On 5/13/07, Dave P. Novakovic <[EMAIL PROTECTED]> wrote:
They are very large numbers indeed. Thanks for giving me a wake up call. Currently my data is represented as vectors in a vectorset, a typical sparse representation. I reduced the problem significantly by removing lots of noise. I'm basically recording traces of a terms occurrence throughout a corpus and doing an analysis of the eigenvectors. I reduced my matrix to 4863 x 4863 by filtering the original corpus. Now when I attempt svd, I'm finding a memory error in the svd routine. Is there a hard upper limit of the size of a matrix for these calculations?
I get the same error here with linalg.svd(eye(5000)), and the memory is indeed gone. Hmm, I think it should work, although it is sure pushing the limits of what I've got: linalg.svd(eye(1000)) works fine. I think 4GB should be enough if your memory limits are set high enough. Are you trying some sort of principal components analysis? <snip> Chuck
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