hmatrix-0.17.0.1: Numeric Linear Algebra

Numeric.LinearAlgebra.Data

Description

This module provides functions for creation and manipulation of vectors and matrices, IO, and other utilities.

Synopsis

# Elements

type R = Double Source

type I = CInt Source

type Z = Int64 Source

type (./.) x n = Mod n x infixr 5 Source

# Vector

1D arrays are storable vectors directly reexported from the vector package.

fromList :: Storable a => [a] -> Vector a

toList :: Storable a => Vector a -> [a] Source

(|>) :: Storable a => Int -> [a] -> Vector a infixl 9 Source

Create a vector from a list of elements and explicit dimension. The input list is truncated if it is too long, so it may safely be used, for instance, with infinite lists.

>>> 5 |> [1..]
fromList [1.0,2.0,3.0,4.0,5.0]


vector :: [R] -> Vector R Source

Create a real vector.

>>> vector [1..5]
fromList [1.0,2.0,3.0,4.0,5.0]

>>> range 5
fromList [0,1,2,3,4]


idxs :: [Int] -> Vector I Source

Create a vector of indexes, useful for matrix extraction using '(??)'

# Matrix

The main data type of hmatrix is a 2D dense array defined on top of a storable vector. The internal representation is suitable for direct interface with standard numeric libraries.

(><) :: Storable a => Int -> Int -> [a] -> Matrix a Source

Create a matrix from a list of elements

>>> (2><3) [2, 4, 7+2*iC,   -3, 11, 0]
(2><3)
[       2.0 :+ 0.0,  4.0 :+ 0.0, 7.0 :+ 2.0
, (-3.0) :+ (-0.0), 11.0 :+ 0.0, 0.0 :+ 0.0 ]


The input list is explicitly truncated, so that it can safely be used with lists that are too long (like infinite lists).

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


This is the format produced by the instances of Show (Matrix a), which can also be used for input.

Arguments

 :: Int number of columns -> [R] elements in row order -> Matrix R

Create a real matrix.

>>> matrix 5 [1..15]
(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 ]


tr :: Transposable m mt => m -> mt Source

conjugate transpose

tr' :: Transposable m mt => m -> mt Source

transpose

# Dimensions

size :: Container c t => c t -> IndexOf c Source

>>> size $vector [1..10] 10 >>> size$ (2><5)[1..10::Double]
(2,5)


# Conversion from/to lists

fromLists :: Element t => [[t]] -> Matrix t Source

Creates a Matrix from a list of lists (considered as rows).

>>> fromLists [[1,2],[3,4],[5,6]]
(3><2)
[ 1.0, 2.0
, 3.0, 4.0
, 5.0, 6.0 ]


toLists :: Element t => Matrix t -> [[t]] Source

the inverse of fromLists

row :: [Double] -> Matrix Double Source

create a single row real matrix from a list

>>> row [2,3,1,8]
(1><4)
[ 2.0, 3.0, 1.0, 8.0 ]


col :: [Double] -> Matrix Double Source

create a single column real matrix from a list

>>> col [7,-2,4]
(3><1)
[  7.0
, -2.0
,  4.0 ]


# Conversions vector/matrix

flatten :: Element t => Matrix t -> Vector t Source

Creates a vector by concatenation of rows. If the matrix is ColumnMajor, this operation requires a transpose.

>>> flatten (ident 3)
fromList [1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0]


reshape :: Storable t => Int -> Vector t -> Matrix t Source

Creates a matrix from a vector by grouping the elements in rows with the desired number of columns. (GNU-Octave groups by columns. To do it you can define reshapeF r = tr' . reshape r where r is the desired number of rows.)

