1 files changed, 5 insertions, 223 deletions

diff --git a/packages/base/src/Numeric/LinearAlgebra/HMatrix.hs b/packages/base/src/Numeric/LinearAlgebra/HMatrix.hs
index 677f9ee..8e67eb4 100644
--- a/packages/base/src/Numeric/LinearAlgebra/HMatrix.hs
+++ b/packages/base/src/Numeric/LinearAlgebra/HMatrix.hs
@@ -1,4 +1,4 @@
------------------------------------------------------------------------------
+--------------------------------------------------------------------------------
 {- |
 Module      :  Numeric.LinearAlgebra.HMatrix
 Copyright   :  (c) Alberto Ruiz 2006-14
@@ -7,229 +7,11 @@ Maintainer  :  Alberto Ruiz
 Stability   :  provisional
 
 -}
------------------------------------------------------------------------------
-module Numeric.LinearAlgebra.HMatrix (
-
-    -- * Basic types and data processing
-    module Numeric.LinearAlgebra.Data,
-
-    -- * Arithmetic and numeric classes
-    -- |
-    -- The standard numeric classes are defined elementwise:
-    --
-    -- >>>  vector [1,2,3] * vector [3,0,-2]
-    -- fromList [3.0,0.0,-6.0]
-    --
-    -- >>> matrix 3 [1..9] * ident 3
-    -- (3><3)
-    --  [ 1.0, 0.0, 0.0
-    --  , 0.0, 5.0, 0.0
-    --  , 0.0, 0.0, 9.0 ]
-    --
-    -- In arithmetic operations single-element vectors and matrices
-    -- (created from numeric literals or using 'scalar') automatically
-    -- expand to match the dimensions of the other operand:
-    --
-    -- >>> 5 + 2*ident 3 :: Matrix Double
-    -- (3><3)
-    --  [ 7.0, 5.0, 5.0
-    --  , 5.0, 7.0, 5.0
-    --  , 5.0, 5.0, 7.0 ]
-    --
-    -- >>> matrix 3 [1..9] + matrix 1 [10,20,30]
-    -- (3><3)
-    --  [ 11.0, 12.0, 13.0
-    --  , 24.0, 25.0, 26.0
-    --  , 37.0, 38.0, 39.0 ]
-    --
-
-    -- * Products
-    -- ** dot
-    dot, (<·>),
-    -- ** matrix-vector
-    app, (#>), (!#>),
-    -- ** matrix-matrix
-    mul, (<>),
-    -- | The matrix product is also implemented in the "Data.Monoid" instance, where
-    -- single-element matrices (created from numeric literals or using 'scalar')
-    -- are used for scaling.
-    --
-    -- >>> import Data.Monoid as M
-    -- >>>  let m = matrix 3 [1..6]
-    -- >>> m M.<> 2 M.<> diagl[0.5,1,0]
-    -- (2><3)
-    --  [ 1.0,  4.0, 0.0
-    --  , 4.0, 10.0, 0.0 ]
-    --
-    -- 'mconcat' uses 'optimiseMult' to get the optimal association order.
-
-
-    -- ** other
-    outer, kronecker, cross,
-    scale,
-    sumElements, prodElements,
-
-    -- * Linear Systems
-    (<\>),
-    linearSolve,
-    linearSolveLS,
-    linearSolveSVD,
-    luSolve,
-    cholSolve,
-    cgSolve,
-    cgSolve',
-
-    -- * Inverse and pseudoinverse
-    inv, pinv, pinvTol,
-
-    -- * Determinant and rank
-    rcond, rank,
-    det, invlndet,
-
-    -- * Norms
-    Normed(..),
-    norm_Frob, norm_nuclear,
-
-    -- * Nullspace and range
-    orth,
-    nullspace, null1, null1sym,
-
-    -- * SVD
-    svd,
-    thinSVD,
-    compactSVD,
-    singularValues,
-    leftSV, rightSV,
-
-    -- * Eigensystems
-    eig, eigSH, eigSH',
-    eigenvalues, eigenvaluesSH, eigenvaluesSH',
-    geigSH',
-
-    -- * QR
-    qr, rq, qrRaw, qrgr,
-
-    -- * Cholesky
-    chol, cholSH, mbCholSH,
-
-    -- * Hessenberg
-    hess,
-
-    -- * Schur
-    schur,
-
-    -- * LU
-    lu, luPacked,
-
-    -- * Matrix functions
-    expm,
-    sqrtm,
-    matFunc,
-
-    -- * Correlation and convolution
-    corr, conv, corrMin, corr2, conv2,
-
-    -- * Random arrays
+--------------------------------------------------------------------------------
 
