hstatistics 0.2.4.2 → 0.2.5

raw patch · 3 files changed

+23/−3 lines, 3 filesPVP ok

version bump matches the API change (PVP)

API changes (from Hackage documentation)

Files

• CHANGES +3/−0
• hstatistics.cabal +1/−1
• lib/Numeric/Statistics/PCA.hs +19/−2

@@ -94,3 +94,6 @@ 0.2.4.2: 		Conrad Parker pointed out that hmatrix defaults to  		  Data.Vector.Storable++0.2.5:+		Added pcaN as requested by Marcel Ruegenberg

@@ -1,5 +1,5 @@ Name:               hstatistics-Version:            0.2.4.2+Version:            0.2.5 License:            BSD3 License-file:       LICENSE Copyright:          (c) A.V.H. McPhail 2010, 2011, 2012

@@ -13,13 +13,15 @@ -----------------------------------------------------------------------------  module Numeric.Statistics.PCA (-                               pca, pcaTransform, pcaReduce+                               pca, pcaN, pcaTransform, pcaReduce                           ) where   -----------------------------------------------------------------------------  import qualified Data.Array.IArray as I +import Data.List(sortBy)+import Data.Ord(comparing)  import Numeric.LinearAlgebra @@ -29,7 +31,8 @@  ----------------------------------------------------------------------------- --- | find the n principal components of multidimensional data+-- | find the principal components of multidimensional data greater than+--    the threshhold pca :: I.Array Int (Vector Double)    -- the data     -> Double                         -- eigenvalue threshold     -> Matrix Double@@ -41,6 +44,20 @@               v' = zip val vec               v = filter (\(x,_) -> x > q) v'  -- keep only eigens > than parameter           in fromColumns $ snd $ unzip v++-- | find N greatest principal components of multidimensional data+--    according to size of the eigenvalue+pcaN :: I.Array Int (Vector Double)    -- the data+     -> Int                            -- number of components to return+     -> Matrix Double+pcaN d n = let d' = fmap (\x -> x - (scalar $ mean x)) d -- remove the mean from each dimension+               cv = covarianceMatrix d'+               (val',vec') = eigSH cv           -- the covariance matrix is real symmetric+               val = toList val'+               vec = toColumns vec'+               v' = zip val vec+               v = take n $ reverse $ sortBy (comparing fst) v'+           in fromColumns $ snd $ unzip v  -- | perform a PCA transform of the original data (remove mean) -- |     Final = M^T Data^T