title: n-gram markov chain transition table
body: <p>I'm trying to build an n-gram markov model from a given piece of text, and then access the transition table for it so I can calculate the conditional entropy for each sequence of words of length n (the grams). 
  For example, in a 2-gram model, after reading in a corpus of text</p>
  
  <p>"dogs chase cats dogs chase cats dogs chase cats
      dogs chase cats dogs chase cats dogs chase cats
      dogs chase cats dogs chase cats dogs chase cats
      dogs chase people"</p>
  
  <p>and building an internal transition table, the state "dogs chase" may transition to the state "chase cats" with probability 0.9, and to state "chase people" with probability 0.1. If I know of the possible transitions, I can calculate the conditional entropy.</p>
  
  <p>Are there any good python libraries for doing this? I've checked NLTK, SRILM, and others but haven't found much.</p>