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. Cheers, Matthew _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
