| Copyright | (c) Alberto Ruiz 2015 |
|---|---|
| License | BSD3 |
| Maintainer | Alberto Ruiz |
| Stability | provisional |
| Safe Haskell | None |
| Language | Haskell98 |
Numeric.LinearAlgebra.Data
Contents
Description
This module provides functions for creation and manipulation of vectors and matrices, IO, and other utilities.
- type R = Double
- type C = Complex Double
- type I = CInt
- type Z = Int64
- type (./.) x n = Mod n x
- fromList :: Storable a => [a] -> Vector a
- toList :: Storable a => Vector a -> [a]
- (|>) :: Storable a => Int -> [a] -> Vector a
- vector :: [R] -> Vector R
- range :: Int -> Vector I
- idxs :: [Int] -> Vector I
- (><) :: Storable a => Int -> Int -> [a] -> Matrix a
- matrix :: Int -> [R] -> Matrix R
- tr :: Transposable m mt => m -> mt
- tr' :: Transposable m mt => m -> mt
- size :: Container c t => c t -> IndexOf c
- rows :: Matrix t -> Int
- cols :: Matrix t -> Int
- fromLists :: Element t => [[t]] -> Matrix t
- toLists :: Element t => Matrix t -> [[t]]
- row :: [Double] -> Matrix Double
- col :: [Double] -> Matrix Double
- flatten :: Element t => Matrix t -> Vector t
- reshape :: Storable t => Int -> Vector t -> Matrix t
- asRow :: Storable a => Vector a -> Matrix a
- asColumn :: Storable a => Vector a -> Matrix a
- fromRows :: Element t => [Vector t] -> Matrix t
- toRows :: Element t => Matrix t -> [Vector t]
- fromColumns :: Element t => [Vector t] -> Matrix t
- toColumns :: Element t => Matrix t -> [Vector t]
- atIndex :: Container c e => c e -> IndexOf c -> e
- class Indexable c t | c -> t, t -> c where
- scalar :: Container c e => e -> c e
- class Konst e d c | d -> c, c -> d where
- class Build d f c e | d -> c, c -> d, f -> e, f -> d, f -> c, c e -> f, d e -> f where
- assoc :: Container c e => IndexOf c -> e -> [(IndexOf c, e)] -> c e
- accum :: Container c e => c e -> (e -> e -> e) -> [(IndexOf c, e)] -> c e
- linspace :: (Fractional e, Container Vector e) => Int -> (e, e) -> Vector e
- ident :: (Num a, Element a) => Int -> Matrix a
- diag :: (Num a, Element a) => Vector a -> Matrix a
- diagl :: [Double] -> Matrix Double
- diagRect :: Storable t => t -> Vector t -> Int -> Int -> Matrix t
- takeDiag :: Element t => Matrix t -> Vector t
- subVector :: Storable t => Int -> Int -> Vector t -> Vector t
- takesV :: Storable t => [Int] -> Vector t -> [Vector t]
- vjoin :: Storable t => [Vector t] -> Vector t
- data Extractor
- (??) :: Element t => Matrix t -> (Extractor, Extractor) -> Matrix t
- (?) :: Element t => Matrix t -> [Int] -> Matrix t
- (¿) :: Element t => Matrix t -> [Int] -> Matrix t
- fliprl :: Element t => Matrix t -> Matrix t
- flipud :: Element t => Matrix t -> Matrix t
- subMatrix :: Element a => (Int, Int) -> (Int, Int) -> Matrix a -> Matrix a
- takeRows :: Element t => Int -> Matrix t -> Matrix t
- dropRows :: Element t => Int -> Matrix t -> Matrix t
- takeColumns :: Element t => Int -> Matrix t -> Matrix t
- dropColumns :: Element t => Int -> Matrix t -> Matrix t
- remap :: Element t => Matrix I -> Matrix I -> Matrix t -> Matrix t
- fromBlocks :: Element t => [[Matrix t]] -> Matrix t
- (|||) :: Element t => Matrix t -> Matrix t -> Matrix t
- (===) :: Element t => Matrix t -> Matrix t -> Matrix t
- diagBlock :: (Element t, Num t) => [Matrix t] -> Matrix t
- repmat :: Element t => Matrix t -> Int -> Int -> Matrix t
- toBlocks :: Element t => [Int] -> [Int] -> Matrix t -> [[Matrix t]]
- toBlocksEvery :: Element t => Int -> Int -> Matrix t -> [[Matrix t]]
- conj :: Container c e => c e -> c e
- cmap :: (Element b, Container c e) => (e -> b) -> c e -> c b
- cmod :: (Integral e, Container c e) => e -> c e -> c e
- step :: (Ord e, Container c e) => c e -> c e
- cond :: (Ord e, Container c e, Container c x) => c e -> c e -> c x -> c x -> c x -> c x
- find :: Container c e => (e -> Bool) -> c e -> [IndexOf c]
- maxIndex :: Container c e => c e -> IndexOf c
- minIndex :: Container c e => c e -> IndexOf c
- maxElement :: Container c e => c e -> e
- minElement :: Container c e => c e -> e
- sortVector :: (Ord t, Element t) => Vector t -> Vector t
- sortIndex :: (Ord t, Element t) => Vector t -> Vector I
- type AssocMatrix = [((Int, Int), Double)]
