On Mon, Jun 8, 2009 at 3:33 PM, Robert Kern<robert.k...@gmail.com> wrote: > 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. >
I started in such a class (with Dr. Isaac as a matter of fact). I found the use of Python with Numpy to be very enlightening for the basic concepts of linear algebra. I appreciated the simple syntax of matrices at the time as a gentler learning curve since my background in programming was mainly at a hobbyist level. I then went on to take a few econometrics courses where we learned the normal equations. Now a few years later I am working on scipy.stats as a google summer of code project, and I am learning why a SVD decomposition is much more efficient (an economist never necessarily *needs* to know what's under the hood of their stats package). The intuition for the numerical methods was in place, as well as the basic familiarity with numpy/scipy. So I would not discount this approach too much. People get what they want out of anything, and I was happy to learn about Python and Numpy/Scipy as alternatives to proprietary packages. And I hope my work this summer can contribute even a little to making the project an accessible alternative for researchers without a strong technical background. Skipper _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
