Hmatrix  from zeros to hero
11 Feb 2024The goal of this post is to give a brief introduction to hmatrix
’s Static API
and show how to implement a typesafe zeros
function in two different ways.
I used GHC 9.4.8 throughout.
The hmatrix
Static API
Hmatrix is a Haskell library that provides a nice functional interface for working with matrices. It is built on top of BLAS and LAPACK.
The library includes a Static API which allows one to define the size of the matrices at the type level. Here’s what creating a random 3x2 matrix looks like using the static API:
{# LANGUAGE DataKinds #}  For using typelevel numbers
import Numeric.LinearAlgebra.Static
main :: IO ()
main = do
x < rand :: IO (L 3 2)
print x
which gives the following output:
(matrix
[ 0.33926174432936207, 0.7822155900216734
, 0.9783547953601716, 0.40214259103040334
, 0.15344813892312728, 0.2366858740508025 ] :: L 3 2)
The benefit of storing the size at the type level is that the compiler can use
the information to prevent impossible operations at compile time. For example,
trying to compile this program (where (<>)
is matrix multiplication)
main :: IO ()
main = do
x < rand :: IO (L 3 2)
y < rand :: IO (L 5 10)
print (x <> y)
results in the following compiler error:
exe/Main.hs:24:18: error:
• Couldn't match type ‘5’ with ‘2’
Expected: L 2 10
Actual: L 5 10
• In the second argument of ‘(LA.<>)’, namely ‘y’
In the first argument of ‘print’, namely ‘(x LA.<> y)’
In a stmt of a 'do' block: print (x LA.<> y)

24  print (x LA.<> y)

That is, the compiler is telling us that it is impossible to multiply a 3x2 matrix by a 5x10 matrix since 2 != 5. Admittedly, the ergonomics of the type error are not great, but the guarantees are nice!
On top of that, keeping the size information at the type level means we don’t need to check for different input sizes in the body of the function: the size of the inputs is guaranteed by the compiler.
However, the hmatrix
static API suffers from lack of development and
documentation. In the rest of this post we will close the gap a little by
implementing the zeros
function from Python’s numpy
two ways.
The zeros
function in two ways
numpy.zeros
in Python is a common function for creating matrices with all elements set to 0.
However, this function is missing from the Hmatrix
Static API. The goal of
this section is to implement it in two ways. The first method introduces some
key typelevel programming patters for working with the Static API. The second
method shows a cleaner alternative and is included for completeness.
The type of the zeros
function we want to build is:
import Numeric.LinearAlgebra.Static
zeros :: L n m
zeros = undefined
In other words, the zeros
function returns a matrix with all elements set to 0
of size NxM.
Implementation using matrix
Our first strategy for implementing zeros
will be to make a wrapper around the
unsafe
matrix
function from the Static API:
matrix :: (KnownNat m, KnownNat n) => [Double] > L n m
The matrix
function takes a list of Double
s of size N*M and returns a matrix
of size NxM. This function is unsafe, because giving matrix
a list of the wrong
size results in a runtime error. To solve this, we will make use of natVal
from
GHC.TypeLits
,
which allows us to bring information from the type level to the value level. For
example:
ghci> :set XDataKinds XTypeApplications
ghci> import Data.Proxy
ghci> import GHC.TypeLits
ghci> x = Proxy @5
ghci> y = Proxy @6
ghci> natVal x + natVal y
11
Here, we first create two values x
and y
using Proxy
. From the
docs:
Proxy is a type that holds no data, but has a phantom parameter of arbitrary type (or even kind). Its use is to provide type information, even though there is no value available of that type (or it may be too costly to create one).
In the example, we use Proxy
to carry numerical information at the type
level: x
carries the number 5 and y
carries the number 6
. We can then use
natVal
to bring the numbers carried by the proxies from the type level to the
value level and add them together. We also used the
TypeApplications
language extension to use the @
notation for specifying the value of type
variables.
In the zeros
function, we can use natVal
in order to automatically create a
list of the correct size using the standard replicate
function.
Let’s take a first stab at it:
{# LANGUAGE TypeApplications #}
import Numeric.LinearAlgebra.Static
import GHC.TypeLits (natVal)
import Data.Data (Proxy(..))
zeros :: L n m
zeros =
let n_val = fromIntegral (natVal @n Proxy)
m_val = fromIntegral (natVal @m Proxy)
in matrix (replicate (n_val * m_val) 0)
Trying to run this code gives us the following compiler error:
src/Const/Matrix.hs:13:37: error: Not in scope: type variable ‘n’

13  let n_val = fromIntegral (natVal @n Proxy)
 ^
src/Const/Matrix.hs:14:37: error: Not in scope: type variable ‘m’