>>> reshape 4 (fromList [1..12])
(3><4)
[ 1.0,  2.0,  3.0,  4.0
, 5.0,  6.0,  7.0,  8.0
, 9.0, 10.0, 11.0, 12.0 ]


asRow :: Storable a => Vector a -> Matrix a Source

creates a 1-row matrix from a vector

>>> asRow (fromList [1..5])
 (1><5)
[ 1.0, 2.0, 3.0, 4.0, 5.0 ]


asColumn :: Storable a => Vector a -> Matrix a Source

creates a 1-column matrix from a vector

>>> asColumn (fromList [1..5])
(5><1)
[ 1.0
, 2.0
, 3.0
, 4.0
, 5.0 ]


fromRows :: Element t => [Vector t] -> Matrix t Source

Create a matrix from a list of vectors. All vectors must have the same dimension, or dimension 1, which is are automatically expanded.

toRows :: Element t => Matrix t -> [Vector t] Source

extracts the rows of a matrix as a list of vectors

fromColumns :: Element t => [Vector t] -> Matrix t Source

Creates a matrix from a list of vectors, as columns

toColumns :: Element t => Matrix t -> [Vector t] Source

Creates a list of vectors from the columns of a matrix

# Indexing

atIndex :: Container c e => c e -> IndexOf c -> e Source

generic indexing function

>>> vector [1,2,3] atIndex 1
2.0

>>> matrix 3 [0..8] atIndex (2,0)
6.0


class Indexable c t | c -> t, t -> c where Source

Alternative indexing function.

>>> vector [1..10] ! 3
4.0


On a matrix it gets the k-th row as a vector:

>>> matrix 5 [1..15] ! 1
fromList [6.0,7.0,8.0,9.0,10.0]

>>> matrix 5 [1..15] ! 1 ! 3
9.0


Methods

(!) :: c -> Int -> t infixl 9 Source

Instances

 Indexable (Vector Double) Double Source Indexable (Vector Float) Float Source Indexable (Vector Z) Z Source Indexable (Vector I) I Source Indexable (Vector (Complex Double)) (Complex Double) Source Indexable (Vector (Complex Float)) (Complex Float) Source Element t => Indexable (Matrix t) (Vector t) Source (Storable t, Indexable (Vector t) t) => Indexable (Vector (Mod m t)) (Mod m t) Source

# Construction

scalar :: Container c e => e -> c e Source

create a structure with a single element

>>> let v = fromList [1..3::Double]
>>> v / scalar (norm2 v)
fromList [0.2672612419124244,0.5345224838248488,0.8017837257372732]


class Konst e d c | d -> c, c -> d where Source

Methods

konst :: e -> d -> c e Source

>>> konst 7 3 :: Vector Float
fromList [7.0,7.0,7.0]

>>> konst i (3::Int,4::Int)
(3><4)
[ 0.0 :+ 1.0, 0.0 :+ 1.0, 0.0 :+ 1.0, 0.0 :+ 1.0
, 0.0 :+ 1.0, 0.0 :+ 1.0, 0.0 :+ 1.0, 0.0 :+ 1.0
, 0.0 :+ 1.0, 0.0 :+ 1.0, 0.0 :+ 1.0, 0.0 :+ 1.0 ]


Instances

 Container Vector e => Konst e Int Vector Source (Num e, Container Vector e) => Konst e (Int, Int) Matrix Source

class Build d f c e | d -> c, c -> d, f -> e, f -> d, f -> c, c e -> f, d e -> f where Source

Methods

build :: d -> f -> c e Source

>>> build 5 (**2) :: Vector Double
fromList [0.0,1.0,4.0,9.0,16.0]


Hilbert matrix of order N:

