Add progress bar to RandomForest

[kenryd]

classify.RandomForest uses the VIGRA library, which is written in C++ and there's not a straightforward way to have a progress bar. Instead, we could train a 1-tree random forest before beginning the full training to see how long it takes to train a tree.

On one test dataset, indications are that this would provide a good estimate of total time (i.e., initial overhead and other factors won't mess things up), since we get the following times for training 1, 2, and 3 trees:

1 tree: 118.812502146 seconds
2 trees: 236.317131042 seconds
3 trees: 351.319090128 seconds

Each successive tree is very close to a multiple of the 1-tree training time. The progress bar could then be based on time.

[jni]

A few more thoughts on this:

• using the threading library we could avoid any time wastage at all: train the 2 or 3 tree forest in one thread, full forest in another. The GIL doesn't actually stop C++ processes so these could run concurrently.
• Taking this idea further, we could use vigra simply as a fast tree classifier, and make all the forest code Python side. This would enable multithreading (currently vigra is not), meaning 8-16-fold speedup. We could also build a feature-selection stage into the classifier.
• We could then also swap out the vigra trees for something else if we found it, which is great since vigra is a pain to install and thus not on the cluster.