Using Ragel for Hidden Markov Model generation
edward... at gmail.com
Fri Nov 3 15:19:18 UTC 2006
> It might be possible to use Ragel as is. There is an experimental (and
> undocumented) semantic condition feature which obeys the rules of NFA to
> DFA conversion. A semantic condition is a block of code which is
> associated with a transition. It must evaluate to true for the
> transition to be taken. Conditions were introduced for the purpose of
> parsing variable length fields. You could use them to do a roll of the
> dice. Look in test/cond?.rl for examples of conditions.
That sounds great, thanks! I'll take a look at it this weekend.
> If conditions don't cut it, then modifying Ragel might be an option.
> Though I don't know enough about HMMs to say for sure if it could be
> done. How are HMMs specified?
It's usually specified by a matrix of probabilities, but that can be
played around with. I'm actually writing a few pages on HMM usage in
general and will be done in a few days. Would you be interested in
reading a rough draft?
> I can tell you that Ragel was designed for producing deterministic
> parsers. Internally it has no epsilon transitions. Machines are made
> deterministic on the fly. So it doesn't have many of the features that
> comprehensive state machine libraries like Grail+ have.
I'm not sure what the breadth of those features is like, but I took a
look at the examples available at
http://www.csd.uwo.ca/Research/grail/.man/index.html and from the looks
of it, I don't really need those. All I'm after is a very efficient
finite state machine with probabilities in its transitions and possibly
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