On Sun, Oct 18, 2009 at 6:06 AM, Gary Ruben <gru...@bigpond.net.au> wrote: > Hi Gaël, > > If you've got a 1D array/vector called "a", I think the normal idiom is > > np.dot(a,a) > > For the more general case, I think > np.tensordot(a, a, axes=something_else) > should do it, where you should be able to figure out something_else for > your particular case.
Is it really possible to get the same as np.sum(a*a, axis) with tensordot if a.ndim=2 ? Any way I try the "something_else", I get extra terms as in np.dot(a.T, a) Josef > > Gary R. > > Gael Varoquaux wrote: >> On Sat, Oct 17, 2009 at 07:27:55PM -0400, josef.p...@gmail.com wrote: >>>>>> Why aren't you using logaddexp ufunc from numpy? >> >>>>> Maybe because it is difficult to find, it doesn't have its own docs entry. >> >> Speaking of which... >> >> I thought that there was a readily-written, optimized function (or ufunc) >> in numpy or scipy that calculated the sum of squares for an array >> (possibly along an axis). However, I cannot find it. >> >> Is there something similar? If not, it is not the end of the world, the >> operation is trivial to write. >> >> Cheers, >> >> Gaël > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion > _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion