hmatrix-0.18.1.0: Numeric Linear Algebra

Numeric.LinearAlgebra

Description

Synopsis

# Basic types and data manipulation

This package works with 2D (Matrix) and 1D (Vector) arrays of real (R) or complex (C) double precision numbers. Single precision and machine integers are also supported for basic arithmetic and data manipulation.

# Numeric classes

The standard numeric classes are defined elementwise (commonly referred to as the Hadamard product or the Schur product):

>>> 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 ]


# Autoconformable dimensions

In most operations, single-element vectors and matrices (created from numeric literals or using scalar), and matrices with just one row or column, 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 ]

>>> (4><3) [1..] + row [10,20,30]
(4><3)
[ 11.0, 22.0, 33.0
, 14.0, 25.0, 36.0
, 17.0, 28.0, 39.0
, 20.0, 31.0, 42.0 ]


# Products

## Dot

dot :: Numeric t => Vector t -> Vector t -> t Source #

(<.>) :: Numeric t => Vector t -> Vector t -> t infixr 8 Source #

An infix synonym for dot

>>> vector [1,2,3,4] <.> vector [-2,0,1,1]
5.0

>>> let 𝑖 = 0:+1 :: C
>>> fromList [1+𝑖,1] <.> fromList [1,1+𝑖]
2.0 :+ 0.0


## Matrix-vector

(#>) :: Numeric t => Matrix t -> Vector t -> Vector t infixr 8 Source #

dense matrix-vector product

>>> let m = (2><3) [1..]
>>> m
(2><3)
[ 1.0, 2.0, 3.0
, 4.0, 5.0, 6.0 ]

>>> let v = vector [10,20,30]

>>> m #> v
fromList [140.0,320.0]


(<#) :: Numeric t => Vector t -> Matrix t -> Vector t infixl 8 Source #

dense vector-matrix product

(!#>) :: GMatrix -> Vector Double -> Vector Double infixr 8 Source #

general matrix - vector product

>>> let m = mkSparse [((0,999),1.0),((1,1999),2.0)]
>>> m !#> vector [1..2000]
fromList [1000.0,4000.0]


## Matrix-matrix

(<>) :: Numeric t => Matrix t -> Matrix t -> Matrix t infixr 8 Source #

dense matrix product

>>> 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 ]


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 :: Product t => Vector t -> Vector t -> Matrix t Source #

Outer product of two vectors.

>>> fromList [1,2,3] outer fromList [5,2,3]
(3><3)
[  5.0, 2.0, 3.0
, 10.0, 4.0, 6.0
, 15.0, 6.0, 9.0 ]


kronecker :: Product t => Matrix t -> Matrix t -> Matrix t Source #

Kronecker product of two matrices.

m1=(2><3)
[ 1.0,  2.0, 0.0
, 0.0, -1.0, 3.0 ]
m2=(4><3)
[  1.0,  2.0,  3.0
,  4.0,  5.0,  6.0
,  7.0,  8.0,  9.0
, 10.0, 11.0, 12.0 ]
>>> kronecker m1 m2
(8><9)
[  1.0,  2.0,  3.0,   2.0,   4.0,   6.0,  0.0,  0.0,  0.0
,  4.0,  5.0,  6.0,   8.0,  10.0,  12.0,  0.0,  0.0,  0.0
,  7.0,  8.0,  9.0,  14.0,  16.0,  18.0,  0.0,  0.0,  0.0
, 10.0, 11.0, 12.0,  20.0,  22.0,  24.0,  0.0,  0.0,  0.0
,  0.0,  0.0,  0.0,  -1.0,  -2.0,  -3.0,  3.0,  6.0,  9.0
,  0.0,  0.0,  0.0,  -4.0,  -5.0,  -6.0, 12.0, 15.0, 18.0
,  0.0,  0.0,  0.0,  -7.0,  -8.0,  -9.0, 21.0, 24.0, 27.0
,  0.0,  0.0,  0.0, -10.0, -11.0, -12.0, 30.0, 33.0, 36.0 ]


cross :: Product t => Vector t -> Vector t -> Vector t Source #

cross product (for three-element vectors)

scale :: Linear t c => t -> c t -> c t Source #

add :: Additive c => c -> c -> c Source #

sumElements :: Container c e => c e -> e Source #

the sum of elements

prodElements :: Container c e => c e -> e Source #

the product of elements

# Linear systems

## General

(<\>) :: (LSDiv c, Field t) => Matrix t -> c t -> c t infixl 7 Source #

Least squares solution of a linear system, similar to the \ operator of Matlab/Octave (based on linearSolveSVD)

a = (3><2)
[ 1.0,  2.0
, 2.0,  4.0
, 2.0, -1.0 ]

v = vector [13.0,27.0,1.0]

>>> let x = a <\> v
>>> x
fromList [3.0799999999999996,5.159999999999999]

>>> a #> x
fromList [13.399999999999999,26.799999999999997,1.0]


It also admits multiple right-hand sides stored as columns in a matrix.

linearSolveLS :: Field t => Matrix t -> Matrix t -> Matrix t Source #

Least squared error solution of an overconstrained linear system, or the minimum norm solution of an underconstrained system. For rank-deficient systems use linearSolveSVD.

linearSolveSVD :: Field t => Matrix t -> Matrix t -> Matrix t Source #

Minimum norm solution of a general linear least squares problem Ax=B using the SVD. Admits rank-deficient systems but it is slower than linearSolveLS. The effective rank of A is determined by treating as zero those singular valures which are less than eps times the largest singular value.

## Determined

linearSolve :: Field t => Matrix t -> Matrix t -> Maybe (Matrix t) Source #

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 ]


luSolve :: Field t => LU t -> Matrix t -> Matrix t Source #

Solution of a linear system (for several right hand sides) from the precomputed LU factorization obtained by luPacked.

luPacked :: Field t => Matrix t -> LU t Source #

Obtains the LU decomposition of a matrix in a compact data structure suitable for luSolve.

luSolve' :: (Container Vector t, Fractional t) => LU t -> Matrix t -> Matrix t Source #

Experimental implementation of luSolve for any Fractional element type, including Mod n I and Mod n Z.

>>> let a = (2><2) [1,2,3,5] :: Matrix (Z ./. 13)
(2><2)
[ 1, 2
, 3, 5 ]
>>> b
(2><3)
[ 5, 1, 3
, 8, 6, 3 ]

>>> luSolve' (luPacked' a) b
(2><3)
[ 4,  7, 4
, 7, 10, 6 ]


luPacked' :: (Num (Vector t), Normed (Vector t), Container Vector t, Fractional t) => Matrix t -> LU t Source #

Experimental implementation of luPacked for any Fractional element type, including Mod n I and Mod n Z.

