2009/3/28 Geoffrey Irving <irv...@naml.us>: > On Sat, Mar 28, 2009 at 12:47 AM, Robert Kern <robert.k...@gmail.com> wrote: >> 2009/3/27 Charles R Harris <charlesr.har...@gmail.com>: >>> >>> On Fri, Mar 27, 2009 at 4:43 PM, Robert Kern <robert.k...@gmail.com> wrote: >>>> >>>> On Fri, Mar 27, 2009 at 17:38, Bryan Cole <br...@cole.uklinux.net> wrote: >>>> > I have a number of arrays of shape (N,4,4). I need to perform a >>>> > vectorised matrix-multiplication between pairs of them I.e. >>>> > matrix-multiplication rules for the last two dimensions, usual >>>> > element-wise rule for the 1st dimension (of length N). >>>> > >>>> > (How) is this possible with numpy? >>>> >>>> dot(a,b) was specifically designed for this use case. >>> >>> I think maybe he wants to treat them as stacked matrices. >> >> Oh, right. Sorry. dot(a, b) works when a is (N, 4, 4) and b is just >> (4, 4). Never mind. > > It'd be great if this operation existed as a primitive. What do you > think would be the best way in which to add it? One option would be > to add a keyword argument to "dot" giving a set of axes to map over. > E.g., > > dot(a, b, map=0) = array([dot(u,v) for u,v in zip(a,b)]) # but in C > > "map" isn't a very good name for the argument, though.
I think the right long-term solution is to make dot (and some other linear algebra functions) into "generalized ufuncs", so that when you dot two multidimensional objects together, they are treated as arrays of two-dimensional arrays, broadcasting is done on all but the last two dimensions, and then the linear algebra is applied "elementwise". This covers basically all "stacked matrices" uses in a very general way, but would require some redesigning of the linear algebra system - for example, dot() currently works on both two- and one-dimensional arrays, which can't work in such a setting. The infrastructure to support such generalized ufuncs has been added to numpy, but as far as I know no functions yet make use of it. Anne > Geoffrey > _______________________________________________ > 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
