An overview of different Haskell numeric libraries. This is intended to be a place to compare different numeric libraries by their ease of use, performance and more.
Libraries are categorised and you should compare the libraries in the same category with each other.
Pull requests are welcome if you want to add a library!
These overviews include:
- Benchmarks
- Example code
- Links to hackage, github and the homepage if available
An efficient implementation of Int-indexed arrays (both mutable and immutable), with a powerful loop optimisation framework.
Example code:
let x = fromList [0..5]
length x -- 6
null x -- False
-- Indexing
x ! 1 -- 1
head x -- 0
last x -- 5
-- Slicing
slice 2 3 x -- [2, 3, 4]
splitAt 2 x -- ([0, 1], [2, 3, 4, 5])
-- Prepending and Appending
cons (-1) x -- [-1, 0, 1, 2, 3, 4, 5]
snoc x 6 -- [0, 1, 2, 3, 4, 5, 6]
-- Concatenation
x ++ x -- [0, 1, 2, 3, 4, 5, 0, 1, 2, 3, 4, 5]
concat [x, x] -- [0, 1, 2, 3, 4, 5, 0, 1, 2, 3, 4, 5]
-- Update
x // [(0, 1), (2, 6)] -- [1, 1, 6, 3, 4, 5]
map (+2) x -- [2, 3, 4, 5, 6, 7]
hmatrix is a linear algebra library and matrix computations.
Example code:
-- Creating matrices
let x = (2><2) [0..3] :: Matrix Double
x -- [ 0.0, 1.0
-- , 2.0, 3.0 ]
let y = fromLists [[0, 1], [2, 3]] :: Matrix Double
-- Random matrices
r <- randn 2 3
r -- [ 0.7764496757867578, 1.246311658930589, -0.684233085372455
-- , -2.540045307941425, -0.20975584071908912, -9.039537343065803e-3 ]
-- Matrix multiplication
x <> y -- [ 2.0, 3.0
-- , 6.0, 11.0 ]
-- Transpose
tr x -- [ 0.0, 2.0
-- , 1.0, 3.0 ]
-- Matrix slicing
r ?? (All, Take 2) -- [ 0.7764496757867578, 1.246311658930589
-- , -2.540045307941425, -0.20975584071908912 ]
-- Mapping over matrices
cmap ((+ 2) . (*2)) x -- [ 2.0, 4.0
-- , 6.0, 8.0 ]
-- Flatten
flatten x -- [0.0, 1.0, 2.0, 3.0]
Notes:
- Uses the vector library under the hood (specifically,
Data.Vector.Storable
)
Links: Hackage . GitHub . Homepage
hmatrix is a linear algebra library and matrix computations.
Example code:
-- Creating matrices
r <- randn 2 2
r -- [ 0.7668461757288327, 0.5573308002071669
-- , -0.7412791132378888, 1.001032678483079 ]
-- SVD
(u, s, v) = svd r
(u, s, v) = thinSVD r
eigenvalues r
singularValues r
nullspace r
orthogonal r
determinant r
Notes:
- Uses the vector library under the hood (specifically,
Data.Vector.Storable
)
Links: Hackage . GitHub . Homepage
- Benchmarks are written using criterion
- Please try to write an example code that matches format of the others and presets the same set of functionalities if available