>>> let m = ident 5 + (5><5) [0..] :: Matrix (Z ./. 17)
(5><5)
[  1,  1,  2,  3,  4
,  5,  7,  7,  8,  9
, 10, 11, 13, 13, 14
, 15, 16,  0,  2,  2
,  3,  4,  5,  6,  8 ]

>>> let (l,u,p,s) = luFact $luPacked' m >>> l (5><5) [ 1, 0, 0, 0, 0 , 6, 1, 0, 0, 0 , 12, 7, 1, 0, 0 , 7, 10, 7, 1, 0 , 8, 2, 6, 11, 1 ] >>> u (5><5) [ 15, 16, 0, 2, 2 , 0, 13, 7, 13, 14 , 0, 0, 15, 0, 11 , 0, 0, 0, 15, 15 , 0, 0, 0, 0, 1 ]  ## Symmetric indefinite ldlSolve :: Field t => LDL t -> Matrix t -> Matrix t Source # Solution of a linear system (for several right hand sides) from a precomputed LDL factorization obtained by ldlPacked. Note: this can be slower than the general solver based on the LU decomposition. ldlPacked :: Field t => Herm t -> LDL t Source # Obtains the LDL decomposition of a matrix in a compact data structure suitable for ldlSolve. ## Positive definite Arguments  :: Field t => Matrix t Cholesky decomposition of the coefficient matrix -> Matrix t right hand sides -> Matrix t solution Solve a symmetric or Hermitian positive definite linear system using a precomputed Cholesky decomposition obtained by chol. ## Triangular data UpLo Source # Constructors  Lower Upper Arguments  :: Field t => UpLo Lower or Upper -> Matrix t coefficient matrix -> Matrix t right hand sides -> Matrix t solution Solve a triangular linear system. If Upper is specified then all elements below the diagonal are ignored; if Lower is specified then all elements above the diagonal are ignored. ## Tridiagonal Arguments  :: Field t => Vector t lower diagonal: $$n - 1$$ elements -> Vector t diagonal: $$n$$ elements -> Vector t upper diagonal: $$n - 1$$ elements -> Matrix t right hand sides -> Matrix t solution Solve a tridiagonal linear system. Suppose you wish to solve $$Ax = b$$ where $A = \begin{bmatrix} 1.0 & 4.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 \\ 3.0 & 1.0 & 4.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 \\ 0.0 & 3.0 & 1.0 & 4.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 \\ 0.0 & 0.0 & 3.0 & 1.0 & 4.0 & 0.0 & 0.0 & 0.0 & 0.0 \\ 0.0 & 0.0 & 0.0 & 3.0 & 1.0 & 4.0 & 0.0 & 0.0 & 0.0 \\ 0.0 & 0.0 & 0.0 & 0.0 & 3.0 & 1.0 & 4.0 & 0.0 & 0.0 \\ 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 3.0 & 1.0 & 4.0 & 0.0 \\ 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 3.0 & 1.0 & 4.0 \\ 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 3.0 & 1.0 \end{bmatrix} \quad b = \begin{bmatrix} 1.0 & 1.0 & 1.0 \\ 1.0 & -1.0 & 2.0 \\ 1.0 & 1.0 & 3.0 \\ 1.0 & -1.0 & 4.0 \\ 1.0 & 1.0 & 5.0 \\ 1.0 & -1.0 & 6.0 \\ 1.0 & 1.0 & 7.0 \\ 1.0 & -1.0 & 8.0 \\ 1.0 & 1.0 & 9.0 \end{bmatrix}$ then dL = fromList [3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0] d = fromList [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0] dU = fromList [4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0] b = (9><3) [ 1.0, 1.0, 1.0, 1.0, -1.0, 2.0, 1.0, 1.0, 3.0, 1.0, -1.0, 4.0, 1.0, 1.0, 5.0, 1.0, -1.0, 6.0, 1.0, 1.0, 7.0, 1.0, -1.0, 8.0, 1.0, 1.0, 9.0 ] x = triDiagSolve dL d dU b  ## Sparse Arguments  :: Bool is symmetric -> GMatrix coefficient matrix -> Vector R right-hand side -> Vector R solution Solve a sparse linear system using the conjugate gradient method with default parameters. Arguments  :: Bool symmetric -> R relative tolerance for the residual (e.g. 1E-4) -> R relative tolerance for δx (e.g. 1E-3) -> Int maximum number of iterations -> GMatrix coefficient matrix -> Vector R initial solution -> Vector R right-hand side -> [CGState] solution Solve a sparse linear system using the conjugate gradient method with default parameters. # Inverse and pseudoinverse inv :: Field t => Matrix t -> Matrix t Source # Inverse of a square matrix. See also invlndet. pinv :: Field t => Matrix t -> Matrix t Source # Pseudoinverse of a general matrix with default tolerance (pinvTol 1, similar to GNU-Octave). pinvTol :: Field t => Double -> Matrix t -> Matrix t Source # pinvTol r computes the pseudoinverse of a matrix with tolerance tol=r*g*eps*(max rows cols), where g is the greatest singular value. m = (3><3) [ 1, 0, 0 , 0, 1, 0 , 0, 0, 1e-10] :: Matrix Double  >>> pinv m 1. 0. 0. 0. 1. 0. 0. 0. 10000000000.  >>> pinvTol 1E8 m 1. 0. 0. 0. 1. 0. 0. 0. 1.  # Determinant and rank rcond :: Field t => Matrix t -> Double Source # Reciprocal of the 2-norm condition number of a matrix, computed from the singular values. rank :: Field t => Matrix t -> Int Source # Number of linearly independent rows or columns. See also ranksv det :: Field t => Matrix t -> t Source # Determinant of a square matrix. To avoid possible overflow or underflow use invlndet. Arguments  :: Field t => Matrix t -> (Matrix t, (t, t)) (inverse, (log abs det, sign or phase of det)) Joint computation of inverse and logarithm of determinant of a square matrix. # Norms class Normed a where Source # p-norm for vectors, operator norm for matrices Minimal complete definition Methods norm_0 :: a -> R Source # norm_1 :: a -> R Source # norm_2 :: a -> R Source # norm_Inf :: a -> R Source # Instances  Source # Methods Source # Methods Source # Methods Source # Methods Source # Methods Source # Methods KnownNat m => Normed (Vector (Mod m Z)) Source # Methodsnorm_0 :: Vector (Mod m Z) -> R Source #norm_1 :: Vector (Mod m Z) -> R Source #norm_2 :: Vector (Mod m Z) -> R Source #norm_Inf :: Vector (Mod m Z) -> R Source # KnownNat m => Normed (Vector (Mod m I)) Source # Methodsnorm_0 :: Vector (Mod m I) -> R Source #norm_1 :: Vector (Mod m I) -> R Source #norm_2 :: Vector (Mod m I) -> R Source #norm_Inf :: Vector (Mod m I) -> R Source # Source # Methods Source # Methods norm_Frob :: (Normed (Vector t), Element t) => Matrix t -> R Source # Frobenius norm (Schatten p-norm with p=2) norm_nuclear :: Field t => Matrix t -> R Source # Sum of singular values (Schatten p-norm with p=1) # Nullspace and range orth :: Field t => Matrix t -> Matrix t Source # return an orthonormal basis of the range space of a matrix. See also orthSVD. nullspace :: Field t => Matrix t -> Matrix t Source # return an orthonormal basis of the null space of a matrix. See also nullspaceSVD. solution of overconstrained homogeneous linear system solution of overconstrained homogeneous symmetric linear system # Singular value decomposition svd :: Field t => Matrix t -> (Matrix t, Vector Double, Matrix t) Source # Full singular value decomposition. a = (5><3) [ 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 ] :: Matrix Double  >>> let (u,s,v) = svd a  >>> disp 3 u 5x5 -0.101 0.768 0.614 0.028 -0.149 -0.249 0.488 -0.503 0.172 0.646 -0.396 0.208 -0.405 -0.660 -0.449 -0.543 -0.072 -0.140 0.693 -0.447 -0.690 -0.352 0.433 -0.233 0.398  >>> s fromList [35.18264833189422,1.4769076999800903,1.089145439970417e-15]  >>> disp 3 v 3x3 -0.519 -0.751 0.408 -0.576 -0.046 -0.816 -0.632 0.659 0.408  >>> let d = diagRect 0 s 5 3 >>> disp 3 d 5x3 35.183 0.000 0.000 0.000 1.477 0.000 0.000 0.000 0.000 0.000 0.000 0.000  >>> disp 3$ u <> d <> tr v
5x3
1.000   2.000   3.000
4.000   5.000   6.000
7.000   8.000   9.000
10.000  11.000  12.000
13.000  14.000  15.000


thinSVD :: Field t => Matrix t -> (Matrix t, Vector Double, Matrix t) Source #

A version of svd which returns only the min (rows m) (cols m) singular vectors of m.

