[Numpy-discussion] deprecate numpy.matrix
josef.pktd at gmail.com
josef.pktd at gmail.com
Tue Feb 11 11:55:42 EST 2014
On Tue, Feb 11, 2014 at 11:25 AM, Matthew Brett <matthew.brett at gmail.com>wrote:
> On Tue, Feb 11, 2014 at 4:16 AM, Jacco Hoekstra - LR
> <J.M.Hoekstra at 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
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.
fast prototyping for code that is, at most used, for a few papers.
(just to avoid misunderstanding: there are also econometricians that are
"developers", and write code that is intended for reuse.)
(but maybe numpy users are all "developers")
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