>>> let hilb n = build (n,n) (\i j -> 1/(i+j+1)) :: Matrix Double
>>> putStr . dispf 2 $hilb 3 3x3 1.00 0.50 0.33 0.50 0.33 0.25 0.33 0.25 0.20  Instances  Container Vector e => Build Int (e -> e) Vector e Source Container Matrix e => Build (Int, Int) (e -> e -> e) Matrix e Source Arguments  :: Container c e => IndexOf c size -> e default value -> [(IndexOf c, e)] association list -> c e result Create a structure from an association list >>> assoc 5 0 [(3,7),(1,4)] :: Vector Double fromList [0.0,4.0,0.0,7.0,0.0]  >>> assoc (2,3) 0 [((0,2),7),((1,0),2*i-3)] :: Matrix (Complex Double) (2><3) [ 0.0 :+ 0.0, 0.0 :+ 0.0, 7.0 :+ 0.0 , (-3.0) :+ 2.0, 0.0 :+ 0.0, 0.0 :+ 0.0 ]  Arguments  :: Container c e => c e initial structure -> (e -> e -> e) update function -> [(IndexOf c, e)] association list -> c e result Modify a structure using an update function >>> accum (ident 5) (+) [((1,1),5),((0,3),3)] :: Matrix Double (5><5) [ 1.0, 0.0, 0.0, 3.0, 0.0 , 0.0, 6.0, 0.0, 0.0, 0.0 , 0.0, 0.0, 1.0, 0.0, 0.0 , 0.0, 0.0, 0.0, 1.0, 0.0 , 0.0, 0.0, 0.0, 0.0, 1.0 ]  computation of histogram: >>> accum (konst 0 7) (+) (map (flip (,) 1) [4,5,4,1,5,2,5]) :: Vector Double fromList [0.0,1.0,1.0,0.0,2.0,3.0,0.0]  linspace :: (Fractional e, Container Vector e) => Int -> (e, e) -> Vector e Source Creates a real vector containing a range of values: >>> linspace 5 (-3,7::Double) fromList [-3.0,-0.5,2.0,4.5,7.0]@  >>> linspace 5 (8,2+i) :: Vector (Complex Double) fromList [8.0 :+ 0.0,6.5 :+ 0.25,5.0 :+ 0.5,3.5 :+ 0.75,2.0 :+ 1.0]  Logarithmic spacing can be defined as follows: logspace n (a,b) = 10 ** linspace n (a,b) # Diagonal ident :: (Num a, Element a) => Int -> Matrix a Source creates the identity matrix of given dimension diag :: (Num a, Element a) => Vector a -> Matrix a Source Creates a square matrix with a given diagonal. diagl :: [Double] -> Matrix Double Source create a real diagonal matrix from a list >>> diagl [1,2,3] (3><3) [ 1.0, 0.0, 0.0 , 0.0, 2.0, 0.0 , 0.0, 0.0, 3.0 ]  diagRect :: Storable t => t -> Vector t -> Int -> Int -> Matrix t Source creates a rectangular diagonal matrix: >>> diagRect 7 (fromList [10,20,30]) 4 5 :: Matrix Double (4><5) [ 10.0, 7.0, 7.0, 7.0, 7.0 , 7.0, 20.0, 7.0, 7.0, 7.0 , 7.0, 7.0, 30.0, 7.0, 7.0 , 7.0, 7.0, 7.0, 7.0, 7.0 ]  takeDiag :: Element t => Matrix t -> Vector t Source extracts the diagonal from a rectangular matrix # Vector extraction Arguments  :: Storable t => Int index of the starting element -> Int number of elements to extract -> Vector t source -> Vector t result takes a number of consecutive elements from a Vector >>> subVector 2 3 (fromList [1..10]) fromList [3.0,4.0,5.0]  takesV :: Storable t => [Int] -> Vector t -> [Vector t] Source Extract consecutive subvectors of the given sizes. >>> takesV [3,4] (linspace 10 (1,10::Double)) [fromList [1.0,2.0,3.0],fromList [4.0,5.0,6.0,7.0]]  vjoin :: Storable t => [Vector t] -> Vector t Source concatenate a list of vectors >>> vjoin [fromList [1..5::Double], konst 1 3] fromList [1.0,2.0,3.0,4.0,5.0,1.0,1.0,1.0]  # Matrix extraction data Extractor Source Specification of indexes for the operator ??. Constructors  All Range Int Int Int Pos (Vector I) PosCyc (Vector I) Take Int TakeLast Int Drop Int DropLast Int Instances  Show Extractor Source (??) :: Element t => Matrix t -> (Extractor, Extractor) -> Matrix t infixl 9 Source General matrix slicing. >>> m (4><5) [ 0, 1, 2, 3, 4 , 5, 6, 7, 8, 9 , 10, 11, 12, 13, 14 , 15, 16, 17, 18, 19 ]  >>> m ?? (Take 3, DropLast 2) (3><3) [ 0, 1, 2 , 5, 6, 7 , 10, 11, 12 ]  >>> m ?? (Pos (idxs[2,1]), All) (2><5) [ 10, 11, 12, 13, 14 , 5, 6, 7, 8, 9 ]  >>> m ?? (PosCyc (idxs[-7,80]), Range 4 (-2) 0) (2><3) [ 9, 7, 5 , 4, 2, 0 ]  (?) :: Element t => Matrix t -> [Int] -> Matrix t infixl 9 Source extract rows >>> (20><4) [1..] ? [2,1,1] (3><4) [ 9.0, 10.0, 11.0, 12.0 , 5.0, 6.0, 7.0, 8.0 , 5.0, 6.0, 7.0, 8.0 ]  ¿ :: Element t => Matrix t -> [Int] -> Matrix t infixl 9 Source extract columns (unicode 0x00bf, inverted question mark, Alt-Gr ?) >>> (3><4) [1..] ¿ [3,0] (3><2) [ 4.0, 1.0 , 8.0, 5.0 , 12.0, 9.0 ]  fliprl :: Element t => Matrix t -> Matrix t Source Reverse columns flipud :: Element t => Matrix t -> Matrix t Source Reverse rows Arguments  :: Element a => (Int, Int) (r0,c0) starting position -> (Int, Int) (rt,ct) dimensions of submatrix -> Matrix a input matrix -> Matrix a result reference to a rectangular slice of a matrix (no data copy) remap :: Element t => Matrix I -> Matrix I -> Matrix t -> Matrix t Source Extract elements from positions given in matrices of rows and columns. >>> r (3><3) [ 1, 1, 1 , 1, 2, 2 , 1, 2, 3 ] >>> c (3><3) [ 0, 1, 5 , 2, 2, 1 , 4, 4, 1 ] >>> m (4><6) [ 0, 1, 2, 3, 4, 5 , 6, 7, 8, 9, 10, 11 , 12, 13, 14, 15, 16, 17 , 18, 19, 20, 21, 22, 23 ]  >>> remap r c m (3><3) [ 6, 7, 11 , 8, 14, 13 , 10, 16, 19 ]  The indexes are autoconformable. >>> c' (3><1) [ 1 , 2 , 4 ] >>> remap r c' m (3><3) [ 7, 7, 7 , 8, 14, 14 , 10, 16, 22 ]  # Block matrix fromBlocks :: Element t => [[Matrix t]] -> Matrix t Source Create a matrix from blocks given as a list of lists of matrices. Single row-column components are automatically expanded to match the corresponding common row and column: disp = putStr . dispf 2  >>> disp$ fromBlocks [[ident 5, 7, row[10,20]], [3, diagl[1,2,3], 0]]
8x10
1  0  0  0  0  7  7  7  10  20
0  1  0  0  0  7  7  7  10  20
0  0  1  0  0  7  7  7  10  20
0  0  0  1  0  7  7  7  10  20
0  0  0  0  1  7  7  7  10  20
3  3  3  3  3  1  0  0   0   0
3  3  3  3  3  0  2  0   0   0
3  3  3  3  3  0  0  3   0   0