If (u,s,v) = thinSVD m then m == u <> diag s <> tr v.

a = (5><3)
[  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 ] :: Matrix Double

>>> let (u,s,v) = thinSVD a

>>> disp 3 u
5x3
-0.101   0.768   0.614
-0.249   0.488  -0.503
-0.396   0.208  -0.405
-0.543  -0.072  -0.140
-0.690  -0.352   0.433

>>> s
fromList [35.18264833189422,1.4769076999800903,1.089145439970417e-15]

>>> disp 3 v
3x3
-0.519  -0.751   0.408
-0.576  -0.046  -0.816
-0.632   0.659   0.408

>>> disp 3 $u <> diag s <> tr v 5x3 1.000 2.000 3.000 4.000 5.000 6.000 7.000 8.000 9.000 10.000 11.000 12.000 13.000 14.000 15.000  compactSVD :: Field t => Matrix t -> (Matrix t, Vector Double, Matrix t) Source # Similar to thinSVD, returning only the nonzero singular values and the corresponding singular vectors. a = (5><3) [ 1.0, 2.0, 3.0 , 4.0, 5.0, 6.0 , 7.0, 8.0, 9.0 , 10.0, 11.0, 12.0 , 13.0, 14.0, 15.0 ] :: Matrix Double  >>> let (u,s,v) = compactSVD a  >>> disp 3 u 5x2 -0.101 0.768 -0.249 0.488 -0.396 0.208 -0.543 -0.072 -0.690 -0.352  >>> s fromList [35.18264833189422,1.4769076999800903]  >>> disp 3 u 5x2 -0.101 0.768 -0.249 0.488 -0.396 0.208 -0.543 -0.072 -0.690 -0.352  >>> disp 3$ u <> diag s <> tr v
5x3
1.000   2.000   3.000
4.000   5.000   6.000
7.000   8.000   9.000
10.000  11.000  12.000
13.000  14.000  15.000


compactSVDTol :: Field t => Double -> Matrix t -> (Matrix t, Vector Double, Matrix t) Source #

compactSVDTol r is similar to compactSVD (for which r=1), but uses tolerance tol=r*g*eps*(max rows cols) to distinguish nonzero singular values, where g is the greatest singular value. If g<r*eps, then only one singular value is returned.

Singular values only.

leftSV :: Field t => Matrix t -> (Matrix t, Vector Double) Source #

Singular values and all left singular vectors (as columns).

rightSV :: Field t => Matrix t -> (Vector Double, Matrix t) Source #

Singular values and all right singular vectors (as columns).

# Eigendecomposition

eig :: Field t => Matrix t -> (Vector (Complex Double), Matrix (Complex Double)) Source #

Eigenvalues (not ordered) and eigenvectors (as columns) of a general square matrix.

If (s,v) = eig m then m <> v == v <> diag s

a = (3><3)
[ 3, 0, -2
, 4, 5, -1
, 3, 1,  0 ] :: Matrix Double

>>> let (l, v) = eig a

>>> putStr . dispcf 3 . asRow $l 1x3 1.925+1.523i 1.925-1.523i 4.151  >>> putStr . dispcf 3$ v
3x3
-0.455+0.365i  -0.455-0.365i   0.181
0.603          0.603  -0.978
0.033+0.543i   0.033-0.543i  -0.104

>>> putStr . dispcf 3 $complex a <> v 3x3 -1.432+0.010i -1.432-0.010i 0.753 1.160+0.918i 1.160-0.918i -4.059 -0.763+1.096i -0.763-1.096i -0.433  >>> putStr . dispcf 3$ v <> diag l
3x3
-1.432+0.010i  -1.432-0.010i   0.753
1.160+0.918i   1.160-0.918i  -4.059
-0.763+1.096i  -0.763-1.096i  -0.433


eigSH :: Field t => Herm t -> (Vector Double, Matrix t) Source #

Eigenvalues and eigenvectors (as columns) of a complex hermitian or real symmetric matrix, in descending order.

If (s,v) = eigSH m then m == v <> diag s <> tr v

a = (3><3)
[ 1.0, 2.0, 3.0
, 2.0, 4.0, 5.0
, 3.0, 5.0, 6.0 ]

>>> let (l, v) = eigSH a

>>> l
fromList [11.344814282762075,0.17091518882717918,-0.5157294715892575]

