[Numpy-discussion] curious problem with SVD

[Arnar Flatberg]

On Fri, Jul 25, 2008 at 9:39 PM, Keith Goodman <kwgoodman@gmail.com> wrote:

> On Fri, Jul 25, 2008 at 12:36 PM, Keith Goodman <kwgoodman@gmail.com>
> wrote:
> > On Fri, Jul 25, 2008 at 12:32 PM, Frank Lagor <dfranci@seas.upenn.edu>
> wrote:
> >> Perhaps I do not understand something properly, if so could someone
> please
> >> explain the behavior I notice with numpy.linalg.svd when acting on
> arrays.
> >> It gives the incorrect answer, but works fine with matrices.  My numpy
> is
>
> > '*' does element-by-element multiplication for arrays but matrix
> > multiplication for mat
> >> n.dot(V, n.dot(n.diag(D), W.transpose()))  # That's hard to read!


Just two small points:

1.) Broadcasting may be easier on the eye ... well, atleast when you have
gotten used to it
Then the above is np.dot(V*D, W)

2.) Also, note that the right hand side eigenvectors in numpy's svd routine
is ordered by rows!
Yes, I know this is confusing as it is different from just about any other
linear algebra software out there, but the documentation is clear. It is
also a little inconsistent with eig and eigh, some more experienced user can
probably answer on why it is like that?

Arnar



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