On Fri, Apr 20, 2012 at 11:45 AM, Chris Barker <chris.bar...@noaa.gov> wrote: > > On Fri, Apr 20, 2012 at 11:39 AM, Dag Sverre Seljebotn > <d.s.seljeb...@astro.uio.no> wrote: > > Oh, right. I was thinking "small" as in "fits in L2 cache", not small as > > in a few dozen entries.
Another example of a small array use-case: I've been using numpy for my research in multi-target tracking, which involves something like a bunch of entangled hidden markov models. I represent target states with small 2d arrays (e.g. 2x2, 4x4, ..) and observations with small 1d arrays (1 or 2 elements). It may be possible to combine a bunch of these small arrays into a single larger array and use indexing to extract views, but it is much cleaner and more intuitive to use separate, small arrays. It's also convenient to use numpy arrays rather than a custom class because I use the linear algebra functionality as well as integration with other libraries (e.g. matplotlib). I also work with approximate probabilistic inference in graphical models (belief propagation, etc), which is another area where it can be nice to work with many small arrays. In any case, I just wanted to chime in with my small bit of evidence for people wanting to use numpy for work with small arrays, even if they are currently pretty slow. If there were a special version of a numpy array that would be faster for cases like this, I would definitely make use of it. Drew _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