>>> disp 3 $v <> diag l <> tr v 3x3 1.000 2.000 3.000 2.000 4.000 5.000 3.000 5.000 6.000  eigenvalues :: Field t => Matrix t -> Vector (Complex Double) Source # Eigenvalues (not ordered) of a general square matrix. Eigenvalues (in descending order) of a complex hermitian or real symmetric matrix. Arguments  :: Field t => Herm t A -> Herm t B -> (Vector Double, Matrix t) Generalized symmetric positive definite eigensystem Av = lBv, for A and B symmetric, B positive definite. # QR qr :: Field t => Matrix t -> (Matrix t, Matrix t) Source # QR factorization. If (q,r) = qr m then m == q <> r, where q is unitary and r is upper triangular. Note: the current implementation is very slow for large matrices. thinQR is much faster. thinQR :: Field t => Matrix t -> (Matrix t, Matrix t) Source # A version of qr which returns only the min (rows m) (cols m) columns of q and rows of r. rq :: Field t => Matrix t -> (Matrix t, Matrix t) Source # RQ factorization. If (r,q) = rq m then m == r <> q, where q is unitary and r is upper triangular. Note: the current implementation is very slow for large matrices. thinRQ is much faster. thinRQ :: Field t => Matrix t -> (Matrix t, Matrix t) Source # A version of rq which returns only the min (rows m) (cols m) columns of r and rows of q. qrRaw :: Field t => Matrix t -> QR t Source # Compute the QR decomposition of a matrix in compact form. qrgr :: Field t => Int -> QR t -> Matrix t Source # generate a matrix with k orthogonal columns from the compact QR decomposition obtained by qrRaw. # Cholesky chol :: Field t => Herm t -> Matrix t Source # Cholesky factorization of a positive definite hermitian or symmetric matrix. If c = chol m then c is upper triangular and m == tr c <> c. mbChol :: Field t => Herm t -> Maybe (Matrix t) Source # Similar to chol, but instead of an error (e.g., caused by a matrix not positive definite) it returns Nothing. # LU lu :: Field t => Matrix t -> (Matrix t, Matrix t, Matrix t, t) Source # Explicit LU factorization of a general matrix. If (l,u,p,s) = lu m then m == p <> l <> u, where l is lower triangular, u is upper triangular, p is a permutation matrix and s is the signature of the permutation. luFact :: Numeric t => LU t -> (Matrix t, Matrix t, Matrix t, t) Source # Compute the explicit LU decomposition from the compact one obtained by luPacked. # Hessenberg hess :: Field t => Matrix t -> (Matrix t, Matrix t) Source # Hessenberg factorization. If (p,h) = hess m then m == p <> h <> tr p, where p is unitary and h is in upper Hessenberg form (it has zero entries below the first subdiagonal). # Schur schur :: Field t => Matrix t -> (Matrix t, Matrix t) Source # Schur factorization. If (u,s) = schur m then m == u <> s <> tr u, where u is unitary and s is a Shur matrix. A complex Schur matrix is upper triangular. A real Schur matrix is upper triangular in 2x2 blocks. "Anything that the Jordan decomposition can do, the Schur decomposition can do better!" (Van Loan) # Matrix functions expm :: Field t => Matrix t -> Matrix t Source # Matrix exponential. It uses a direct translation of Algorithm 11.3.1 in Golub & Van Loan, based on a scaled Pade approximation. sqrtm :: Field t => Matrix t -> Matrix t Source # Matrix square root. Currently it uses a simple iterative algorithm described in Wikipedia. It only works with invertible matrices that have a real solution. m = (2><2) [4,9 ,0,4] :: Matrix Double >>> sqrtm m (2><2) [ 2.0, 2.25 , 0.0, 2.0 ]  For diagonalizable matrices you can try matFunc sqrt: >>> matFunc sqrt ((2><2) [1,0,0,-1]) (2><2) [ 1.0 :+ 0.0, 0.0 :+ 0.0 , 0.0 :+ 0.0, 0.0 :+ 1.0 ]  Generic matrix functions for diagonalizable matrices. For instance: logm = matFunc log # Correlation and convolution Arguments  :: (Container Vector t, Product t) => Vector t kernel -> Vector t source -> Vector t correlation >>> corr (fromList[1,2,3]) (fromList [1..10]) fromList [14.0,20.0,26.0,32.0,38.0,44.0,50.0,56.0]  conv :: (Container Vector t, Product t, Num t) => Vector t -> Vector t -> Vector t Source # convolution (corr with reversed kernel and padded input, equivalent to polynomial product) >>> conv (fromList[1,1]) (fromList [-1,1]) fromList [-1.0,0.0,1.0]  corrMin :: (Container Vector t, RealElement t, Product t) => Vector t -> Vector t -> Vector t Source # similar to corr, using min instead of (*) corr2 :: Product a => Matrix a -> Matrix a -> Matrix a Source # 2D correlation (without padding) >>> disp 5$ corr2 (konst 1 (3,3)) (ident 10 :: Matrix Double)
8x8
3  2  1  0  0  0  0  0
2  3  2  1  0  0  0  0
1  2  3  2  1  0  0  0
0  1  2  3  2  1  0  0
0  0  1  2  3  2  1  0
0  0  0  1  2  3  2  1
0  0  0  0  1  2  3  2
0  0  0  0  0  1  2  3


Arguments

 :: (Num (Matrix a), Product a, Container Vector a) => Matrix a kernel -> Matrix a -> Matrix a

2D convolution

>>> disp 5 $conv2 (konst 1 (3,3)) (ident 10 :: Matrix Double) 12x12 1 1 1 0 0 0 0 0 0 0 0 0 1 2 2 1 0 0 0 0 0 0 0 0 1 2 3 2 1 0 0 0 0 0 0 0 0 1 2 3 2 1 0 0 0 0 0 0 0 0 1 2 3 2 1 0 0 0 0 0 0 0 0 1 2 3 2 1 0 0 0 0 0 0 0 0 1 2 3 2 1 0 0 0 0 0 0 0 0 1 2 3 2 1 0 0 0 0 0 0 0 0 1 2 3 2 1 0 0 0 0 0 0 0 0 1 2 3 2 1 0 0 0 0 0 0 0 0 1 2 2 1 0 0 0 0 0 0 0 0 0 1 1 1  # Random arrays type Seed = Int Source # data RandDist Source # Constructors  Uniform uniform distribution in [0,1) Gaussian normal distribution with mean zero and standard deviation one Instances  Source # MethodsenumFrom :: RandDist -> [RandDist] #enumFromTo :: RandDist -> RandDist -> [RandDist] # Arguments  :: Seed -> RandDist distribution -> Int vector size -> Vector Double Obtains a vector of pseudorandom elements (use randomIO to get a random seed). rand :: Int -> Int -> IO (Matrix Double) Source # pseudorandom matrix with uniform elements between 0 and 1 randn :: Int -> Int -> IO (Matrix Double) Source # pseudorandom matrix with normal elements >>> disp 3 =<< randn 3 5 3x5 0.386 -1.141 0.491 -0.510 1.512 0.069 -0.919 1.022 -0.181 0.745 0.313 -0.670 -0.097 -1.575 -0.583  Arguments  :: Seed -> Int number of rows -> Vector Double mean vector -> Herm Double covariance matrix -> Matrix Double result Obtains a matrix whose rows are pseudorandom samples from a multivariate Gaussian distribution. Arguments  :: Seed -> Int number of rows -> [(Double, Double)] ranges for each column -> Matrix Double result Obtains a matrix whose rows are pseudorandom samples from a multivariate uniform distribution. # Misc Compute mean vector and covariance matrix of the rows of a matrix. >>> meanCov$ gaussianSample 666 1000 (fromList[4,5]) (diagl[2,3])
(fromList [4.010341078059521,5.0197204699640405],
(2><2)
[     1.9862461923890056, -1.0127225830525157e-2
, -1.0127225830525157e-2,     3.0373954915729318 ])


outer products of rows

>>> a
(3><2)
[   1.0,   2.0
,  10.0,  20.0
, 100.0, 200.0 ]
>>> b
(3><3)
[ 1.0, 2.0, 3.0
, 4.0, 5.0, 6.0
, 7.0, 8.0, 9.0 ]

>>> rowOuters a (b ||| 1)
(3><8)
[   1.0,   2.0,   3.0,   1.0,    2.0,    4.0,    6.0,   2.0
,  40.0,  50.0,  60.0,  10.0,   80.0,  100.0,  120.0,  20.0
, 700.0, 800.0, 900.0, 100.0, 1400.0, 1600.0, 1800.0, 200.0 ]


Matrix of pairwise squared distances of row vectors (using the matrix product trick in blog.smola.org)

normalize :: (Normed (Vector t), Num (Vector t), Field t) => Vector t -> Vector t Source #

Obtains a vector in the same direction with 2-norm=1

peps :: RealFloat x => x Source #

1 + 0.5*peps == 1, 1 + 0.6*peps /= 1

relativeError :: Num a => (a -> Double) -> a -> a -> Double Source #

magnit :: (Element t, Normed (Vector t)) => R -> t -> Bool Source #

Check if the absolute value or complex magnitude is greater than a given threshold

>>> magnit 1E-6 (1E-12 :: R)
False
>>> magnit 1E-6 (3+iC :: C)
True
>>> magnit 0 (3 :: I ./. 5)
True


haussholder :: Field a => a -> Vector a -> Matrix a Source #

udot :: Product e => Vector e -> Vector e -> e Source #

unconjugated dot product

Arguments

 :: Field t => Either Double Int Left "numeric" zero (eg. 1*eps), or Right "theoretical" matrix rank. -> Matrix t input matrix m -> (Vector Double, Matrix t) rightSV of m -> Matrix t nullspace

The nullspace of a matrix from its precomputed SVD decomposition.

