Re: [Numpy-discussion] Matlab page on scipy wiki

[Fernando P. <Fer...@co...>]

[ regarding the way of describing arrays vs. matlab's matrices, and the use of 
'dimension', 'rank', 'number of axes', etc.]

Let's not introduce new terms where none are needed.  These concepts have had 
well-established names (tensor rank and matrix rank) for a long time.  It may 
be a good idea to add a local glossary page reminding anyone of what the 
definitions are, but for as long as I remember reading literature on these 
topics, the two terms have been fully unambiguous.

A numpy array with length(array.shape)==d is closest to a rank d tensor (minus 
the geometric co/contravariance information).  A d=2 array can be used to 
represent a matrix, and linear algebra operations can be performed on it; if a 
Matrix object is built out of it, a number of things (notably the * operator) 
are then performed in the linear algebra sense (and not element-wise).  The 
rank of a matrix has nothing to do with the shape attribute of the underlying 
array, but with the number of non-zero singular values (and for floating-point 
matrices, is best defined up to a given tolerance).

Since numpy is a n-dimensional array package, it may be convenient to 
introduce a matrix_rank() routine which does what matlab's rank() for 2-d 
arrays and matrices, while raising an error for any other shape.  This would 
also make it explicit that this operation is only well-defined for 2-d objects.

My 1e-2,

f