Emanuele Olivetti wrote: <snip> > > This solution is super-fast, stable and use little memory. > It is based on the fact that: > (x-y)^2*w = x*x*w - 2*x*y*w + y*y*w > > For size1=size2=dimensions=1000 requires ~0.6sec. to compute > on my dual core duo. It is 2 order of magnitude faster than my > previous solution, but 1-2 order of magnitude slower than using > C with weave.inline. > > Definitely good enough for me. > > > Emanuele
Reading this thread, I remembered having tried scipy's sandbox.rbf (radial basis function) to interpolate a pretty large, multidimensional dataset, to fill in the missing data points. This however failed soon with out-of-memory errors, which, if I remember correctly, came from the pretty straightforward distance calculation between the different data points that is used in this package. Being no math wonder, I assumed that there simply was no simple way to calculate distances without using much memory, and ended my rbf experiments. To make a story short: correct me if I am wrong, but might it be an idea to use the above solution in scipy.sandbox.rbf? Vincent. _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
