than most datasets. As a result, the regularization is less important on this example.

What I suggest is to take only a subset of the data for training, and use the rest for testing. You can do that with the following:

<pre>
condition_mask_train = np.logical_and(condition_mask, labels['sessions'] < 10)
condition_mask_test = np.logical_and(condition_mask, labels['sessions'] >= 10)
</pre>

and then define X_train, y_train, X_test, y_test below.

You can then compute the performance of the model on left-out data using the predict method of the model.