On Friday 05 of January 2007 19:29, Matt Knox wrote: > > On Friday 05 of January 2007 17:42, Matt Knox wrote: > > > --------------------------------------------- > > > Example 1 - exponential moving average: > > > > > > # naive brute force method... > > > def expmave(x, k): > > > result = numpy.array(x, copy=True) > > > for i in range(1, result.size): > > > result[i] = result[i-1] + k * (result[i] - result[i-1]) > > > return result > > > > > > # slicker method (if it worked, which it doesn't)... > > > def expmave(x, k): > > > def expmave_sub(a, b): > > > return a + k * (b - a) > > > return numpy.vectorize(expmave_sub).accumulate(x) > > > > Why can't you simply use list comprehensions? Too slow? > > > > For example: > > def expmave(x, k): > > return [x[0]] + [x[i-1] + k*(x[i]-x[i-1]) for i in range(1,len(x))] > > > > Karol > > That's not the same calculation. There is no cumulative effect in your > function. > > Each iteration depends on the previous iteration, like a cumulative sum, > etc. > > And as for the speed, I'm basically trying to determine what is the most > efficient way to do it, outside of writing it in C. And I don't really want > to create intermediate lists, ideally things would be kept as numpy arrays. > I can't think of a loop free way to do it with numpy. > > Good try though. No harm in trying! That's the only way we learn things.
You're right, I didn't think that throught. Sorry. -- written by Karol Langner Sat Jan 6 08:43:50 CET 2007 _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
