There *is* this in NumPy (which is the main use-case for this PEP): numpy.matrix. But it causes headaches for everyone who uses it.
Aaron Meurer On Thu, Feb 27, 2014 at 3:42 PM, Alexey U. Gudchenko <pr...@goodok.ru> wrote: > > > On 25.02.2014 20:31, Aaron Meurer wrote: >> >> Some people on this list might be interested in this. Some folks are >> giving another shot at writing a PEP to add a new operator to Python >> for matrix multiplication. The discussion is at >> https://github.com/numpy/numpy/pull/4351. >> >> Those of you who work with tensors may want to have a look as well. >> There is talk to extending the operator to higher dimensional arrays, >> but I feel that the discussion would be easier if it were formalized >> into the language of tensor contractions. >> >> Aaron Meurer >> > > Usually (in the one expression) we use only one type of the binary operator > for the multiplication. > > And the example in the PEP also use only one operator '@' in some context > without mixing with '*' operator in this expression (for the same class): > > S = (H @ beta - r).T @ inv(H @ V @ H.T) @ (H @ beta - r) > > Moreover, I can't imagine easy the example when two types of the binary > operators can be used simultaneously for the same class in one expression > (or even in one block of expressions). > > If it is so, then it is possible maintain code and expressions to be > readable with the help of the switching of the context for this "*" > operator. Just use the python's dynamical polymorphism. > > Example: > > Suppose we have a class of the array (class M). See attachment for details. > > And we want to use sometimes the elemente-wise product, or sometimes Cauchy > product. > > > And we want to write down expressions more readable for both cases > > a = M([1, 2, 3]) > b = M([5, 7, 11]) > c = M([1, 2, 4]) > > Element wise product: > > >>> a * b * c > [5, 28, 132] > > Cauchy product: > > # switch to cauchy_product > >>> M.mul_current = M.mul_cauchy_product > > >>> a * b * c > [5, 27, 94] > > > We can do it by switching: M.mul_current = M.mul_cauchy_product > > (It is possible to use class method if it have a few methods to be switched: > __rmul__, for example) > > Disadvantage of this: > > We can forget to switch '*' operator to the default behavior after usage, > or this situation may occur as the result of some exception. > > But may be it is possible to combine switching with the "with .. as" > statement. (Some __enter__, __exit__ methods?) > > > Element wise product: > >>> a * b * c > [5, 28, 132] > > Cauchy product: > > > >>> with M.use_cauhcy_product() as dummy: > >>> a * b * c > [5, 27, 94] > > > > The last example is not implemented in the attachment. But I don't see the > big problems, expect that "with...as" statement usually uses for the > object's context, not classes. > > As my opinion (conclusion) I don't think that PEP for '@' operator is timely > and not even convenient. > > -- > Alexey Gudchenko > > > -- > You received this message because you are subscribed to the Google Groups > "sympy" group. > To unsubscribe from this group and stop receiving emails from it, send an > email to sympy+unsubscr...@googlegroups.com. > To post to this group, send email to sympy@googlegroups.com. > Visit this group at http://groups.google.com/group/sympy. > To view this discussion on the web visit > https://groups.google.com/d/msgid/sympy/530FB132.8030506%40goodok.ru. > > For more options, visit https://groups.google.com/groups/opt_out. -- You received this message because you are subscribed to the Google Groups "sympy" group. To unsubscribe from this group and stop receiving emails from it, send an email to sympy+unsubscr...@googlegroups.com. To post to this group, send email to sympy@googlegroups.com. Visit this group at http://groups.google.com/group/sympy. To view this discussion on the web visit https://groups.google.com/d/msgid/sympy/CAKgW%3D6Ka5qQAyz5v8E61ZcHwudXiyrcWcqU0cq3t3ofTqgB2iA%40mail.gmail.com. For more options, visit https://groups.google.com/groups/opt_out.
