David Warde-Farley wrote:

Trying to debug code written by an undergrad working for a colleague of mine who ported code over from MATLAB, I am seeing an ugly melange of matrix objects and ndarrays that are interacting poorly with each other and various functions in SciPy/other libraries. In particular there was a custom minimizer function that contained a line "a * b", that was receiving an Nx1 "matrix" and a N-length array and computing an outer product. Hence the unexpected 6 GB of memory usage and weird results...

If this was in a library function of some sort, I think they should always call np.asarray on the input arguments. That converts matrices to normal arrays. It could have been Python lists-of-lists, other PEP 3118 objects -- in Python an object can be everything in general, and I think it is very proper for most reusable functions to either validate the type of their arguments or take some steps to convert. That said, I second that it would be good to deprecate the matrix class from NumPy. The problem for me is not the existance of a matrix class as such, but the fact that it subclasses np.ndarray and is so similar with it, breaking a lot of rules for OO programming in the process. (Example: I happen to have my own oomatrix.py which allows me to do P, L = (A * A.H).cholesky() y = L.solve_right(x) This works fine because the matrices don't support any NumPy operations, and so I don't confuse them. But it helps to have to habit to do np.asarray in reusable functions so that errors are caught early. I do this so that A above can be either sparse, dense, triangular, diagonal, etc. -- i.e. polymorphic linear algebra. On the other hand, they don't even support single-element lookups, although that's just because I've been to lazy to implement it. Iteration is out of the question, it's just not the level of abstraction I'd like a "matrix" to work at.) Dag Sverre

We've had this discussion before and it seems that the matrix class isn't going anywhere (I *really* wish it would at least be banished from the top-level namespace), but it has its adherents for pedagogical reasons. Could we at least consider putting a gigantic warning on all the functions for creating matrix objects (matrix, mat, asmatrix, etc.) that they may not behave quite so predictably in some situations and should be avoided when writing nontrivial code?

There are already such warnings scattered about on SciPy.org but the situation is so bad, in my opinion (bad from a programming perspective and bad from a new user perspective, asking "why doesn't this work? why doesn't that work? why is this language/library/etc. so stupid, inconsistent, etc.?") that the situation warrants steering people still further away from the matrix object.

I apologize for ranting, but it pains me when people give up on Python/NumPy because they can't figure out inconsistencies that aren't really there for a good reason. IMHO, of course.

David

David _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion

-- Dag Sverre