14  m_val = fromIntegral (natVal @m Proxy)
 ^
This error occurs because by default the compiler does not recognise that the
type variable n
in the let
binding is the same as the type variable n
in
the function’s type definition (and the same with m
). To fix this we can use
the
ScopedTypeVariables
language extension which allows the body of the function to access type
variables from the function’s type definition when they are explicitly
introduced with the forall
keyword:
{# LANGUAGE TypeApplications #}
{# LANGUAGE ScopedTypeVariables #}
import Numeric.LinearAlgebra.Static
import GHC.TypeLits (natVal)
import Data.Data (Proxy(..))
zeros :: forall n m. L n m
zeros =
let n_val = fromIntegral (natVal @n Proxy)
m_val = fromIntegral (natVal @m Proxy)
in matrix (replicate (n_val * m_val) 0)
Now we get another scarylooking set of compiler errors:
src/Const/Matrix.hs:13:2934: error:
• No instance for (KnownNat n) arising from a use of ‘natVal’
Possible fix:
add (KnownNat n) to the context of
the type signature for:
zeros :: forall (n :: GHC.TypeNats.Nat) (m :: GHC.TypeNats.Nat).
L n m
• In the first argument of ‘fromIntegral’, namely
‘(natVal @n Proxy)’
In the expression: fromIntegral (natVal @n Proxy)
In an equation for ‘n_val’: n_val = fromIntegral (natVal @n Proxy)

13  let n_val = fromIntegral (natVal @n Proxy)
 ^^^^^^
src/Const/Matrix.hs:14:2934: error:
• No instance for (KnownNat m) arising from a use of ‘natVal’
Possible fix:
add (KnownNat m) to the context of
the type signature for:
zeros :: forall (n :: GHC.TypeNats.Nat) (m :: GHC.TypeNats.Nat).
L n m
• In the first argument of ‘fromIntegral’, namely
‘(natVal @m Proxy)’
In the expression: fromIntegral (natVal @m Proxy)
In an equation for ‘m_val’: m_val = fromIntegral (natVal @m Proxy)

14  m_val = fromIntegral (natVal @m Proxy)
 ^^^^^^
src/Const/Matrix.hs:15:712: error:
• No instance for (KnownNat n) arising from a use of ‘matrix’
Possible fix:
add (KnownNat n) to the context of
the type signature for:
zeros :: forall (n :: GHC.TypeNats.Nat) (m :: GHC.TypeNats.Nat).
L n m
• In the expression: matrix (replicate (n_val * m_val) 0)
In the expression:
let
n_val = fromIntegral (natVal @n Proxy)
m_val = fromIntegral (natVal @m Proxy)
in matrix (replicate (n_val * m_val) 0)
In an equation for ‘zeros’:
zeros
= let
n_val = fromIntegral (natVal @n Proxy)
m_val = fromIntegral (natVal @m Proxy)
in matrix (replicate (n_val * m_val) 0)

15  in matrix (replicate (n_val * m_val) 0)
 ^^^^^^
Here, the compiler is saying that it doesn’t know whether the type variables n
and m
are associated with a particular integer. This can be fixed by giving
them a KnownNat
constraint:
{# LANGUAGE TypeApplications #}
{# LANGUAGE ScopedTypeVariables #}
import Numeric.LinearAlgebra.Static
import GHC.TypeLits (natVal, KnownNat)
import Data.Data (Proxy(..))
zeros :: forall n m. (KnownNat n, KnownNat m) => L n m
zeros =
let n_val = fromIntegral (natVal @n Proxy)
m_val = fromIntegral (natVal @m Proxy)
in matrix (replicate (n_val * m_val) 0)
And voilá! We have implemented a typesafe function to create constant matrices
using the hmatrix
static API. Here’s a complete program that creates and
prints a matrix full of zeros:
{# LANGUAGE DataKinds #}
{# LANGUAGE ScopedTypeVariables #}
{# LANGUAGE TypeApplications #}
import Data.Data (Proxy (..))
import GHC.TypeLits (KnownNat, natVal)
import Numeric.LinearAlgebra.Static as LA
zeros :: forall n m. (KnownNat m, KnownNat n) => L n m
zeros =
let n_val = fromIntegral (natVal @n Proxy)
m_val = fromIntegral (natVal @m Proxy)
in matrix (replicate (n_val * m_val) 0)
main :: IO ()
main = print (zeros @3 @2)
which returns:
(matrix
[ 0.0, 0.0
, 0.0, 0.0
, 0.0, 0.0 ] :: L 3 2)
Implementation using build
A more straightforward (but less fun) way to implement the zeros
function in
Hmatrix is using the build
function from the Static API:
build :: forall m n. (KnownNat n, KnownNat m)
=> (Double > Double > Double) > L m n
build
is a higherorder function which takes a function as input and returns a
matrix of the correct size. The input function takes two arguments (the row and
column index of the element) and returns a value. The build
function then
creates the matrix of the correct size (using similar forall
and natVal
tricks as before) and creates the values by applying the input function to the
indices of each cell. Therefore, to implement zeros
all we need to do is
define a function that takes two arguments of type Double
and always returns
0 and pass it as an argument to build
:
constZero :: Double > Double > Double
constZero _ _ = 0
zeros :: (KnownNat n, KnownNat m) => L n m
zeros = build constZero
Here’s the full program:
{# LANGUAGE DataKinds #}
{# LANGUAGE TypeApplications #}
import GHC.TypeLits (KnownNat)
import Numeric.LinearAlgebra.Static
constZero :: Double > Double > Double
constZero _ _ = 0
zeros :: (KnownNat n, KnownNat m) => L n m
zeros = build constZero
main :: IO ()
main = print (zeros @3 @2)
which returns:
(matrix
[ 0.0, 0.0
, 0.0, 0.0
, 0.0, 0.0 ] :: L 3 2)
If you have feedback or find any mistakes, feel free drop an issue on Github or to email me at nicolas.audinet@chalmers.se !