2009/10/20 <josef.p...@gmail.com>: > 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)
It seems like this would be a good place to apply numpy's higher-dimensional ufuncs: what you want seems to just be the vector inner product, broadcast over all other dimensions. In fact I believe this is implemented in numpy as a demo: numpy.umath_tests.inner1d should do the job. Anne _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