(|||) :: Element t => Matrix t -> Matrix t -> Matrix t infixl 3 Source

horizontal concatenation

>>> ident 3 ||| konst 7 (3,4)
(3><7)
[ 1.0, 0.0, 0.0, 7.0, 7.0, 7.0, 7.0
, 0.0, 1.0, 0.0, 7.0, 7.0, 7.0, 7.0
, 0.0, 0.0, 1.0, 7.0, 7.0, 7.0, 7.0 ]


(===) :: Element t => Matrix t -> Matrix t -> Matrix t infixl 2 Source

vertical concatenation

diagBlock :: (Element t, Num t) => [Matrix t] -> Matrix t Source

create a block diagonal matrix

>>> disp 2 $diagBlock [konst 1 (2,2), konst 2 (3,5), col [5,7]] 7x8 1 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 2 2 2 2 2 0 0 0 2 2 2 2 2 0 0 0 2 2 2 2 2 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 0 7  >>> diagBlock [(0><4)[], konst 2 (2,3)] :: Matrix Double (2><7) [ 0.0, 0.0, 0.0, 0.0, 2.0, 2.0, 2.0 , 0.0, 0.0, 0.0, 0.0, 2.0, 2.0, 2.0 ]  repmat :: Element t => Matrix t -> Int -> Int -> Matrix t Source creates matrix by repetition of a matrix a given number of rows and columns >>> repmat (ident 2) 2 3 (4><6) [ 1.0, 0.0, 1.0, 0.0, 1.0, 0.0 , 0.0, 1.0, 0.0, 1.0, 0.0, 1.0 , 1.0, 0.0, 1.0, 0.0, 1.0, 0.0 , 0.0, 1.0, 0.0, 1.0, 0.0, 1.0 ]  toBlocks :: Element t => [Int] -> [Int] -> Matrix t -> [[Matrix t]] Source Partition a matrix into blocks with the given numbers of rows and columns. The remaining rows and columns are discarded. toBlocksEvery :: Element t => Int -> Int -> Matrix t -> [[Matrix t]] Source Fully partition a matrix into blocks of the same size. If the dimensions are not a multiple of the given size the last blocks will be smaller. # Mapping functions conj :: Container c e => c e -> c e Source complex conjugate cmap :: (Element b, Container c e) => (e -> b) -> c e -> c b Source like fmap (cannot implement instance Functor because of Element class constraint) cmod :: (Integral e, Container c e) => e -> c e -> c e Source mod for integer arrays >>> cmod 3 (range 5) fromList [0,1,2,0,1]  step :: (Ord e, Container c e) => c e -> c e Source A more efficient implementation of cmap (\x -> if x>0 then 1 else 0) >>> step$ linspace 5 (-1,1::Double)
5 |> [0.0,0.0,0.0,1.0,1.0]


Arguments

 :: (Ord e, Container c e, Container c x) => c e a -> c e b -> c x l -> c x e -> c x g -> c x result

Element by element version of case compare a b of {LT -> l; EQ -> e; GT -> g}.

Arguments with any dimension = 1 are automatically expanded:

>>> cond ((1><4)[1..]) ((3><1)[1..]) 0 100 ((3><4)[1..]) :: Matrix Double
(3><4)
[ 100.0,   2.0,   3.0,  4.0
,   0.0, 100.0,   7.0,  8.0
,   0.0,   0.0, 100.0, 12.0 ]

>>> let chop x = cond (abs x) 1E-6 0 0 x


# Find elements

find :: Container c e => (e -> Bool) -> c e -> [IndexOf c] Source

Find index of elements which satisfy a predicate

>>> find (>0) (ident 3 :: Matrix Double)
[(0,0),(1,1),(2,2)]


maxIndex :: Container c e => c e -> IndexOf c Source

index of maximum element

minIndex :: Container c e => c e -> IndexOf c Source

index of minimum element

maxElement :: Container c e => c e -> e Source

value of maximum element

minElement :: Container c e => c e -> e Source

value of minimum element

sortIndex :: (Ord t, Element t) => Vector t -> Vector I Source

>>> m <- randn 4 10
>>> disp 2 m
4x10
-0.31   0.41   0.43  -0.19  -0.17  -0.23  -0.17  -1.04  -0.07  -1.24
0.26   0.19   0.14   0.83  -1.54  -0.09   0.37  -0.63   0.71  -0.50
-0.11  -0.10  -1.29  -1.40  -1.04  -0.89  -0.68   0.35  -1.46   1.86
1.04  -0.29   0.19  -0.75  -2.20  -0.01   1.06   0.11  -2.09  -1.58

>>> disp 2 $m ?? (All, Pos$ sortIndex (m!1))
4x10
-0.17  -1.04  -1.24  -0.23   0.43   0.41  -0.31  -0.17  -0.07  -0.19
-1.54  -0.63  -0.50  -0.09   0.14   0.19   0.26   0.37   0.71   0.83
-1.04   0.35   1.86  -0.89  -1.29  -0.10  -0.11  -0.68  -1.46  -1.40
-2.20   0.11  -1.58  -0.01   0.19  -0.29   1.04   1.06  -2.09  -0.75


# IO

disp :: Int -> Matrix Double -> IO () Source

print a real matrix with given number of digits after the decimal point

>>> disp 5 $ident 2 / 3 2x2 0.33333 0.00000 0.00000 0.33333  load a matrix from an ASCII file formatted as a 2D table. Arguments  :: FilePath -> String "printf" format (e.g. "%.2f", "%g", etc.) -> Matrix Double -> IO () save a matrix as a 2D ASCII table Arguments  :: String type of braces: "matrix", "bmatrix", "pmatrix", etc. -> String Formatted matrix, with elements separated by spaces and newlines -> String Tool to display matrices with latex syntax. >>> latexFormat "bmatrix" (dispf 2$ ident 2)
"\\begin{bmatrix}\n1  &  0\n\\\\\n0  &  1\n\\end{bmatrix}"


Show a matrix with a given number of decimal places.