Arguments

 :: Field t => Either Double Int Left "numeric" zero (eg. 1*eps), or Right "theoretical" matrix rank. -> Matrix t input matrix m -> (Matrix t, Vector Double) leftSV of m -> Matrix t orth

The range space a matrix from its precomputed SVD decomposition.

Arguments

 :: Double numeric zero (e.g. 1*eps) -> Int maximum dimension of the matrix -> [Double] singular values -> Int rank of m

Numeric rank of a matrix from its singular values.

imaginary unit

sym :: Field t => Matrix t -> Herm t Source #

Compute the complex Hermitian or real symmetric part of a square matrix ((x + tr x)/2).

mTm :: Numeric t => Matrix t -> Herm t Source #

Compute the contraction tr x <> x of a general matrix.

trustSym :: Matrix t -> Herm t Source #

At your own risk, declare that a matrix is complex Hermitian or real symmetric for usage in chol, eigSH, etc. Only a triangular part of the matrix will be used.

unSym :: Herm t -> Matrix t Source #

Extract the general matrix from a Herm structure, forgetting its symmetric or Hermitian property.

# Auxiliary classes

class Storable a => Element a Source #

Supported matrix elements.

Minimal complete definition

constantD, extractR, setRect, sortI, sortV, compareV, selectV, remapM, rowOp, gemm, reorderV

Instances

 Source # MethodssetRect :: Int -> Int -> Matrix Double -> Matrix Double -> IO ()rowOp :: Int -> Double -> Int -> Int -> Int -> Int -> Matrix Double -> IO () Source # MethodssetRect :: Int -> Int -> Matrix Float -> Matrix Float -> IO ()rowOp :: Int -> Float -> Int -> Int -> Int -> Int -> Matrix Float -> IO () Source # MethodssetRect :: Int -> Int -> Matrix CInt -> Matrix CInt -> IO ()rowOp :: Int -> CInt -> Int -> Int -> Int -> Int -> Matrix CInt -> IO ()gemm :: Vector CInt -> Matrix CInt -> Matrix CInt -> Matrix CInt -> IO () Source # MethodsconstantD :: Z -> Int -> Vector ZextractR :: MatrixOrder -> Matrix Z -> CInt -> Vector CInt -> CInt -> Vector CInt -> IO (Matrix Z)setRect :: Int -> Int -> Matrix Z -> Matrix Z -> IO ()rowOp :: Int -> Z -> Int -> Int -> Int -> Int -> Matrix Z -> IO ()gemm :: Vector Z -> Matrix Z -> Matrix Z -> Matrix Z -> IO () Source # MethodssetRect :: Int -> Int -> Matrix (Complex Double) -> Matrix (Complex Double) -> IO ()rowOp :: Int -> Complex Double -> Int -> Int -> Int -> Int -> Matrix (Complex Double) -> IO ()gemm :: Vector (Complex Double) -> Matrix (Complex Double) -> Matrix (Complex Double) -> Matrix (Complex Double) -> IO () Source # MethodssetRect :: Int -> Int -> Matrix (Complex Float) -> Matrix (Complex Float) -> IO ()rowOp :: Int -> Complex Float -> Int -> Int -> Int -> Int -> Matrix (Complex Float) -> IO ()gemm :: Vector (Complex Float) -> Matrix (Complex Float) -> Matrix (Complex Float) -> Matrix (Complex Float) -> IO () KnownNat m => Element (Mod m Z) Source # MethodsconstantD :: Mod m Z -> Int -> Vector (Mod m Z)extractR :: MatrixOrder -> Matrix (Mod m Z) -> CInt -> Vector CInt -> CInt -> Vector CInt -> IO (Matrix (Mod m Z))setRect :: Int -> Int -> Matrix (Mod m Z) -> Matrix (Mod m Z) -> IO ()sortI :: Vector (Mod m Z) -> Vector CIntsortV :: Vector (Mod m Z) -> Vector (Mod m Z)compareV :: Vector (Mod m Z) -> Vector (Mod m Z) -> Vector CIntselectV :: Vector CInt -> Vector (Mod m Z) -> Vector (Mod m Z) -> Vector (Mod m Z) -> Vector (Mod m Z)remapM :: Matrix CInt -> Matrix CInt -> Matrix (Mod m Z) -> Matrix (Mod m Z)rowOp :: Int -> Mod m Z -> Int -> Int -> Int -> Int -> Matrix (Mod m Z) -> IO ()gemm :: Vector (Mod m Z) -> Matrix (Mod m Z) -> Matrix (Mod m Z) -> Matrix (Mod m Z) -> IO ()reorderV :: Vector CInt -> Vector CInt -> Vector (Mod m Z) -> Vector (Mod m Z) KnownNat m => Element (Mod m I) Source # MethodsconstantD :: Mod m I -> Int -> Vector (Mod m I)extractR :: MatrixOrder -> Matrix (Mod m I) -> CInt -> Vector CInt -> CInt -> Vector CInt -> IO (Matrix (Mod m I))setRect :: Int -> Int -> Matrix (Mod m I) -> Matrix (Mod m I) -> IO ()sortI :: Vector (Mod m I) -> Vector CIntsortV :: Vector (Mod m I) -> Vector (Mod m I)compareV :: Vector (Mod m I) -> Vector (Mod m I) -> Vector CIntselectV :: Vector CInt -> Vector (Mod m I) -> Vector (Mod m I) -> Vector (Mod m I) -> Vector (Mod m I)remapM :: Matrix CInt -> Matrix CInt -> Matrix (Mod m I) -> Matrix (Mod m I)rowOp :: Int -> Mod m I -> Int -> Int -> Int -> Int -> Matrix (Mod m I) -> IO ()gemm :: Vector (Mod m I) -> Matrix (Mod m I) -> Matrix (Mod m I) -> Matrix (Mod m I) -> IO ()reorderV :: Vector CInt -> Vector CInt -> Vector (Mod m I) -> Vector (Mod m I)

class Element e => Container c e Source #

Basic element-by-element functions for numeric containers

Minimal complete definition

conj', size', scalar', scale', addConstant, add', sub, mul, equal, cmap', konst', build', atIndex', minIndex', maxIndex', minElement', maxElement', sumElements', prodElements', step', ccompare', cselect', find', assoc', accum', scaleRecip, divide, arctan2', cmod', fromInt', toInt', fromZ', toZ'

