On Fri, Jun 5, 2009 at 17:54, Keith Goodman <kwgood...@gmail.com> wrote: > On Fri, Jun 5, 2009 at 3:02 PM, Alan G Isaac <ais...@american.edu> wrote: >> I think something close to this would be possible: >> add dot as an array method. >> A .dot(B) .dot(C) >> is not as pretty as >> A * B * C >> but it is much better than >> np.dot(np.dot(A,B),C) > > I've noticed that x.sum() is faster than sum(x) > >>> x = np.array([1,2,3]) >>> timeit x.sum() > 100000 loops, best of 3: 3.01 µs per loop >>> from numpy import sum >>> timeit sum(x) > 100000 loops, best of 3: 4.84 µs per loop > > Would the same be true of dot? That is, would x.dot(y) be faster than > dot(x,y)? Or is it just that np.sum() has to go through some extra > python code before it hits the C code?
No and yes, respectively. > In general, if I'm trying to speed up an inner loop, I try to replace > func(x) with x.func(). But I don't really understand the general > principle at work here. Most of the functions that mirror methods, including numpy.sum(), are Python functions that have a little of bit of code to convert the input to an array if necessary and to dispatch to the method. numpy.dot() is already implemented in C. -- Robert Kern "I have come to believe that the whole world is an enigma, a harmless enigma that is made terrible by our own mad attempt to interpret it as though it had an underlying truth." -- Umberto Eco _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
