Fixed up vectorizing and learning code

Frank,

I made a bunch of changes.  

These include massive simplification of the reading and vectorization.  This wasn't strictly necessary, but it seemed like a good idea.

More important was the way that I changed the vectorization.  For the continuous values, I added log transforms.  For the categorical values, I encoded as they are.  I also increased the feature vector size to 100 to avoid excessive collisions.

In the learning code itself, I got rid of the use of index arrays in favor of shuffling the training data itself.  I also tuned the learning parameters a lot.

The result is that the AUC that results is just a tiny bit less than 0.9 which is pretty close to what I got in R.

If you would like we can go over the code on a call so I can add proper comments.