Instances

 Source # Methodscmap' :: Element b => (Double -> b) -> Vector Double -> Vector bbuild' :: IndexOf Vector -> ArgOf Vector Double -> Vector Doublefind' :: (Double -> Bool) -> Vector Double -> [IndexOf Vector]assoc' :: IndexOf Vector -> Double -> [(IndexOf Vector, Double)] -> Vector Doubleaccum' :: Vector Double -> (Double -> Double -> Double) -> [(IndexOf Vector, Double)] -> Vector Double Source # Methodscmap' :: Element b => (Float -> b) -> Vector Float -> Vector bbuild' :: IndexOf Vector -> ArgOf Vector Float -> Vector Floatfind' :: (Float -> Bool) -> Vector Float -> [IndexOf Vector]assoc' :: IndexOf Vector -> Float -> [(IndexOf Vector, Float)] -> Vector Floataccum' :: Vector Float -> (Float -> Float -> Float) -> [(IndexOf Vector, Float)] -> Vector Float Source # Methodsscalar' :: Z -> Vector Zscale' :: Z -> Vector Z -> Vector Zsub :: Vector Z -> Vector Z -> Vector Zmul :: Vector Z -> Vector Z -> Vector Zequal :: Vector Z -> Vector Z -> Boolcmap' :: Element b => (Z -> b) -> Vector Z -> Vector bbuild' :: IndexOf Vector -> ArgOf Vector Z -> Vector Zfind' :: (Z -> Bool) -> Vector Z -> [IndexOf Vector]assoc' :: IndexOf Vector -> Z -> [(IndexOf Vector, Z)] -> Vector Zaccum' :: Vector Z -> (Z -> Z -> Z) -> [(IndexOf Vector, Z)] -> Vector Zcmod' :: Z -> Vector Z -> Vector Z Source # Methodsscalar' :: I -> Vector Iscale' :: I -> Vector I -> Vector Isub :: Vector I -> Vector I -> Vector Imul :: Vector I -> Vector I -> Vector Iequal :: Vector I -> Vector I -> Boolcmap' :: Element b => (I -> b) -> Vector I -> Vector bbuild' :: IndexOf Vector -> ArgOf Vector I -> Vector Ifind' :: (I -> Bool) -> Vector I -> [IndexOf Vector]assoc' :: IndexOf Vector -> I -> [(IndexOf Vector, I)] -> Vector Iaccum' :: Vector I -> (I -> I -> I) -> [(IndexOf Vector, I)] -> Vector Icmod' :: I -> Vector I -> Vector I (Num a, Element a, Container Vector a) => Container Matrix a Source # Methodsconj' :: Matrix a -> Matrix asize' :: Matrix a -> IndexOf Matrixscalar' :: a -> Matrix ascale' :: a -> Matrix a -> Matrix aaddConstant :: a -> Matrix a -> Matrix aadd' :: Matrix a -> Matrix a -> Matrix asub :: Matrix a -> Matrix a -> Matrix amul :: Matrix a -> Matrix a -> Matrix aequal :: Matrix a -> Matrix a -> Boolcmap' :: Element b => (a -> b) -> Matrix a -> Matrix bkonst' :: a -> IndexOf Matrix -> Matrix abuild' :: IndexOf Matrix -> ArgOf Matrix a -> Matrix aatIndex' :: Matrix a -> IndexOf Matrix -> aminElement' :: Matrix a -> amaxElement' :: Matrix a -> asumElements' :: Matrix a -> aprodElements' :: Matrix a -> astep' :: Matrix a -> Matrix accompare' :: Matrix a -> Matrix a -> Matrix Icselect' :: Matrix I -> Matrix a -> Matrix a -> Matrix a -> Matrix afind' :: (a -> Bool) -> Matrix a -> [IndexOf Matrix]assoc' :: IndexOf Matrix -> a -> [(IndexOf Matrix, a)] -> Matrix aaccum' :: Matrix a -> (a -> a -> a) -> [(IndexOf Matrix, a)] -> Matrix ascaleRecip :: a -> Matrix a -> Matrix adivide :: Matrix a -> Matrix a -> Matrix aarctan2' :: Matrix a -> Matrix a -> Matrix acmod' :: a -> Matrix a -> Matrix atoInt' :: Matrix a -> Matrix IfromZ' :: Matrix Z -> Matrix atoZ' :: Matrix a -> Matrix Z Source # Methodsequal :: Vector (Complex Double) -> Vector (Complex Double) -> Boolcmap' :: Element b => (Complex Double -> b) -> Vector (Complex Double) -> Vector bbuild' :: IndexOf Vector -> ArgOf Vector (Complex Double) -> Vector (Complex Double)find' :: (Complex Double -> Bool) -> Vector (Complex Double) -> [IndexOf Vector]accum' :: Vector (Complex Double) -> (Complex Double -> Complex Double -> Complex Double) -> [(IndexOf Vector, Complex Double)] -> Vector (Complex Double) Source # Methodsequal :: Vector (Complex Float) -> Vector (Complex Float) -> Boolcmap' :: Element b => (Complex Float -> b) -> Vector (Complex Float) -> Vector bbuild' :: IndexOf Vector -> ArgOf Vector (Complex Float) -> Vector (Complex Float)find' :: (Complex Float -> Bool) -> Vector (Complex Float) -> [IndexOf Vector]accum' :: Vector (Complex Float) -> (Complex Float -> Complex Float -> Complex Float) -> [(IndexOf Vector, Complex Float)] -> Vector (Complex Float) KnownNat m => Container Vector (Mod m Z) Source # Methodsconj' :: Vector (Mod m Z) -> Vector (Mod m Z)size' :: Vector (Mod m Z) -> IndexOf Vectorscalar' :: Mod m Z -> Vector (Mod m Z)scale' :: Mod m Z -> Vector (Mod m Z) -> Vector (Mod m Z)addConstant :: Mod m Z -> Vector (Mod m Z) -> Vector (Mod m Z)add' :: Vector (Mod m Z) -> Vector (Mod m Z) -> Vector (Mod m Z)sub :: Vector (Mod m Z) -> Vector (Mod m Z) -> Vector (Mod m Z)mul :: Vector (Mod m Z) -> Vector (Mod m Z) -> Vector (Mod m Z)equal :: Vector (Mod m Z) -> Vector (Mod m Z) -> Boolcmap' :: Element b => (Mod m Z -> b) -> Vector (Mod m Z) -> Vector bkonst' :: Mod m Z -> IndexOf Vector -> Vector (Mod m Z)build' :: IndexOf Vector -> ArgOf Vector (Mod m Z) -> Vector (Mod m Z)atIndex' :: Vector (Mod m Z) -> IndexOf Vector -> Mod m ZminIndex' :: Vector (Mod m Z) -> IndexOf VectormaxIndex' :: Vector (Mod m Z) -> IndexOf VectorminElement' :: Vector (Mod m Z) -> Mod m ZmaxElement' :: Vector (Mod m Z) -> Mod m ZsumElements' :: Vector (Mod m Z) -> Mod m ZprodElements' :: Vector (Mod m Z) -> Mod m Zstep' :: Vector (Mod m Z) -> Vector (Mod m Z)ccompare' :: Vector (Mod m Z) -> Vector (Mod m Z) -> Vector Icselect' :: Vector I -> Vector (Mod m Z) -> Vector (Mod m Z) -> Vector (Mod m Z) -> Vector (Mod m Z)find' :: (Mod m Z -> Bool) -> Vector (Mod m Z) -> [IndexOf Vector]assoc' :: IndexOf Vector -> Mod m Z -> [(IndexOf Vector, Mod m Z)] -> Vector (Mod m Z)accum' :: Vector (Mod m Z) -> (Mod m Z -> Mod m Z -> Mod m Z) -> [(IndexOf Vector, Mod m Z)] -> Vector (Mod m Z)scaleRecip :: Mod m Z -> Vector (Mod m Z) -> Vector (Mod m Z)divide :: Vector (Mod m Z) -> Vector (Mod m Z) -> Vector (Mod m Z)arctan2' :: Vector (Mod m Z) -> Vector (Mod m Z) -> Vector (Mod m Z)cmod' :: Mod m Z -> Vector (Mod m Z) -> Vector (Mod m Z)fromInt' :: Vector I -> Vector (Mod m Z)toInt' :: Vector (Mod m Z) -> Vector IfromZ' :: Vector Z -> Vector (Mod m Z)toZ' :: Vector (Mod m Z) -> Vector Z KnownNat m => Container Vector (Mod m I) Source # Methodsconj' :: Vector (Mod m I) -> Vector (Mod m I)size' :: Vector (Mod m I) -> IndexOf Vectorscalar' :: Mod m I -> Vector (Mod m I)scale' :: Mod m I -> Vector (Mod m I) -> Vector (Mod m I)addConstant :: Mod m I -> Vector (Mod m I) -> Vector (Mod m I)add' :: Vector (Mod m I) -> Vector (Mod m I) -> Vector (Mod m I)sub :: Vector (Mod m I) -> Vector (Mod m I) -> Vector (Mod m I)mul :: Vector (Mod m I) -> Vector (Mod m I) -> Vector (Mod m I)equal :: Vector (Mod m I) -> Vector (Mod m I) -> Boolcmap' :: Element b => (Mod m I -> b) -> Vector (Mod m I) -> Vector bkonst' :: Mod m I -> IndexOf Vector -> Vector (Mod m I)build' :: IndexOf Vector -> ArgOf Vector (Mod m I) -> Vector (Mod m I)atIndex' :: Vector (Mod m I) -> IndexOf Vector -> Mod m IminIndex' :: Vector (Mod m I) -> IndexOf VectormaxIndex' :: Vector (Mod m I) -> IndexOf VectorminElement' :: Vector (Mod m I) -> Mod m ImaxElement' :: Vector (Mod m I) -> Mod m IsumElements' :: Vector (Mod m I) -> Mod m IprodElements' :: Vector (Mod m I) -> Mod m Istep' :: Vector (Mod m I) -> Vector (Mod m I)ccompare' :: Vector (Mod m I) -> Vector (Mod m I) -> Vector Icselect' :: Vector I -> Vector (Mod m I) -> Vector (Mod m I) -> Vector (Mod m I) -> Vector (Mod m I)find' :: (Mod m I -> Bool) -> Vector (Mod m I) -> [IndexOf Vector]assoc' :: IndexOf Vector -> Mod m I -> [(IndexOf Vector, Mod m I)] -> Vector (Mod m I)accum' :: Vector (Mod m I) -> (Mod m I -> Mod m I -> Mod m I) -> [(IndexOf Vector, Mod m I)] -> Vector (Mod m I)scaleRecip :: Mod m I -> Vector (Mod m I) -> Vector (Mod m I)divide :: Vector (Mod m I) -> Vector (Mod m I) -> Vector (Mod m I)arctan2' :: Vector (Mod m I) -> Vector (Mod m I) -> Vector (Mod m I)cmod' :: Mod m I -> Vector (Mod m I) -> Vector (Mod m I)fromInt' :: Vector I -> Vector (Mod m I)toInt' :: Vector (Mod m I) -> Vector IfromZ' :: Vector Z -> Vector (Mod m I)toZ' :: Vector (Mod m I) -> Vector Z

