On Fri, Apr 8, 2016 at 2:52 PM, <josef.p...@gmail.com> wrote: > > > On Fri, Apr 8, 2016 at 3:55 PM, Charles R Harris < > charlesr.har...@gmail.com> wrote: > >> >> >> On Fri, Apr 8, 2016 at 12:17 PM, Chris Barker <chris.bar...@noaa.gov> >> wrote: >> >>> On Fri, Apr 8, 2016 at 9:59 AM, Charles R Harris < >>> charlesr.har...@gmail.com> wrote: >>> >>>> Apropos column/row vectors, I've toyed a bit with the idea of adding a >>>> flag to numpy arrays to indicate that the last index is one or the other, >>>> and maybe neither. >>>> >>> >>> I don't follow this. wouldn't it ony be an issue for 1D arrays, rather >>> than the "last index". Or maybe I'm totally missing the point. >>> >>> But anyway, are (N,1) and (1, N) arrays insufficient for representing >>> column and row vectors for some reason? If not -- then we have a way to >>> express a column or row vector, we just need an easier and more obvious way >>> to create them. >>> >>> *maybe* we could have actual column and row vector classes -- they would >>> BE regular arrays, with (1,N) or (N,1) dimensions, and act the same in >>> every way except their __repr__. and we're provide handy factor functions >>> for them. >>> >>> These were needed to complete the old Matrix class -- which is no longer >>> needed now that we have @ (i.e. a 2D array IS a matrix) >>> >> >> One problem with that approach is that `vrow @ vcol` has dimension 1 x 1, >> which is not a scalar. >> > > I think it's not supposed to be a scalar, if @ breaks on scalars > > `vrow @ vcol @ a >
It's supposed to be a scalar and the expression should be written `vrow @ vcol * a`, although parens are probably desireable for clarity `(vrow @ vcol) * a`. Chuck
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