On Tue, 27 Mar 2007, Robert Kern apparently wrote:
Gram-Schmidt orthogonalization
Alan G Isaac wrote:
I take it from context that you consider it desirable to end up with a list of matrices?
Robert wrote:
Honestly, I don't care. You asked about iteration, and I gave you an example where it was important that the iterates were matrices (such that .T worked correctly). Please stop moving the goal posts.
It is really not my intent to be irritating or to move the posts. But I had two reasons to ask. First, your claim is false that this was important in your example. (To see this, replace M[1:] with M.A[1:].) So actually your code would work the same if iteration over matrices were producing arrays (after replacing M[0] with M[0,:] to match that case). Second, when I spoke of *desirability*, the output is relevant. Try nump.mat(ortho) to see what I mean. If iteration were to produce arrays, the outcome of implementing the algorithm (e.g., using ``dot``) would be I suggest more "desirable", in that numpy.mat(ortho) would work as hoped/expected. In this sense, the example perhaps speaks against your intent. You offer code that would work just fine if iteration yielded arrays. Apologies in advance if this again seems tangential to my original request. To me it does not.
If you really want iterates to be arrays, just use .A.
I have mentioned that several times. It is orthogonal to the design issue I raised. (I suggested that under the current behavior you do not usually end up with what you want when iterating over matrices.) But again, I recognize that I am alone in that view. I am just trying to understand why. Cheers, Alan Isaac