[WIP] Multinomial newtoncg

This adds newton-cg solver to the linear_model.LogisticRegression class when multi_class="multinomial". Benchmarks on a few sparse datasets against L-BFGS solver are included below. Convergence times tended to be slightly lower for newton-cg and the scores were pretty much identical on these datasets.

Benchmark on 20NewsGroup dataset (11314 x 130107 sparse matrix):

![20news_bench](https://cloud.githubusercontent.com/assets/4610553/4179948/d35bf1d0-36dd-11e4-8bb5-b02299024725.jpeg)

Benchmark on Brown Corpus using TFIDF feature extraction (335 x 42453 sparse matrix):

![brown_bench](https://cloud.githubusercontent.com/assets/4610553/4179957/57b56b32-36de-11e4-93d9-70ba5f551238.jpeg)

Benchmark on Movie Review Corpus using TFIDF feature extraction (1340 x 39659 sparse matrix)

![moviereview_bench](https://cloud.githubusercontent.com/assets/4610553/4179803/13e0a806-36d7-11e4-9775-22426e48189a.jpeg)

Benchmark on Reuters Corpus using TFIDF feature extraction (7227 x 30916 sparse matrix)

![reuters_bench](https://cloud.githubusercontent.com/assets/4610553/4180034/40167f08-36e2-11e4-907f-90f7bbe29b7e.jpeg)


This is not the same as Bishop Eq 4.110. The actual hessian matrix is never computed; instead, the hessian-vector product is computed directly. The technique used to do this is in the reference section of the docstrings of _multinomial_loss_grad_hess. For convenience I'll also include it here:

    Barak A. Pearlmutter (1993). Fast Exact Multiplication by the Hessian.
        http://www.bcl.hamilton.ie/~barak/papers/nc-hessian.pdf

The exact formula applied to the multinomial loss objective function is not provided in the paper nor could I find it elsewhere online, I had to derive it using the technique described in the reference. If you find it helpful I can post the work here. In terms of motivation for doing this, I believe this direct calculation is cheaper than calculating the hessian directly and then multiplying by the vector input.