On Wed, Apr 28, 2010 at 10:08 AM, Dag Sverre Seljebotn <dagss@student.matnat.uio.no> wrote:
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.

Yeah. Masked arrays have similar problems. Pierre has done so much work to have masked versions of the various functions that it might as well be a standalone class.