--
-- A k-means clustering implementation.
-- Run the generate-samples program first to create some random data.
--
module Main where
import Kmeans
import Config
import Data.Array.Accelerate as A
import Data.Array.Accelerate.Examples.Internal as A
import Control.Applicative
import Control.Monad ( unless )
import Data.Binary ( decodeFile )
import Data.Label ( get )
import System.Directory
import System.Environment
import Prelude as P
main :: IO ()
main = do
beginMonitoring
argv <- getArgs
(_, opts, rest) <- parseArgs options defaults header footer argv
inputs <- (&&) <$> doesFileExist "points.bin"
<*> doesFileExist "clusters"
unless inputs $ do
error "Run the GenSamples program first to generate random data"
points' <- decodeFile "points.bin"
initial' <- read `fmap` readFile "clusters"
let nclusters = P.length initial'
npoints = P.length points'
solve = run1 backend (kmeans (use points))
backend = get optBackend opts
initial :: Vector (Cluster Float)
initial = A.fromList (Z:.nclusters) initial'
points :: Vector (Point Float)
points = A.fromList (Z:.npoints) points'
-- Warm up first by printing the expected results
--
putStrLn $ "number of points: " P.++ show npoints
putStrLn $ "final clusters:\n" P.++
unlines (P.map show . A.toList $ solve initial)
-- Now benchmark
--
runBenchmarks opts rest
[ bench "k-means" $ whnf solve initial ]