module Main where import Control.Applicative import Criterion.Main import Test.QuickCheck import qualified Data.Vector as G import qualified Data.Vector.Unboxed as V import qualified OldKMeans as K import qualified Math.KMeans as K2 main :: IO () main = do persons1 <- generate persons persons2 <- generate persons defaultMain [ bgroup "ints" [ bench "v0.2" \$ whnf kmeans1 ints1 , bench "v0.3" \$ whnf kmeans2 ints2 ] , bgroup "persons" [ bench "v0.2" \$ whnf kmeansP1 persons1 , bench "v0.3" \$ whnf kmeansP2 persons2] ] ints1, ints2 :: [Int] ints1 = [1..10000] ints2 = [1..10000] data Person = Person { age :: Int , weight :: Double , name :: String , salary :: Int } deriving (Eq, Show) instance Arbitrary Person where arbitrary = do Person <\$> choose (2, 100) <*> choose (5, 150) <*> pure "francis" <*> choose (500, 100000) persons :: Gen [Person] persons = vector 10000 -- kmeans of 'Int's in 3 clusters kmeans1 = G.fromList . K.kmeans 3 . map (\i -> (extract i, i)) kmeans2 = K2.kmeans extract dist 3 -- kmeans of 'Person's in 4 clusters kmeansP1 = G.fromList . K.kmeans 4 . map p2v where p2v p = (personToVec p, p) kmeansP2 = K2.kmeans personToVec eucl 4 personToVec :: Person -> V.Vector Double personToVec p = V.fromList [ fromIntegral \$ age p , weight p , fromIntegral \$ salary p ] extract :: Int -> V.Vector Double extract = V.singleton . fromIntegral dist :: K2.Distance dist v1 v2 = V.sum \$ V.zipWith (\x1 x2 -> abs (x1 - x2)) v1 v2 eucl :: K2.Distance eucl v1 v2 = V.sum \$ V.zipWith (\x1 x2 -> (x1 - x2)^2) v1 v2