- toDense :: AssocMatrix -> Matrix Double
- mkSparse :: AssocMatrix -> GMatrix
- mkDiagR :: Int -> Int -> Vector Double -> GMatrix
- mkDense :: Matrix Double -> GMatrix
- disp :: Int -> Matrix Double -> IO ()
- loadMatrix :: FilePath -> IO (Matrix Double)
- loadMatrix' :: FilePath -> IO (Maybe (Matrix Double))
- saveMatrix :: FilePath -> String -> Matrix Double -> IO ()
- latexFormat :: String -> String -> String
- dispf :: Int -> Matrix Double -> String
- disps :: Int -> Matrix Double -> String
- dispcf :: Int -> Matrix (Complex Double) -> String
- format :: Element t => String -> (t -> String) -> Matrix t -> String
- dispDots :: Int -> Matrix Double -> IO ()
- dispBlanks :: Int -> Matrix Double -> IO ()
- dispShort :: Int -> Int -> Int -> Matrix Double -> IO ()
- class Convert t where
- roundVector :: Vector Double -> Vector Double
- fromInt :: Container c e => c I -> c e
- toInt :: Container c e => c e -> c I
- fromZ :: Container c e => c Z -> c e
- toZ :: Container c e => c e -> c Z
- arctan2 :: (Fractional e, Container c e) => c e -> c e -> c e
- separable :: Element t => (Vector t -> Vector t) -> Matrix t -> Matrix t
- fromArray2D :: Storable e => Array (Int, Int) e -> Matrix e
- module Data.Complex
- data Mod (n :: Nat) t
- data Vector a :: * -> *
- data Matrix t
- data GMatrix
- nRows :: GMatrix -> Int
- nCols :: GMatrix -> Int
Elements
Vector
1D arrays are storable vectors directly reexported from the vector package.
(|>) :: 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]
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.
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 ]
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` 12.0
>>>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] ! 34.0
On a matrix it gets the k-th row as a vector:
>>>matrix 5 [1..15] ! 1fromList [6.0,7.0,8.0,9.0,10.0]
>>>matrix 5 [1..15] ! 1 ! 39.0
Minimal complete definition
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 #
Minimal complete definition
class Build d f c e | d -> c, c -> d, f -> e, f -> d, f -> c, c e -> f, d e -> f where Source #
Minimal complete definition
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 DoublefromList [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 DoublefromList [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
Specification of indexes for the operator ??.
(??) :: 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 ]
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 ]
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
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]
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)]
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 m4x10 -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
Sparse
mkSparse :: AssocMatrix -> GMatrix Source #
IO
disp :: Int -> Matrix Double -> IO () Source #
print a real matrix with given number of digits after the decimal point
>>>disp 5 $ ident 2 / 32x2 0.33333 0.00000 0.00000 0.33333
loadMatrix :: FilePath -> IO (Matrix Double) Source #
load a matrix from an ASCII file formatted as a 2D table.
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}"
dispf :: Int -> Matrix Double -> String Source #
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
disps :: Int -> Matrix Double -> String Source #
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
dispcf :: Int -> Matrix (Complex Double) -> String Source #
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 #
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 ]
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.
module Data.Complex
data Mod (n :: Nat) t Source #
Wrapper with a phantom integer for statically checked modular arithmetic.
Instances
Storable-based vectors
Instances
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 ℂ (M m n) 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 Source # | |
| type Trans (Matrix t) b Source # | |
| type TransRaw (Matrix t) b 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)]>>>mSparseR {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])>>>mDense {gmDense = (2><2) [ 1.0, 2.0 , 3.0, 4.0 ], nRows = 2, nCols = 2}