{- | Copyright : (c) Henning Thielemann 2007 Maintainer : haskell@henning-thielemann.de Stability : stable Portability : Haskell 98 Markov chains can be used to recompose a list of elements respecting the fact that the probability of a certain element depends on preceding elements in the list. -} module Data.MarkovChain (run, runMulti, ) where import Data.Map (Map) import qualified Data.Map as Map import System.Random (RandomGen, randomR) import Control.Monad.State (State(State), evalState) {- | Creates a chain of elements respecting to the probabilities of possible successors. The list is considered being cyclic in order to have successors for the last elements. Example: > take 100 \$ run 2 "The sad cat sat on the mat. " 0 (Random.mkStdGen 123) -} run :: (Ord a, RandomGen g) => Int -- ^ size of prediction context -> [a] -- ^ training sequence, the one to walk through randomly -> Int -- ^ index to start the random walk within the training sequence -> g -- ^ random generator state -> [a] run n dict start g = let keyError = error "key is not contained in dictionary" fm = createMap n dict {- This is the main function of this program. It is quite involved. If you want to understand it, imagine that the list 'y' completely exists before the computation. -} y = take n (drop start dict) ++ -- run them on the initial random generator state (flip evalState g \$ -- this turns the list of possible successors -- into an action that generate a list -- of randomly chosen items mapM (randomItem . -- lookup all possible successors of an infix flip (Map.findWithDefault keyError) fm . -- turn suffix into an infixes of length n take n) \$ iterate tail y) in y runMulti :: (Ord a, RandomGen g) => Int -- ^ size of prediction context -> [[a]] -- ^ training sequences, the order is relevant -> Int -- ^ index of starting training sequence -> g -- ^ random generator state -> [[a]] runMulti n dicts i g = let wrappedDicts = map ((Nothing :) . map Just) dicts k = sum (map length (take i wrappedDicts)) xs = run n (concat wrappedDicts) k g ([], ys) = segment (maybe (Left ()) Right) xs in map snd ys {- runMulti :: (Ord a, RandomGen g) => Int -- ^ size of prediction context -> [[a]] -- ^ training sequences, the order is relevant -> (Int,Int) -- ^ index to start the random walk within a training sequence -> g -- ^ random generator state -> [[a]] runMulti n dicts (i,j) g = let wrappedDicts = map ((Nothing :) . map Just) dicts k = sum (map length (take i wrappedDicts)) + j xs = run n (concat wrappedDicts) k g (y, ys) = segment (maybe (Left ()) Right) xs in y : map snd ys -} segment :: (a -> Either b c) -> [a] -> ([c], [(b,[c])]) segment p = foldr (\ x ~(y,ys) -> either (\b -> ([], (b,y):ys)) (\c -> (c:y, ys)) (p x)) ([], []) {- | Choose a random item from a list. -} randomItem :: (RandomGen g) => [a] -> State g a randomItem x = fmap (x!!) (randomRState (0, length x - 1)) {- | 'System.Random.randomR' wrapped in a State monad. -} randomRState :: (RandomGen g) => (Int,Int) -> State g Int randomRState bnds = State (randomR bnds) {- | Create a map that lists for each string all possible successors. -} createMap :: (Ord a) => Int -> [a] -> Map [a] [a] createMap n x = let xc = cycle x -- list of the map keys sufxs = map (take n) (iterate tail xc) -- list of the map images, i.e. single element lists imgxs = map (:[]) (drop n xc) mapList = takeMatch x (zip sufxs imgxs) in Map.fromListWith (++) mapList {- | Lazy variant of 'take'. -} takeMatch :: [b] -> [a] -> [a] takeMatch = zipWith (flip const)