On Tue, 2016-09-13 at 14:26, Neal Becker <[email protected]> wrote: > 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?
The operators do not do the fusing yet, check right at the bottom of the linked manual section. I think you can work around it by using their functional form: x .+ y # not fused (+).(x,y) # fused So: out .= abs2.((-).(x, y.')) > 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? >>> >>>
