So you're saying that abs2.(x .- y.') will not allocate a 2d array and then pass to abs2? That's great! But how would I know that?
DNF wrote: > For your particular example, it looks like what you want is (and I am just > guessing what mag_sqr means): > dist = abs2.(x .- y.') > The performance should be the similar to a hand-written loop on version > 0.5. > > You can read about it here: > http://docs.julialang.org/en/release-0.5/manual/functions/#dot-syntax-for-vectorizing-functions > > > On Monday, September 12, 2016 at 9:29:15 PM UTC+2, Neal Becker wrote: >> >> Some time ago I asked this question >> >> http://stackoverflow.com/questions/25486506/julia-broadcasting-equivalent-of-numpy-newaxis >> >> As a more interesting example, here is some real python code I use: >> dist = mag_sqr (demod_out[:,np.newaxis] - const.map[np.newaxis,:]) >> >> where demod_out, const.map are each vectors, mag_sqr performs >> element-wise euclidean distance, and the result is a 2D array whose 1st >> axis matches the >> 1st axis of demod_out, and the 2nd axis matches the 2nd axis of >> const.map. >> >> >> From the answers I've seen, julia doesn't really have an equivalent >> functionality. The idea here is, without allocating a new array, >> manipulate >> the strides to cause broadcasting. >> >> AFAICT, the best for Julia would be just forget the vectorized code, and >> explicitly write out loops to perform the computation. OK, I guess, but >> maybe not as readable. >> >> Is there any news on this front? >> >>