>>> dispf 2 (1/3 + ident 3)
"3x3\n1.33  0.33  0.33\n0.33  1.33  0.33\n0.33  0.33  1.33\n"

>>> putStr . dispf 2 $(3><4)[1,1.5..] 3x4 1.00 1.50 2.00 2.50 3.00 3.50 4.00 4.50 5.00 5.50 6.00 6.50  >>> putStr . unlines . tail . lines . dispf 2 . asRow$ linspace 10 (0,1)
0.00  0.11  0.22  0.33  0.44  0.56  0.67  0.78  0.89  1.00


Show a matrix with "autoscaling" and a given number of decimal places.

>>> putStr . disps 2 \$ 120 * (3><4) [1..]
3x4  E3
0.12  0.24  0.36  0.48
0.60  0.72  0.84  0.96
1.08  1.20  1.32  1.44


Pretty print a complex matrix with at most n decimal digits.

format :: Element t => String -> (t -> String) -> Matrix t -> String Source

Creates a string from a matrix given a separator and a function to show each entry. Using this function the user can easily define any desired display function:

import Text.Printf(printf)
disp = putStr . format "  " (printf "%.2f")

# Element conversion

class Convert t where Source

Methods

real :: Complexable c => c (RealOf t) -> c t Source

complex :: Complexable c => c t -> c (ComplexOf t) Source

single :: Complexable c => c t -> c (SingleOf t) Source

double :: Complexable c => c t -> c (DoubleOf t) Source

toComplex :: (Complexable c, RealElement t) => (c t, c t) -> c (Complex t) Source

fromComplex :: (Complexable c, RealElement t) => c (Complex t) -> (c t, c t) Source

Instances

 Convert Double Source Convert Float Source Convert (Complex Double) Source Convert (Complex Float) Source

fromInt :: Container c e => c I -> c e Source

>>> fromInt ((2><2) [0..3]) :: Matrix (Complex Double)
(2><2)
[ 0.0 :+ 0.0, 1.0 :+ 0.0
, 2.0 :+ 0.0, 3.0 :+ 0.0 ]


toInt :: Container c e => c e -> c I Source

fromZ :: Container c e => c Z -> c e Source

toZ :: Container c e => c e -> c Z Source

# Misc

arctan2 :: (Fractional e, Container c e) => c e -> c e -> c e Source

separable :: Element t => (Vector t -> Vector t) -> Matrix t -> Matrix t Source

matrix computation implemented as separated vector operations by rows and columns.

data Mod n t Source

Wrapper with a phantom integer for statically checked modular arithmetic.

Instances

 KnownNat m => Container Vector (Mod m Z) Source KnownNat m => Container Vector (Mod m I) Source KnownNat m => Num (Vector (Mod m Z)) Source KnownNat m => Num (Vector (Mod m I)) Source KnownNat m => Testable (Matrix (Mod m I)) Source KnownNat m => Normed (Vector (Mod m Z)) Source KnownNat m => Normed (Vector (Mod m I)) Source (Storable t, Indexable (Vector t) t) => Indexable (Vector (Mod m t)) (Mod m t) Source (Integral t, Enum t, KnownNat m) => Enum (Mod m t) Source (Eq t, KnownNat m) => Eq (Mod m t) Source (Show (Mod m t), Num (Mod m t), Eq t, KnownNat m) => Fractional (Mod m t) Source this instance is only valid for prime m (Integral t, KnownNat m, Num (Mod m t)) => Integral (Mod m t) Source (Integral t, KnownNat n) => Num (Mod n t) Source (Ord t, KnownNat m) => Ord (Mod m t) Source (Real t, KnownNat m, Integral (Mod m t)) => Real (Mod m t) Source Show t => Show (Mod n t) Source Storable t => Storable (Mod n t) Source NFData t => NFData (Mod n t) Source KnownNat m => Element (Mod m Z) Source KnownNat m => Element (Mod m I) Source KnownNat m => Product (Mod m Z) Source KnownNat m => Product (Mod m I) Source KnownNat m => Numeric (Mod m Z) Source KnownNat m => Numeric (Mod m I) Source type RealOf (Mod n Z) = Z Source type RealOf (Mod n I) = I Source

