Hi Olivier, There was this idea posted on the Scipy-user list a while back: http://projects.scipy.org/pipermail/scipy-user/2008-August/017954.html but it doesn't look like he got anywhere with it, or even got a response. I just tried it and I observe the same behaviour. A quick look at the SciPy sources tells me there is something fishy. subroutine < tchar=s,d,c,z>gemm(m,n,k,alpha,a,b,beta,c,trans_a,trans_b,lda,ka,ldb,kb) ! c = gemm(alpha,a,b,beta=0,c=0,trans_a=0,trans_b=0,overwrite_c=0) ! Calculate C <- alpha * op(A) * op(B) + beta * C I don't read Fortran very well, but it seems to me as though the Fortran prototype doesn't match the python prototype. I'll poke around a little more, but in summary: there's no numpy- sanctioned way to specify an output array for a dot(), AFAIK. This is a bit of an annoyance, I agree, though I seem to remember Robert Kern offering a fairly compelling argument why it's hard. I just don't know what that argument is :) David On 18-Feb-09, at 4:06 AM, Olivier Grisel wrote:
Hi numpist people,
I discovered the ufunc and there ability to compute the results on preallocated arrays:
a = arange(10, dtype=float32) b = arange(10, dtype=float32) + 1 c = add(a, b, a) c is a True a array([ 1., 3., 5., 7., 9., 11., 13., 15., 17., 19.], dtype=float32)
My questions is : is there a way to have an equivalent for the dot product operation: I want atlas to build my dot products without allocating a temporary array and reuse a preallocated array of results. Suppose I have:
results = array((10, 3), dtype=float32) W = arange(6, dtype=float32).reshape((2, 3)) x = arange(20, dtype=float32).reshape((10, 2))
What I want is the equivalent of the following without the intermediate call to malloc:
results[:] = dot(x, W)
Any idea? I tried to introspect the various docstring of numpy core modules but I could not get any lead.
-- Olivier _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion