1. Imporve benchmark algorithm(the Naive Bayes classifier) to 87% accuracy. Main idea: use sqrt to smooth the log probabilites of each P(wi|ti). So that small probabilities(large absolute value since all of them are negative after log) got larger, yet big probabilites are not changed. The intuition is that the
difference between big probability should be emphasized and the difference between small probabilities should be forgiven. After all we care when which topic most
likely generated the given word, rather than which topic are less unlikely to not generate the word.

2. Add the server mode of model/bench/decode.py