The recent message by Ferenc.Pintye (how does one pronounce that BTW) reminded me of something I've been meaning to discuss: I think we can do a better job dealing with stacked matrices. By stacked matrices I mean 3 (or more) dimensional arrays where the last two dimensions are considered to be matrices. Let us suppose that one wants to find the eigenvalues of 200,000 3x3 matrices as Ferenc does. At present you have to call linalg.eigvals 200,000 time; the overhead is going to kill you. I've run into this problem before myself in the past (and kludged my way around it), so the problem is not limited to Ferenc.
I see no reason that this could not be fixed for the linalg functions. The details would vary from function to function, but for eigvals, for example, when passed an NxMxM array, it would return an NxM array of eigenvalues. One can actually get pretty far just modifying the python functions in linalg.py, or at least one could in numarray and I'm guessing that numpy is similar. That is because there is a fair amount of overhead in setting up the function calls and this can all be done just once. I have code lying around that does this for solve, inverse and determinant that works with numarray that would probably not be hard to adapt to numpy if we were interested in making this change of interface to linalg. Another place that this same effect has bitten me in the past is with dot. Dot does work on higher dimensional arrays, but it attempts to do a tensor product of sorts. While this sounds nifty, in practice I've found it less than useful. However, because dot computes both scalar and matrix products it's not clear that a set of rules that was more or less backwards compatible and that extended to higher dimensions in a more useful way than at present could be crafted. Probably something with different behavior, perhaps that always performed matrix products on the last two axes, could be added with a name other than dot. This avoids backwards compatibility issues as well. None of this stuff is worth holding up the the release 1.0 over, but I though I would mention it while it was on my mind. -tim ------------------------------------------------------------------------- Take Surveys. Earn Cash. Influence the Future of IT Join SourceForge.net's Techsay panel and you'll get the chance to share your opinions on IT & business topics through brief surveys -- and earn cash http://www.techsay.com/default.php?page=join.php&p=sourceforge&CID=DEVDEV _______________________________________________ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
