[Numpy-discussion] any better way to normalize a matrix
Fri Dec 28 20:01:43 CST 2007
On 28/12/2007, Christopher Barker <Chris.Barker@noaa.gov> wrote:
> Anne Archibald wrote:
> > Numpy provides ufuncs as general powerful tools for operating on
> > matrices. More can be added relatively easily, they provide not just
> > the basic "apply" operation but also "outer" and others. Adding
> > another way to accomplish the same operation just adds bulk to numpy.
> Maybe so, but if it were up to me, I'd have just the methods, and not
> the functions -- I like namespaces and OO design. I've always thought it
> was a spurious argument that these kinds of functions can operate on
> things that aren't numpy arrays -- it's true, but they all return
> arrays, to it's not like they really are universal.
It doesn't make a whole lot of sense to me to have n-ary methods
(though I don't think we currently have any ternary ufuncs). How would
you accomodate all the other operations ufuncs support?
The big problem with methods, it seems to me, is that if I want to add
a new ufunc, it's relatively easy. In fact, with vectorize() I do that
all the time (with all sorts of arities). But adding compiled ufuncs
in a new module is relatively straightforward; it's not at all clear
how a separate package could ever add new methods to the ndarray
object. This is not hypothetical - scipy.special introduces many
mathematical functions that either are or should be ufuncs.
As for whether this is "object-oriented", well, ufuncs are objects
with a variety of methods; one could productively inherit from them
(at least in principle, though I've never had occasion to).
Anyway, I don't know that we need to rehash the old discussion. I just
wanted to point out the value of ufuncs as objects.
More information about the Numpy-discussion