Hi, On Mon, Feb 10, 2014 at 11:44 AM, <josef.p...@gmail.com> wrote: > > > 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 - as a data point - does statsmodels use np.matrix? Cheers, Matthew _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
