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Simple parallel neural networks implementation in pure Haskell
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Simple parallel genetic algorithm implementation in pure Haskell
Sin() function interpolation example ( src/MainSin.hs ):
import AI.GeneticAlgorithm.Simple import System.Random import Text.Printf import Data.List as L import Control.DeepSeq newtype SinInt = SinInt [Double] instance NFData SinInt where rnf (SinInt xs) = rnf xs `seq` () instance Show SinInt where show (SinInt ) = "<empty SinInt>" show (SinInt (x:xs)) = let start = printf "%.5f" x lst = [1 :: Int ..] end = concat $ zipWith (\c p -> printf "%+.5f" c ++ "X^" ++ show p) xs lst in start ++ end polynomialOrder = 4 :: Int err :: SinInt -> Double err (SinInt xs) = let f x = snd $ L.foldl' (\(mlt,s) coeff -> (mlt*x, s + coeff*mlt)) (1,0) xs in maximum [ abs $ sin x - f x | x <- [0.0,0.001 .. pi/2]] instance Chromosome SinInt where crossover g (SinInt xs) (SinInt ys) = ( [ SinInt (L.zipWith (\x y -> (x+y)/2) xs ys) ], g) mutation g (SinInt xs) = let (idx, g') = randomR (0, length xs - 1) g (dx, g'') = randomR (-10.0, 10.0) g' t = xs !! idx xs' = take idx xs ++ [t + t*dx] ++ drop (idx+1) xs in (SinInt xs', g'') fitness int = let max_err = 1000.0 in max_err - (min (err int) max_err) randomSinInt gen = let (lst, gen') = L.foldl' (\(xs, g) _ -> let (x, g') = randomR (-10.0,10.0) g in (x:xs,g') ) (, gen) [0..polynomialOrder] in (SinInt lst, gen') stopf :: SinInt -> Int -> IO Bool stopf best gnum = do let e = err best _ <- printf "Generation: %02d, Error: %.8f\n" gnum e return $ e < 0.0002 || gnum > 20 main = do int <- runGAIO 64 0.1 randomSinInt stopf putStrLn "" putStrLn $ "Result: " ++ show int
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