On Mon, Jun 8, 2009 at 14:10, Alan G Isaac<ais...@american.edu> wrote: >>> Going back to Alan Isaac's example: >>> 1) beta = (X.T*X).I * X.T * Y >>> 2) beta = np.dot(np.dot(la.inv(np.dot(X.T,X)),X.T),Y) > > > Robert Kern wrote: >> 4) beta = la.lstsq(X, Y)[0] >> >> I really hate that example. > > > Remember, the example is a **teaching** example.
I know. Honestly, I would prefer that teachers skip over the normal equations entirely and move directly to decomposition approaches. If you are going to make them implement least-squares from more basic tools, I think it's more enlightening as a student to start with the SVD than the normal equations. > I actually use NumPy in a Master's level math econ course > (among other places). As it happens, I do get around to > explaining why using an explicit inverse is a bad idea > numerically, but that is entirely an aside in a course > that is not concerned with numerical methods. It is > concerned only with mastering a few basic math tools, > and being able to implement some of them in code is > largely a check on understanding and precision (and > to provide basic background for future applications). > Having them use lstsq is counterproductive for the > material being covered, at least initially. > > A typical course of this type uses Excel or includes > no applications at all. So please, > show a little gratitude. ;-) If it's not a class where they are going to use what they learn in the future to write numerical programs, I really don't care whether you teach it with numpy or not. If it *is* such a class, then I would prefer that the students get taught the right way to write numerical programs. -- Robert Kern "I have come to believe that the whole world is an enigma, a harmless enigma that is made terrible by our own mad attempt to interpret it as though it had an underlying truth." -- Umberto Eco _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
