change benchmark to allow different track xml for training and eval
add log prob to totext
deal with epsilon in more parameterized way in emission model via fudge parameter.  let this be zero if desired as well to accept zero probs using specialized log function
further epsilon cleaning
override scikit learn logprobs when supervised training to use fudge factor consistently and allow 0 probs if desired
dont throw fatal error when gaps in training data.  though its usually not a great idea
Update README.md
add a few more tests on scaling... but it all seems to be working
print track distrubition
fix bad bug in track load where, when not updating, the preloaded track dictionary was not being used
merge multitrack