class (Num e, Element e) => Product e Source #

Matrix product and related functions

Minimal complete definition

multiply, absSum, norm1, norm2, normInf

Instances

 Source # Methods Source # Methods Source # Methods Source # Methods Source # Methods Source # Methods KnownNat m => Product (Mod m Z) Source # Methodsmultiply :: Matrix (Mod m Z) -> Matrix (Mod m Z) -> Matrix (Mod m Z)absSum :: Vector (Mod m Z) -> RealOf (Mod m Z)norm1 :: Vector (Mod m Z) -> RealOf (Mod m Z)norm2 :: Vector (Mod m Z) -> RealOf (Mod m Z)normInf :: Vector (Mod m Z) -> RealOf (Mod m Z) KnownNat m => Product (Mod m I) Source # Methodsmultiply :: Matrix (Mod m I) -> Matrix (Mod m I) -> Matrix (Mod m I)absSum :: Vector (Mod m I) -> RealOf (Mod m I)norm1 :: Vector (Mod m I) -> RealOf (Mod m I)norm2 :: Vector (Mod m I) -> RealOf (Mod m I)normInf :: Vector (Mod m I) -> RealOf (Mod m I)

class (Container Vector t, Container Matrix t, Konst t Int Vector, Konst t (Int, Int) Matrix, CTrans t, Product t, Additive (Vector t), Additive (Matrix t), Linear t Vector, Linear t Matrix) => Numeric t Source #

Instances

 Source # Source # Source # Source # Source # Source # KnownNat m => Numeric (Mod m Z) Source # KnownNat m => Numeric (Mod m I) Source #

class LSDiv c Source #

Minimal complete definition

linSolve

Instances

 Source # MethodslinSolve :: Field t => Matrix t -> Vector t -> Vector t Source # MethodslinSolve :: Field t => Matrix t -> Matrix t -> Matrix t

data Herm t Source #

A matrix that, by construction, it is known to be complex Hermitian or real symmetric.

It can be created using sym, mTm, or trustSym, and the matrix can be extracted using unSym.

Instances

 Field t => Linear t Herm Source # Methodsscale :: t -> Herm t -> Herm t Source # (Element t, Show t) => Show (Herm t) Source # MethodsshowsPrec :: Int -> Herm t -> ShowS #show :: Herm t -> String #showList :: [Herm t] -> ShowS # (NFData t, Numeric t) => NFData (Herm t) Source # Methodsrnf :: Herm t -> () # Field t => Additive (Herm t) Source # Methodsadd :: Herm t -> Herm t -> Herm t Source #

class Complexable c Source #

Structures that may contain complex numbers

Minimal complete definition

toComplex', fromComplex', comp', single', double'

