Files
GitMining/DataModel.hs

56 lines
1.5 KiB
Haskell

module DataModel where
import Data.Set (Set)
import qualified Data.Set as Set
type Count = Int
type Frequency = Double
type Confidence = Double
type Lift = Double
class Freq a where
frequency :: [ItemSet] -> a -> Frequency
data Item = Item String deriving (Eq, Ord)
instance Show Item where
show (Item a) = a
data ItemSet = ItemSet (Set Item) deriving (Eq, Ord)
instance Show ItemSet where
show (ItemSet x) =
init $ foldr ((\y old -> y ++ " " ++ old).show) "" (Set.toList x)
instance Freq ItemSet where
frequency table (ItemSet set) =
setCount / fromIntegral (length table) where
setCount = fromIntegral $ count table (ItemSet set)
count :: [ItemSet] -> ItemSet -> Count
count table (ItemSet set) =
length (filter isSuperset table) where
isSuperset (ItemSet row) = set `Set.isSubsetOf` row
difference :: ItemSet -> ItemSet -> ItemSet
difference (ItemSet set1) (ItemSet set2) = ItemSet (Set.difference set1 set2)
empty :: ItemSet
empty = ItemSet (Set.fromList [])
data Rule = Rule ItemSet ItemSet deriving (Eq)
instance Show Rule where
show (Rule a b) = show a ++ "," ++ show b
instance Freq Rule where
frequency table (Rule (ItemSet set1) (ItemSet set2)) = frequency table $
ItemSet (set1 `Set.union` set2)
confidence :: [ItemSet] -> Rule -> Confidence
confidence table (Rule x y) = frequency table (Rule x y) / frequency table x
lift :: [ItemSet] -> Rule -> Lift
lift table (Rule x y) = confidence table (Rule x y) / frequency table y