data Vector a :: * -> *

Instances

 Complexable Vector LSDiv Vector Storable a => Vector Vector a Container Vector t => Linear t Vector Container Vector Double Container Vector Float Container Vector Z Container Vector I Container Vector e => Konst e Int Vector Container Vector (Complex Double) Container Vector (Complex Float) KnownNat n => Sized ℝ (R n) Vector KnownNat m => Container Vector (Mod m Z) KnownNat m => Container Vector (Mod m I) Container Vector e => Build Int (e -> e) Vector e Storable a => IsList (Vector a) (Storable a, Eq a) => Eq (Vector a) (Data a, Storable a) => Data (Vector a) KnownNat m => Num (Vector (Mod m Z)) KnownNat m => Num (Vector (Mod m I)) (Storable a, Ord a) => Ord (Vector a) (Read a, Storable a) => Read (Vector a) (Show a, Storable a) => Show (Vector a) Storable a => Monoid (Vector a) NFData (Vector a) Storable t => TransArray (Vector t) Container Vector t => Additive (Vector t) Normed (Vector Float) Normed (Vector (Complex Float)) Normed (Vector C) Normed (Vector R) Normed (Vector Z) Normed (Vector I) KnownNat m => Normed (Vector (Mod m Z)) KnownNat m => Normed (Vector (Mod m I)) Indexable (Vector Double) Double Indexable (Vector Float) Float Indexable (Vector Z) Z Indexable (Vector I) I Indexable (Vector (Complex Double)) (Complex Double) Indexable (Vector (Complex Float)) (Complex Float) Element t => Indexable (Matrix t) (Vector t) (Storable t, Indexable (Vector t) t) => Indexable (Vector (Mod m t)) (Mod m t) type Mutable Vector = MVector type IndexOf Vector = Int type Item (Vector a) = a type Trans (Vector t) b = CInt -> Ptr t -> b type TransRaw (Vector t) b = CInt -> Ptr t -> b

data Matrix t Source

Matrix representation suitable for BLAS/LAPACK computations.

Instances

 Complexable Matrix Source LSDiv Matrix Source Container Matrix t => Linear t Matrix Source (Num a, Element a, Container Vector a) => Container Matrix a Source (Num e, Container Vector e) => Konst e (Int, Int) Matrix Source (KnownNat m, KnownNat n) => Sized ℝ (L m n) Matrix Source (Storable t, NFData t) => NFData (Matrix t) Source Storable t => TransArray (Matrix t) Source KnownNat m => Testable (Matrix (Mod m I)) Source Container Matrix t => Additive (Matrix t) Source Normed (Matrix C) Source Normed (Matrix R) Source (CTrans t, Container Vector t) => Transposable (Matrix t) (Matrix t) Source Element t => Indexable (Matrix t) (Vector t) Source Container Matrix e => Build (Int, Int) (e -> e -> e) Matrix e Source type IndexOf Matrix = (Int, Int) Source type Trans (Matrix t) b = CInt -> CInt -> CInt -> CInt -> Ptr t -> b Source type TransRaw (Matrix t) b = CInt -> CInt -> Ptr t -> b Source

data GMatrix Source

General matrix with specialized internal representations for dense, sparse, diagonal, banded, and constant elements.

>>> let m = mkSparse [((0,999),1.0),((1,1999),2.0)]
>>> m
SparseR {gmCSR = CSR {csrVals = fromList [1.0,2.0],
csrCols = fromList [1000,2000],
csrRows = fromList [1,2,3],
csrNRows = 2,
csrNCols = 2000},
nRows = 2,
nCols = 2000}

>>> let m = mkDense (mat 2 [1..4])
>>> m
Dense {gmDense = (2><2)
[ 1.0, 2.0
, 3.0, 4.0 ], nRows = 2, nCols = 2}


Instances

 Show GMatrix Source Transposable GMatrix GMatrix Source