module Data.CRF.Chain1.Constrained.Dataset.External ( Word (..) , unknown , Sent , Prob (unProb) , mkProb , WordL , SentL ) where import qualified Data.Set as S import qualified Data.Map as M -- | A Word is represented by a set of observations -- and a set of potential interpretation labels. -- When the set of potential labels is empty the word -- is considered to be unknown and the default potential -- set is used in its place. data Word a b = Word { obs :: S.Set a -- ^ The set of observations , lbs :: S.Set b -- ^ The set of potential interpretations. } deriving (Show, Eq, Ord) -- | The word is considered to be unknown when the set of potential -- labels is empty. unknown :: Word a b -> Bool unknown word = S.size (lbs word) == 0 {-# INLINE unknown #-} -- | A sentence of words. type Sent a b = [Word a b] -- | A probability distribution defined over elements of type a. -- All elements not included in the map have probability equal -- to 0. newtype Prob a = Prob { unProb :: M.Map a Double } deriving (Show, Eq, Ord) -- | Construct the probability distribution. mkProb :: Ord a => [(a, Double)] -> Prob a mkProb = Prob . normalize . M.fromListWith (+) . filter ((>0).snd) where normalize dist | M.null dist = error "mkProb: no elements with positive probability" | otherwise = let z = sum (M.elems dist) in fmap (/z) dist -- | A WordL is a labeled word, i.e. a word with probability distribution -- defined over labels. We assume that every label from the distribution -- domain is a member of the set of potential labels corresponding to the -- word. TODO: Ensure the assumption using the smart constructor. type WordL a b = (Word a b, Prob b) -- | A sentence of labeled words. type SentL a b = [WordL a b]