Instances

 Source # MethodstoComplex' :: RealElement e => (Vector e, Vector e) -> Vector (Complex e)fromComplex' :: RealElement e => Vector (Complex e) -> (Vector e, Vector e)comp' :: RealElement e => Vector e -> Vector (Complex e)single' :: Precision a b => Vector b -> Vector adouble' :: Precision a b => Vector a -> Vector b Source # MethodstoComplex' :: RealElement e => (Matrix e, Matrix e) -> Matrix (Complex e)fromComplex' :: RealElement e => Matrix (Complex e) -> (Matrix e, Matrix e)comp' :: RealElement e => Matrix e -> Matrix (Complex e)single' :: Precision a b => Matrix b -> Matrix adouble' :: Precision a b => Matrix a -> Matrix b

class (Element t, Element (Complex t), RealFloat t) => RealElement t Source #

Supported real types

Instances

 Source # Source #

type family RealOf x Source #

Instances

 type RealOf Double Source # type RealOf Double = Double type RealOf Float Source # type RealOf Float = Float type RealOf Z Source # type RealOf Z = Z type RealOf I Source # type RealOf I = I type RealOf (Complex Double) Source # type RealOf (Complex Double) = Double type RealOf (Complex Float) Source # type RealOf (Complex Float) = Float type RealOf (Mod n Z) Source # type RealOf (Mod n Z) = Z type RealOf (Mod n I) Source # type RealOf (Mod n I) = I

type family ComplexOf x Source #

Instances

 type ComplexOf Double Source # type ComplexOf Float Source # type ComplexOf (Complex Double) Source # type ComplexOf (Complex Float) Source #

type family SingleOf x Source #

Instances

 type SingleOf Double Source # type SingleOf Double = Float type SingleOf Float Source # type SingleOf Float = Float type SingleOf (Complex a) Source # type SingleOf (Complex a) = Complex (SingleOf a)

type family DoubleOf x Source #

Instances

 type DoubleOf Double Source # type DoubleOf Double = Double type DoubleOf Float Source # type DoubleOf Float = Double type DoubleOf (Complex a) Source # type DoubleOf (Complex a) = Complex (DoubleOf a)

type family IndexOf (c :: * -> *) Source #

Instances

 type IndexOf Vector Source # type IndexOf Vector = Int type IndexOf Matrix Source # type IndexOf Matrix = (Int, Int)

class (Numeric t, Convert t, Normed Matrix t, Normed Vector t, Floating t, Linear t Vector, Linear t Matrix, Additive (Vector t), Additive (Matrix t), RealOf t ~ Double) => Field t Source #

Generic linear algebra functions for double precision real and complex matrices.

(Single precision data can be converted using single and double).

Minimal complete definition

svd', thinSVD', sv', luPacked', luSolve', mbLinearSolve', linearSolve', cholSolve', triSolve', triDiagSolve', ldlPacked', ldlSolve', linearSolveSVD', linearSolveLS', eig', eigSH'', eigOnly, eigOnlySH, cholSH', mbCholSH', qr', qrgr', hess', schur'

Instances

 Source # MethodsluPacked' :: Matrix Double -> (Matrix Double, [Int])luSolve' :: (Matrix Double, [Int]) -> Matrix Double -> Matrix DoubleldlPacked' :: Matrix Double -> (Matrix Double, [Int])ldlSolve' :: (Matrix Double, [Int]) -> Matrix Double -> Matrix Doubleqrgr' :: Int -> (Matrix Double, Vector Double) -> Matrix Double Source # MethodsluPacked' :: Matrix (Complex Double) -> (Matrix (Complex Double), [Int])luSolve' :: (Matrix (Complex Double), [Int]) -> Matrix (Complex Double) -> Matrix (Complex Double)ldlPacked' :: Matrix (Complex Double) -> (Matrix (Complex Double), [Int])ldlSolve' :: (Matrix (Complex Double), [Int]) -> Matrix (Complex Double) -> Matrix (Complex Double)qr' :: Matrix (Complex Double) -> (Matrix (Complex Double), Vector (Complex Double))qrgr' :: Int -> (Matrix (Complex Double), Vector (Complex Double)) -> Matrix (Complex Double)

class Linear t c where Source #

Minimal complete definition

scale

Methods

scale :: t -> c t -> c t Source #

Instances

 Source # Methodsscale :: t -> Matrix t -> Matrix t Source # Source # Methodsscale :: t -> Vector t -> Vector t Source # Field t => Linear t Herm Source # Methodsscale :: t -> Herm t -> Herm t Source #

class Additive c where Source #

Minimal complete definition

Methods

add :: c -> c -> c Source #

Instances

 Source # Methodsadd :: Vector t -> Vector t -> Vector t Source # Source # Methodsadd :: Matrix t -> Matrix t -> Matrix t Source # Field t => Additive (Herm t) Source # Methodsadd :: Herm t -> Herm t -> Herm t Source # Additive (C n) Source # Methodsadd :: C n -> C n -> C n Source # Additive (R n) Source # Methodsadd :: R n -> R n -> R n Source # KnownNat n => Additive (Sym n) Source # Methodsadd :: Sym n -> Sym n -> Sym n Source # (KnownNat m, KnownNat n) => Additive (M m n) Source # Methodsadd :: M m n -> M m n -> M m n Source # (KnownNat m, KnownNat n) => Additive (L m n) Source # Methodsadd :: L m n -> L m n -> L m n Source #

class Transposable m mt | m -> mt, mt -> m where Source #

Minimal complete definition

Methods

tr :: m -> mt Source #

conjugate transpose

tr' :: m -> mt Source #

transpose

Instances

 Source # Methods (CTrans t, Container Vector t) => Transposable (Matrix t) (Matrix t) Source # Methodstr :: Matrix t -> Matrix t Source #tr' :: Matrix t -> Matrix t Source # KnownNat n => Transposable (Her n) (Her n) Source # Methodstr :: Her n -> Her n Source #tr' :: Her n -> Her n Source # KnownNat n => Transposable (Sym n) (Sym n) Source # Methodstr :: Sym n -> Sym n Source #tr' :: Sym n -> Sym n Source # (KnownNat n, KnownNat m) => Transposable (M m n) (M n m) Source # Methodstr :: M m n -> M n m Source #tr' :: M m n -> M n m Source # (KnownNat n, KnownNat m) => Transposable (L m n) (L n m) Source # Methodstr :: L m n -> L n m Source #tr' :: L m n -> L n m Source #

data LU t Source #

LU decomposition of a matrix in a compact format.

Constructors

 LU (Matrix t) [Int]

Instances

 (Element t, Show t) => Show (LU t) Source # MethodsshowsPrec :: Int -> LU t -> ShowS #show :: LU t -> String #showList :: [LU t] -> ShowS # (NFData t, Numeric t) => NFData (LU t) Source # Methodsrnf :: LU t -> () #

data LDL t Source #

LDL decomposition of a complex Hermitian or real symmetric matrix in a compact format.

Constructors

 LDL (Matrix t) [Int]

Instances

 (Element t, Show t) => Show (LDL t) Source # MethodsshowsPrec :: Int -> LDL t -> ShowS #show :: LDL t -> String #showList :: [LDL t] -> ShowS # (NFData t, Numeric t) => NFData (LDL t) Source # Methodsrnf :: LDL t -> () #

data QR t Source #

QR decomposition of a matrix in compact form. (The orthogonal matrix is not explicitly formed.)

Constructors

 QR (Matrix t) (Vector t)

Instances

 (NFData t, Numeric t) => NFData (QR t) Source # Methodsrnf :: QR t -> () #

data CGState Source #

Constructors

 CGState Fieldscgp :: Vector Rconjugate gradientcgr :: Vector Rresidualcgr2 :: Rsquared norm of residualcgx :: Vector Rcurrent solutioncgdx :: Rnormalized size of correction

class Testable t where Source #

Minimal complete definition

checkT

Methods

checkT :: t -> (Bool, IO ()) Source #

ioCheckT :: t -> IO (Bool, IO ()) Source #

Instances

 KnownNat m => Testable (Matrix (Mod m I)) Source # MethodscheckT :: Matrix (Mod m I) -> (Bool, IO ()) Source #ioCheckT :: Matrix (Mod m I) -> IO (Bool, IO ()) Source #