> One thing to note is that dot uses optimized atlas if available, which > makes it quite faster than equivalent operations you would do using > purely numpy. I doubt that's the reason here, since the arrays are > small, but that's something to keep in mind when performances matter: > use dot wherever possible, it is generally faster than prod/sum,
This is quite true; I once had a very large matrix (600 x 200,000) that I needed to normalize. Using .sum( ) and /= took about 30 minutes. When I switched to using dot( ) to do the same operation (matrix multiplication with a vector of 1's, then turning that into a diagonal matrix and using dot() again to normalize it), it dropped the computation time down to about 2 minutes. Most of the gain was likely due to ATLAS using all the cores and numpy only using 1, but I was still impressed. --Hoyt ++++++++++++++++++++++++++++++++++++++++++++++++ + Hoyt Koepke + University of Washington Department of Statistics + http://www.stat.washington.edu/~hoytak/ + hoy...@gmail.com ++++++++++++++++++++++++++++++++++++++++++ _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
