New version

[Thomas Bereknyei]

{-# LANGUAGE GeneralizedNewtypeDeriving,MultiParamTypeClasses #-}

{-Implementation:


PROs: Room for generalization
CONS: Can't have different types of Neurons in a Net, (I think)
-}
import Data.Graph.Inductive
import Data.List (unzip3)
import Data.Maybe (fromJust)

--for viewing
import Data.GraphViz
import System.Process
-- end for viewing

class Show a => Neuron a where
    afunc :: a-> a
    --eval :: (Graph gr) => gr a b -> Node -> a
    eval :: (Graph gr)=>gr a a -> Context a a -> Context a a

class Graph gr => Net gr  where
    view :: (Show a,Show b) =>gr a b -> IO ProcessHandle
    step :: Neuron a => gr a a -> gr a a


--Implementation of standard Neural Network where step simply maps
eval over all the Neurons
--data NN gr a b
--    where NN :: gr a a -> NN gr a a
--data NN gr a b = NN (gr a b)
newtype NN gr a b = NN (gr a b)
   deriving (Graph,DynGraph)
instance (DynGraph gr) => Net (NN gr) where
    view (NN g) = do
            writeFile "./temp.dot" $ graphviz' g
            runCommand "dotty temp.dot"
    step g= gmap (eval g) g


--Implementation of the standard sigmoid neuron with standard weighted sum
newtype NSig a= NSig a
    deriving (Eq,Show,Read,Ord,Enum,Num,Real,Fractional,RealFrac,RealFloat,Floating)
instance (RealFloat a) => Neuron (NSig a) where
    afunc x = 1.0 / (1 + exp x)
    eval g c@(i,n,a,o)= (i,n,afunc . sum $ zipWith (*) az ws,o)
        where   (ns,_,ws)=unzip3 $ inn g n
                az=map (fromJust . lab g) ns


--Examples to work with
-- t has 4 layers of 10,10,2,1 NSig Neurons
nz=zip [1..23] $ (take 10 [-1,-0.9..]) ++ repeat 0  :: [(Int,NSig Double)]
ez= let
        a2layer=[(x,y) | x<-[1..10],y<-[11..20]
                    , or [x+10==y,x*2==y,x*3==y] ]
        a3layer=a2layer++[(x,y) | x<-[11..20],y<-[21,22]
                    , or [ and [y==22,x>15] , and [y==21,x<=15] ]
                    ]
        a4layer=a3layer++[(21,23),(22,23)]
        zipit (x,y) z=(x,y,z)
    in
        zipWith zipit a4layer [-1,-0.9..] :: [(Int,Int,NSig Double)]

t=mkGraph nz ez :: Gr (NSig Double) (NSig Double)
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