Hi, On Tue, Feb 11, 2014 at 8:55 AM, <josef.p...@gmail.com> wrote: > > > On Tue, Feb 11, 2014 at 11:25 AM, Matthew Brett <matthew.br...@gmail.com> > wrote: >> >> Hi, >> >> On Tue, Feb 11, 2014 at 4:16 AM, Jacco Hoekstra - LR >> <j.m.hoeks...@tudelft.nl> wrote: >> > For our students, the matrix class is really appealing as we use a lot >> > of linear algebra and expressions with matrices simply look better with an >> > operator instead of a function: >> > >> > x=A.I*b >> > >> > looks much better than >> > >> > x = np.dot(np.linalg.inv(A),b) >> >> Yes, but: >> >> 1) as Alan has mentioned, the dot method helps a lot. >> >> import numpy.linalg as npl >> >> x = npl.inv(A).dot(b) >> >> 2) Overloading the * operator means that you've lost * to do >> element-wise operations. MATLAB has a different operator for that, >> '.*' - and it's very easy to forget the dot. numpy makes this more >> explicit - you read 'dot' as 'dot'. >> >> > And this gets worse when the expression is longer: >> > >> > x = R.I*A*R*b >> > >> > becomes: >> > >> > x = np.dot( np.linalg.inv(R), np.dot(A, np.dot(R, b))) >> >> x = npl.inv(R).dot(A.dot(R.dot(b)) >> >> > Actually, not being involved in earlier discussions on this topic, I was >> > a bit surprised by this and do not see the problem of having the matrix >> > class as nobody is obliged to use it. I tried to find the reasons, but did >> > not find it in the thread mentioned. Maybe someone could summarize the main >> > problem with keeping this class for newbies on this list like me? >> > >> > Anyway, I would say that there is a clear use for the matrix class: >> > readability of linear algebra code and hence a lower chance of errors, so >> > higher productivity. >> >> Yes, but it looks like there are not any developers on this list who >> write substantial code with the np.matrix class, so, if there is a >> gain in productivity, it is short-lived, soon to be replaced by a >> cost. > > > selection bias ! > > I have seen lots of Matlab and GAUSS code written by academic > econometricians that have been programming for years but are not > "developers", code that is "inefficient" and numerically not very stable but > looks just like the formulas.
Yes, I used to use matlab myself. There is certainly biased sampling on this list, so it's very difficult to say whether there is a large constituency of np.matrix users out there, it's possible. I hope not, because I think they would honestly be better served with ndarray even if some of the lines in their script don't look quite as neat. But in any case, I still don't think that dropping np.matrix is an option in the short or even the medium term. The discussion - I think - is whether we should move towards standardizing to ndarray semantics for clarity and simplicity of future development (and teaching), Cheers, Matthew _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
