Xiao-Yong Jin wrote:
Tomas Andersson <[EMAIL PROTECTED]> writes:
You can never go wrong with a good old fashioned hand written tail recursion
when you're in doubt, they are pretty much the closest thing to for-loops
there is in haskell and should be easy to grok for Imperative programmers and
usually produce really fast code.
sum vect = let d = dim vect
in sum' (d - 1) 0
where sum' 0 s = s + (vect @> 0)
sum' index s = sum' (index - 1) (s + (vect @> index))
Do I need the strict version of sum'? Put something like
sum' a b | a `seq` b `seq` False = undefined
Or ghc will optimize it automatically? I always don't know
when such optimization is useful.
I don't know the hmatrix api very well, but if there is a function for
computing the inner product between two vectors you could always do something
like the following meta code:
sum v = innerProduct v <1,1,1,1,1,1>
I just doubt the efficiency here. If v is a very large
vector, I guess the allocation time of that intermediate
vector [1,1..] is not small. But it is just my guess.
My experience is that Haskell allocation time is very fast and usually
negligible in most non trivial matrix computations.
A good thing about
sum v = constant 1 (dim v) <.> v
is that a constant vector is efficiently created internally (not from an
intermediate Haskell list), and the inner product will be computed by
the possibly optimized blas version available in your machine.
You can also write simple definitions like the next one for the average
of the rows of a matrix as a simple vector-matrix product:
mean m = w <> m
where w = constant k r
r = rows m
k = 1 / fromIntegral r
I prefer high level "index free" matrix operations to C-like code.
Definitions are clearer, have less bugs, and are also more efficient if
you use optimized numerical libs.
In any case, many algorithms can be solved by the recursive approach
described by Tomas.
Alberto
Xiao-Yong
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