On Fri, Aug 26, 2011 at 7:41 PM, Mark Janikas <mjani...@esri.com> wrote: > I wonder if my last statement is essentially the only answer... which I > wanted to avoid... > > Should I just use combinations of the columns and try and construct the > corrcoef() (then ID whether NaNs are present), or use the condition number to > ID the singularity? I just wanted to avoid the whole k! algorithm. >
This is a completely naive, off-the-top of my head reply, so most likely completely wrong. But wouldn't a Gram-Schmidt type process let you identify things here? You're effectively looking for n vectors that belong to an m-dimensional subspace with n>m. As you walk through the G-S process you could probably track the projections and identify when one of the vectors in the m-n set is 'emptied out' by the G-S projections, and would have the info of what it projected into. I don't remember the details of G-S so perhaps there's a really obvious reason why the above is dumb and doesn't work. But just in case it gets you thinking in the right direction... (and I'll learn something from the corrections) Cheers, f _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
