Re: [Numpy-discussion] pickable ndarray subclass

2007-04-16 Thread Christian K
Stefan van der Walt wrote: > Hi Christiaan > > On Sun, Apr 15, 2007 at 02:03:49PM +0900, Christian K wrote: >> could someone please provide example code for how to make a subclassed >> ndarray >> pickable? I don't quite understand the docs of ndarray.__reduce__. >> My subclassed ndarray has just

Re: [Numpy-discussion] building numpy with atlas on ubuntu edgy

2007-04-16 Thread Christian K
David Cournapeau wrote: > On Ubuntu and debian, you do NOT need any site.cfg to compile numpy with > atlas support. Just install the package atlas3-base-dev, and you are > done. The reason is that when *compiling* a software which needs atlas, > the linker will try to find libblas.so in /usr/lib

Re: [Numpy-discussion] Fastest distance matrix calc

2007-04-16 Thread Bill Baxter
Here's a bunch of dist matrix implementations and their timings. The upshot is that for most purposes this seems to be the best or at least not too far off (basically the cookbook solution Kier posted) def dist2hd(x,y): """Generate a 'coordinate' of the solution at a time""" d = npy.zeros((

Re: [Numpy-discussion] Fastest distance matrix calc

2007-04-16 Thread Keir Mierle
On 4/13/07, Timothy Hochberg <[EMAIL PROTECTED]> wrote: > On 4/13/07, Bill Baxter <[EMAIL PROTECTED]> wrote: > > I think someone posted some timings about this before but I don't recall. http://aspn.activestate.com/ASPN/Cookbook/Python/Recipe/498246 [snip] > I'm going to go out on a limb and cont

[Numpy-discussion] NumPy benchmark

2007-04-16 Thread rex
I'm about to build numpy using Intel's MKL 9.1 beta and want to compare it with the version I built using MKL 8.1. Is the LINPACK benchmark the most appropriate? Thanks, -rex -- Pollytheism: n., the belief that there are many gods, all of them parrots. ___

Re: [Numpy-discussion] Fastest distance matrix calc

2007-04-16 Thread Christopher Barker
Timothy Hochberg wrote: > results = empty([M, N], float) > # You could be fancy and swap axes depending on which array is larger, but > # I'll leave that for someone else > for i, v in enumerate(x): > results[i] = sqrt(sum((v-y)**2, axis=-1)) you can probably use numpy.hypot(v-y) to speed thi