I'm an advanced beginner, and I'm finding it hard to comprehend Data.Machine.Moore. Is there a way to explain more to me how this would look in practice? Or is there another way to organize it that is less "computer-sciency" and I could work with more easily? D On Thu, Aug 24, 2017 at 9:13 PM, John Wiegley wrote:

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"DR" == Dennis Raddle writes:

DR> I have a search optimization problem in which there are a lot of DR> individual fitness functions which combine scores to give an overall DR> fitness level.

DR> Each fitness function will be associated with data, both initial data to DR> configure it, and a cache that it maintains.

This sounds a lot like a network of Moore machines, which could be combined monoidally to arrive at the final fitness function.

-- John Wiegley GPG fingerprint = 4710 CF98 AF9B 327B B80F http://newartisans.com 60E1 46C4 BD1A 7AC1 4BA2

Thanks very much! I'll look at it in detail. D On Thu, Aug 24, 2017 at 10:30 PM, John Wiegley wrote:

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"DR" == Dennis Raddle writes:

DR> I'm an advanced beginner, and I'm finding it hard to comprehend DR> Data.Machine.Moore. Is there a way to explain more to me how this would DR> look in practice? Or is there another way to organize it that is less DR> "computer-sciency" and I could work with more easily?

Thinks of a Moore machine as a packaged up State function, where you have an initial state, and the type of that state is hidden within the machine. For example:

{-# LANGUAGE ExistentialQuantification #-}

data Moore i m o = forall s. Moore { mooreInit :: s , mooreFunc :: i -> StateT s m o }

This machine is really just a packaged function, yielding outputs from inputs, while dependent on an internal state that may vary at each call.

Since the state is "existential" (or private to Moore), it can be changed freely when you combine machines:

instance (Monad m, Monoid o) => Monoid (Moore i m o) where mempty = Moore () (const (return mempty)) Moore s1 f1 `mappend` Moore s2 f2 = Moore (s1, s2) go where go ev = StateT $ \(st1, st2) -> do (mres, st1') <- runStateT (f1 ev) st1 (mres', st2') <- runStateT (f2 ev) st2 return (mres <> mres', (st1', st2'))

Something like this could serve your needs, without needing an external package like 'machines'. I've used it to do almost just what you described initially.

-- John Wiegley GPG fingerprint = 4710 CF98 AF9B 327B B80F http://newartisans.com 60E1 46C4 BD1A 7AC1 4BA2