On Mon, Feb 10, 2014 at 2:49 PM, Matthew Brett <matthew.br...@gmail.com>wrote:
> 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? > > No. Skipper
_______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion