Further corrections, test speedup, unseen vocab

Test now accounts for unseen vocabulary. Need to check for correctness
of value used for unseen in Katz backoff with GT counts.

testLM.py precalculates this value, but training may need to discount
for this value beforehand. Precalculating keeps testLM.py quick.

By adding for unseen vocab, was able to do a quick test by doing the
bizarre; training over test.txt and testing over train.txt. As train.txt
is several times larger, this allowed a quick train time, while taxing
testLM.py.

The test shows testLM.py to be quick over a large test file, although
this speed may decrease with a larger LM file. PPL values were around
700. Comparison needs to be done to check this is a good value, but is a
lot better than smaller values we were getting beforehand.

TODO: Check correctness of unseen vocab value
TODO: Compare to nltk speed and ppl values