Oops. I can just abandon the Entropy typeclass and put the function directly into Model, eh? Yeah, I think I'll do that...
Supposing I didn't want to - any alternatives? Other instances of Entropy I might consider: instance (Eq a) => Entropy [a] instance (Eq a) => Entropy (Tree a) On Mon, Jun 6, 2011 at 02:13, Scott Lawrence <byt...@gmail.com> wrote: > On Mon, Jun 6, 2011 at 01:52, Yitzchak Gale <g...@sefer.org> wrote: >> Scott Lawrence wrote: >> You almost never want to use UndecidableInstances >> when writing practical programs in Haskell. > > Ah. That's what I wanted to know :P > > (Although it does seem to me - from looking around docs and the source > - that GHC's rules for allowing certain combinations might be a bit > too conservative - but then, I have next to no idea what I'm doing, so > hey.) > >> When GHC tells you that you need them, it almost >> always means that your types are poorly designed, >> usually due to influence from previous experience >> with OOP. > > * hides behind book > >> >> Your best bet is to step back and think again about >> the problem you are trying to solve. What is the >> best way to formulate the problem functionally? >> That will lead you in the right direction. Please >> feel free to share more details about what you are >> trying to do. We would be happy to help you work out >> some good directions. > > I'm modelling text in a markov-model-like way. I have an actual markov > model (albeit one in which X_n depends on a fixed range X_n-1 .. > X-n-k). I'm vaguely anticipating the presence of other models: > > class Model m a | m -> a where > lexemes :: m -> Set a > genFunc :: m -> [a] -> ProbDist a > > Having that working, I'm trying to estimate the information entropy of a model > > entropy :: (Model m) => m -> Double > > (This is a slight simplification, since entropy needs a second > argument "precision" to know when to terminate.) > > Which works well and fine - this function is pretty trivial to > implement, on the assumption that Markov (the instance of Model > described above) implements genFunc properly. But it happens not to - > the array argument to genFunc must be the right size, otherwise an > even probability distribution is used. So my OOP-infected mind wants > to specialize 'entropy' for Markov: > > class Entropy d where > entropy :: d -> Double -- again, simplified > > Note that it's not (Entropy d a) because the type of the lexeme > doesn't matter. Now, the problem code > > instance (Model m a) => Entropy m where > entropy = undefined > > > As you might have picked up, I suspect the part where I want to > specialize entropy for Markov is where I mess up - but I'm not sure > what to do. (To be clear, I expect to want to specialize entropy for > other models too - the general function I have in mind would be > horribly slow for many reasonable models.) > > Thanks. > >> >> Regards, >> Yitz >> > > > > -- > Scott Lawrence > -- Scott Lawrence _______________________________________________ Haskell-Cafe mailing list Haskell-Cafe@haskell.org http://www.haskell.org/mailman/listinfo/haskell-cafe
