Sasha wrote:
On 8/25/06, Charles R Harris <charlesr.harris@gmail.com> wrote:
Matrix rank has nothing to do with numpy rank. Numpy rank is simply the number of indices required to address an element of an ndarray. I always thought a better name for the Numpy rank would be dimensionality, but like everything else one gets used to the numpy jargon, it only needs to be defined someplace for what it is.
That's my point exactly. The rank(2) definition was added by Sebastian Haase who advocates the use of the term "ndims" instead of "rank". I've discussed the use of "dimentionality' in the preamble. Note that ndims stands for the number of dimensions, not dimensionality.
I don't want to remove rank(2) without hearing from Sebastian first and I appreciate his effort to improve the glossary. Maybe we shold add a "matrix rank" entry instead.
My phasing is certainly suboptimal (I only remember the German wording - and even that only faintly - "linear independent" !?) But I put it in, remembering the discussion in "numpy" on *why* array.rank (numarray) was changed to array.ndim (numpy) I just thought this page might be a good place to 'discourage usage of badly-defined terms' or at least give the argument for "ndim". [ OK: it's not "badly" defined: but there are two separate camps on *what* it should mean --- ndim is clear.] BTW: Does the "matrix" class have m.rank attribute !? Cheers, Sebastian.