Safe Haskell	None
Language	Haskell2010

Numeric.LinearAlgebra.SVD.SVDLIBC

Contents

Singular value decomposition
Sparse SVD
Parameters

Synopsis

svd :: Int -> Matrix Double -> (Matrix Double, Vector Double, Matrix Double)
svd' :: SVDParams -> Int -> Matrix Double -> (Matrix Double, Vector Double, Matrix Double)
sparsify :: Matrix Double -> CSR
sparseSvd :: Int -> CSR -> (Matrix Double, Vector Double, Matrix Double)
sparseSvd' :: SVDParams -> Int -> CSR -> (Matrix Double, Vector Double, Matrix Double)
data SVDParams = SVDParams {
- maxIters :: Maybe Int
- minEigenvalRange :: (Double, Double)
- kappa :: Double
}
defaultSVDParams :: SVDParams

Singular value decomposition

svd :: Int -> Matrix Double -> (Matrix Double, Vector Double, Matrix Double) Source #

svd rank a is the sparse SVD of matrix a with the given rank This function handles the conversion to svdlibc's sparse representation.

svd' :: SVDParams -> Int -> Matrix Double -> (Matrix Double, Vector Double, Matrix Double) Source #

Similar to svd but allowing control over the SVDParams.

Sparse SVD

sparsify :: Matrix Double -> CSR Source #

sparseSvd :: Int -> CSR -> (Matrix Double, Vector Double, Matrix Double) Source #

sparseSvd rank a is the sparse SVD of matrix a with the given rank This function handles the conversion to svdlibc's sparse representation, but does not require making the whole matrix dense first

sparseSvd' :: SVDParams -> Int -> CSR -> (Matrix Double, Vector Double, Matrix Double) Source #

Like sparseSvd but providing greater control over the SVDParams used for the computation.

Parameters

data SVDParams Source #

Constructors

SVDParams
Fields maxIters :: Maybe Int Maximum iteration count minEigenvalRange :: (Double, Double) Eigenvalues in this range are considered uninteresting kappa :: Double

defaultSVDParams :: SVDParams Source #

No iteration limit, exclude eigenvalues in range ((-10^{-30}, +10^{-30}), kappa of10^{-6}).