On Wed, May 2, 2012 at 11:26 PM, Francesc Alted <franc...@continuum.io> wrote: > On 5/2/12 4:20 PM, Nathaniel Smith wrote: >> On Wed, May 2, 2012 at 9:53 PM, Francesc Alted<franc...@continuum.io> wrote: >>> On 5/2/12 11:16 AM, Wolfgang Kerzendorf wrote: >>>> Hi all, >>>> >>>> I'm currently writing a code that needs three dimensional data (for the >>>> physicists it's dimensions are atom, ion, level). The problem is that not >>>> all combinations do exist (a sparse array). Sparse matrices in scipy only >>>> deal with two dimensions. The operations that I need to do on those are >>>> running functions like exp(item/constant) on all of the items. I also want >>>> to sum them up in the last dimension. What's the best way to make a class >>>> that takes this kind of data and does the required operations fast. Maybe >>>> some phycisists have implemented these things already. Any thoughts? >>> Curiously enough, I have recently been discussing with Travis O. about >>> how to represent sparse matrices with complete generality. One of the >>> possibilities is to use what Travis call "synthetic dimensions". The >>> idea behind it is easy: use a table with as many columns as dimensions, >>> and add another one for the actual values of the array. For a 3-D >>> sparse array, this looks like: >>> >>> dim0 | dim1 | dim2 | value >>> ========================== >>> 0 | 0 | 0 | val0 >>> 0 | 10 | 100 | val1 >>> 20 | 5 | 202 | val2 >> This coordinate format is also what's used by the MATLAB Tensor >> Toolbox. They have a paper justifying this choice and describing some >> tricks for how to work with them: >> http://epubs.siam.org/sisc/resource/1/sjoce3/v30/i1/p205_s1 >> (Spoiler: you use a lot of sort operations. Conveniently, timsort >> appears to be perfectly adapted for their algorithmic requirements.) > > Uh, I do not have access to the article, but yes, sorting makes a lot of > sense for these scenarios (this is why I was suggesting using indexes, > which is sort of sorting too).
Doh, sorry. Sticking the title into google scholar gives me this: http://csmr.ca.sandia.gov/~tgkolda/pubs/bibtgkfiles/SIAM-67648.pdf - N _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
