[Numpy-discussion] KeepDims flag?
Charles R Harris
Mon Apr 22 12:00:06 CDT 2013
On Sun, Apr 21, 2013 at 4:59 AM, Sebastian Berg
> just something that has been spooking around in my mind. Considering
> that matrix indexing does not really support fancy indexing, I was
> wondering about introducing a KeepDims flag. Maybe it is not worth it,
> at least not unless other subclasses could make use of it, too. And a
> big reason for it being not worth the trouble is probably that you could
> not use it inside the current matrix class because it would i.e. break
> workarounds for ufunc reductions (like np.multiply.reduce(a, axis=1).T,
> as the .T would be unnecessary).
> Such a flag could only be set (not unset) and never be set on base class
> arrays (basically it should be set by array_finalize). If set it would
> toggle ufunc reductions to always use keepdims (unless the reduction is
> to a scalar, maybe). And the same thing for indexing (meaning that some
> fancy indices and np.newaxis would just error out), though axes added by
> broadcasting should be caught by the subclass itself.
> That way, a matrix-like class would normally have a 1:1 mapping of old
> to new axes (even if they might be transposed or elements arbitrarily
> shuffled), and does not have to do magic to guess where to add the
> missing one (instead the magic is done in the core, where it is actually
> easier to implement).
> Anyway, as I never use matrix I personally have no real use for it, but
> I thought I would throw the thought out there. For starters one might
> rather think about something specific to indexing.
I'd be hesitant to add another flag. Perhaps a better direction to go would
be to add row/column vectors, something that has been much discussed. There
is also a keepdims flag for reduce operations that is a fairly new addition
that I don't think has been exploited in the matrix class. ISTR discussion
of adding a facility for subclasses to intercept ufunc calls, or something
along those lines which would help with that, both for matrix and for
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