import AI.GeneticAlgorithm.Simple
import Text.Printf
import Data.List as L
import Control.DeepSeq
import Control.Monad.Random
import Control.Monad
newtype SinInt = SinInt [Double]
instance NFData SinInt where
rnf (SinInt xs) = rnf xs `seq` ()
instance Show SinInt where
show (SinInt []) = ""
show (SinInt (x:xs)) =
let start = printf "%.5f" x
end = concat $ zipWith (\c p -> printf "%+.5f" c ++ "X^" ++ show p) xs [1 :: Int ..]
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 (SinInt xs) (SinInt ys) =
return [ SinInt (L.zipWith (\x y -> (x+y)/2) xs ys) ]
mutation (SinInt xs) = do
idx <- getRandomR (0, length xs - 1)
dx <- getRandomR (-10.0, 10.0)
let t = xs !! idx
xs' = take idx xs ++ [t + t*dx] ++ drop (idx+1) xs
return $ SinInt xs'
fitness int =
let max_err = 1000.0 in
max_err - (min (err int) max_err)
randomSinInt :: (MonadRandom m) => m SinInt
randomSinInt = do
lst <- replicateM polynomialOrder (getRandomR (-10.0,10.0))
return $ SinInt lst
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