sorry if this a duplicate, it seems that my last mail got lost... is there something to take care about when sending a mail to the numpy mailing list?
On Tue, Sep 22, 2009 at 9:42 AM, Sebastian Walter <sebastian.wal...@gmail.com> wrote: > This is somewhat similar to the question about fixed-point arithmetic > earlier on this mailing list. > > I need to do computations on arrays whose elements are truncated polynomials. > At the momement, I have implemented the univariate truncated > polynomials as objects of a class UTPS. > > The class basically looks like this: > > class UTPS: > def __init__(self, taylor_coeffs): > """ polynomial x(t) = tc[0] + tc[1] t + tc[2] t^2 + tc[3] > t^3 + ... """ > self.tc = numpy.asarray(taylor_coeffs) > > def __add__(self, rhs): > return UTPS(self.tc + rhs.tc) > > def sin(self): > # numpy.sin(self) apparently automatically calls self.sin() > which is very cool > > etc.... > > One can create arrays of UTPS instances like this: > x = numpy.array( [[UTPS([1,2]), UTPS([3,4])], [UTPS([0,1]), UTPS([4,3])]]) > > and perform funcs and ufuncs on it > > y = numpy.sum(x) > y = numy.sin(x) > y = numpy.dot(numpy.eye(2), x) > > This works out of the box, which is very nice. > > my question: > Is it possible to speed up the computation by defining a special dtype > for truncated polynomials? Especially when the arrays get large, > computing on arrays of objects is quite slow. I had a look at the > numpy svn trunk but couldn't find any clues. > > If you are interested, you can have a look at the full pre alpha > version code (BSD licence) at http://github.com/b45ch1/algopy . > > regards, > Sebastian > _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
