On Mon, Feb 10, 2014 at 2:12 PM, eat <e.antero.ta...@gmail.com> wrote:
> > > > On Mon, Feb 10, 2014 at 9:08 PM, alex <argri...@ncsu.edu> wrote: > >> On Mon, Feb 10, 2014 at 2:03 PM, eat <e.antero.ta...@gmail.com> wrote: >> > Rhetorical or not, but FWIW I'll prefer to take singular value >> decomposition >> > (u, s, vt= svd(x)) and then based on the singular values s I'll >> estimate a >> > "numerically feasible rank" r. Thus the diagonal of such hat matrix >> would be >> > (u[:, :r]** 2).sum(1). >> >> It's a small detail but you probably want svd(x, full_matrices=False) >> to avoid anything NxN. >> > Indeed. > I meant the entire diagonal not the trace of the projection matrix. My (not articulated) thought was that I use element wise multiplication together with dot products instead of the three dot products, however elementwise algebra is not very common in linear algebra based textbooks. The question is whether students and new user coming from `matrix` languages can translate formulas into code, or just copy formulas to code. (It took me a while to get used to numpy and take advantage of it's features coming from GAUSS and Matlab.) OT since the precense or absence of matrix in numpy doesn't affect me. Josef > > Thanks, > -eat > >> _______________________________________________ >> NumPy-Discussion mailing list >> NumPy-Discussion@scipy.org >> http://mail.scipy.org/mailman/listinfo/numpy-discussion >> > > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion > >
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