On Fri, Jul 25, 2008 at 9:39 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

On Fri, Jul 25, 2008 at 12:36 PM, Keith Goodman <kwgoodman@gmail.com> wrote: 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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