Hitting the nail one the head! sounds like your solution (I mean numpy arrays with CDF elements, which I didn't know was possible) is going to be perfect for me.
Thank you so much! On 13 fév, 01:30, Nils Bruin <nbr...@sfu.ca> wrote: > On Feb 12, 1:39 pm, Pierre <pierre.guil...@gmail.com> wrote:> i think zz > above might still be considered as a 1 x 1 matrix instead > > of a complex number, somehow, and this may be slowing things down. > > No, that's not the problem. It's simply that numpy's default complex > number type is apparently a bit slower for individual element > arithmetic. It may well be that you're mainly measuring overhead, > though, so you should really test in a more representative situation > before committing to a particular implementation choice. numpy does > allow arbitrary types in its arrays. I doubt they're as optimized as > its own types, but you can try: > > sage: A= MatrixSpace(CDF, 2).random_element() > sage: B= MatrixSpace(CDF, 2).random_element() > sage: %timeit A*B > 625 loops, best of 3: 11.8 µs per loop > sage: import numpy > sage: AA= numpy.array(A); BB= numpy.array(B) > sage: %timeit AA.dot(BB) > 625 loops, best of 3: 1.28 µs per loop > sage: AAA= numpy.array(A,dtype=type(A[0,0])); BBB= > numpy.array(B,dtype=type(B[0,0])) > sage: %timeit AAA.dot(BBB) > 625 loops, best of 3: 2.33 µs per loop > sage: z=A[0,0] > sage: %timeit z*z > 625 loops, best of 3: 101 ns per loop > sage: zz=AA[0,0] > sage: %timeit zz*zz > 625 loops, best of 3: 253 ns per loop > sage: zzz=AAA[0,0] > sage: %timeit zzz*zzz > 625 loops, best of 3: 107 ns per loop > sage: type(z); type(zz); type(zzz) > <type 'sage.rings.complex_double.ComplexDoubleElement'> > <type 'numpy.complex128'> > <type 'sage.rings.complex_double.ComplexDoubleElement'> -- To post to this group, send email to sage-support@googlegroups.com To unsubscribe from this group, send email to sage-support+unsubscr...@googlegroups.com For more options, visit this group at http://groups.google.com/group/sage-support URL: http://www.sagemath.org
