Using Ragel for Hidden Markov Model generation

[Adrian Thurston]

Hi,

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.

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?

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.

Cheers,
  Adrian

edward... at gmail.com wrote:
> Hi everyone,
> 
> I'm interested in using Ragel as the core of a Hidden Markov Model
> (HMM) engine.
> 
> As I understand it, a HMM is really just a non-deterministic finite
> state machine with probabilities that dictate the emissions and
> transition probabilities. So instead of there being a completely random
> choice between two epsilon transitions (for example), there's a
> probability for each path.
> 
> Would it be a big deal to alter Ragel to support probabilities in state
> transitions?
> 
> Thanks!
> 
> Edward
> 
> 
>