I just figured out that I had a few arrays that where taking up a bunch of the memory. That said I still wonder if there is a better way. *Vincent Davis 720-301-3003 * vincent@vincentdavis.net my blog <http://vincentdavis.net> | LinkedIn<http://www.linkedin.com/in/vincentdavis> On Sat, Mar 6, 2010 at 10:22 PM, Vincent Davis <vincent@vincentdavis.net>wrote:
I have arrays of 8-20 rows and 230,000 column, all the data is float64 I what to be able to find the difference in the correlation matrix between arrays let A and B be of size (10, 230000) np.corrcoef(a)-np.corrcoef(b)
I can't seem to do this with more than 10000 columns at a time because of memory limitations. (about 9GB usable to python) Is there a better way?
I also have problem finding the column means which is surprising to me, I was not able to get the column means for 10000 columns, but I can computer the corrcoeff ? np.mean(a, axis=0)
Do I just need to divide up the job or is there a better approach?
Thanks
*Vincent Davis 720-301-3003 * vincent@vincentdavis.net my blog <http://vincentdavis.net> | LinkedIn<http://www.linkedin.com/in/vincentdavis>