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-- Principal component analysis
import Numeric.LinearAlgebra
import System.Directory(doesFileExist)
import System.Process(system)
import Control.Monad(when)
type Vec = Vector Double
type Mat = Matrix Double
{-
-- Vector with the mean value of the columns of a matrix
mean a = constant (recip . fromIntegral . rows $ a) (rows a) <> a
-- covariance matrix of a list of observations stored as rows
cov x = (trans xc <> xc) / fromIntegral (rows x - 1)
where xc = x - asRow (mean x)
-}
-- creates the compression and decompression functions from the desired number of components
pca :: Int -> Mat -> (Vec -> Vec , Vec -> Vec)
pca n dataSet = (encode,decode)
where
encode x = vp #> (x - m)
decode x = x <# vp + m
(m,c) = meanCov dataSet
(_,v) = eigSH (trustSym c)
vp = tr $ takeColumns n v
main = do
ok <- doesFileExist ("mnist.txt")
when (not ok) $ do
putStrLn "\nTrying to download test datafile..."
system("wget -nv http://dis.um.es/~alberto/material/sp/mnist.txt.gz")
system("gunzip mnist.txt.gz")
return ()
m <- loadMatrix "mnist.txt" -- fromFile "mnist.txt" (5000,785)
let xs = takeColumns (cols m -1) m -- the last column is the digit type (class label)
let x = toRows xs !! 4 -- an arbitrary test Vec
let (pe,pd) = pca 10 xs
let y = pe x
print y -- compressed version
print $ norm_2 (x - pd y) / norm_2 x --reconstruction quality
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