-    Seed, RandDist(..), randomVector, rand, randn, gaussianSample, uniformSample,
-
-    -- * Misc
-    meanCov, rowOuters, peps, relativeError, haussholder, optimiseMult, udot, nullspaceSVD, orthSVD, ranksv,
-    ℝ,ℂ,iC,
-    -- * Auxiliary classes
-    Element, Container, Product, Numeric, LSDiv,
-    Complexable, RealElement,
-    RealOf, ComplexOf, SingleOf, DoubleOf,
-    IndexOf,
-    Field,
---    Normed,
-    Transposable,
-    CGState(..),
-    Testable(..)
+module Numeric.LinearAlgebra.HMatrix (
+    module Numeric.LinearAlgebra
 ) where
 
-import Numeric.LinearAlgebra.Data
-
-import Numeric.Matrix()
-import Numeric.Vector()
-import Data.Packed.Numeric hiding ((<>), mul)
-import Numeric.LinearAlgebra.Algorithms hiding (linearSolve,Normed,orth)
-import qualified Numeric.LinearAlgebra.Algorithms as A
-import Numeric.LinearAlgebra.Util
-import Numeric.LinearAlgebra.Random
-import Numeric.Sparse((!#>))
-import Numeric.LinearAlgebra.Util.CG
-
-{- | infix synonym of 'mul'
-
->>> let a = (3><5) [1..]
->>> a
-(3><5)
- [  1.0,  2.0,  3.0,  4.0,  5.0
- ,  6.0,  7.0,  8.0,  9.0, 10.0
- , 11.0, 12.0, 13.0, 14.0, 15.0 ]
-
->>> let b = (5><2) [1,3, 0,2, -1,5, 7,7, 6,0]
->>> b
-(5><2)
- [  1.0, 3.0
- ,  0.0, 2.0
- , -1.0, 5.0
- ,  7.0, 7.0
- ,  6.0, 0.0 ]
-
->>> a <> b
-(3><2)
- [  56.0,  50.0
- , 121.0, 135.0
- , 186.0, 220.0 ]
-
--}
-(<>) :: Numeric t => Matrix t -> Matrix t -> Matrix t
-(<>) = mXm
-infixr 8 <>
-
--- | dense matrix product
-mul :: Numeric t => Matrix t -> Matrix t -> Matrix t
-mul = mXm
-
-
-{- | Solve a linear system (for square coefficient matrix and several right-hand sides) using the LU decomposition, returning Nothing for a singular system. For underconstrained or overconstrained systems use 'linearSolveLS' or 'linearSolveSVD'.
-
-@
-a = (2><2)
- [ 1.0, 2.0
- , 3.0, 5.0 ]
-@
-
-@
-b = (2><3)
- [  6.0, 1.0, 10.0
- , 15.0, 3.0, 26.0 ]
-@
-
->>> linearSolve a b
-Just (2><3)
- [ -1.4802973661668753e-15,     0.9999999999999997, 1.999999999999997
- ,       3.000000000000001, 1.6653345369377348e-16, 4.000000000000002 ]
-
->>> let Just x = it
->>> disp 5 x
-2x3
--0.00000  1.00000  2.00000
- 3.00000  0.00000  4.00000
-
->>> a <> x
-(2><3)
- [  6.0, 1.0, 10.0
- , 15.0, 3.0, 26.0 ]
-
--}
-linearSolve m b = A.mbLinearSolve m b
-
--- | return an orthonormal basis of the null space of a matrix. See also 'nullspaceSVD'.
-nullspace m = nullspaceSVD (Left (1*eps)) m (rightSV m)
-
--- | return an orthonormal basis of the range space of a matrix. See also 'orthSVD'.
-orth m = orthSVD (Left (1*eps)) m (leftSV m)
+import Numeric.LinearAlgebra