I solved Expert 4 by Alireza's approach like following. (I don't understand how to use TensorOperations.jl in this case) Thanks for Alireza!
``` p, n = 10, 20 M = ones(n,n,p) V = ones(n,p) S = reduce(+, [M[i,:,j]*V[i] for i = 1:n, j = 1:p])' S ``` 2014年6月27日金曜日 5時59分15秒 UTC+9 Steven G. Johnson: > > > > On Thursday, June 26, 2014 9:54:34 AM UTC-4, Michiaki Ariga wrote: >> >> In original numpy version as following, matrix and vector are 3dimension >> arrays. >> Is there any way to compute tensordot like numpy? >> > > There is no built-in tensor contraction function at the moment ( > https://github.com/JuliaLang/julia/issues/3250), but you can try out the > TensorOperations package: > > https://github.com/Jutho/TensorOperations.jl > > You can also just write your own loop and the performance should be fine; > it's pretty easy to do this if you know the dimensionality in advance, and > it's only complicated to write tensor contraction code if you want to > handle arbitrary dimensionality and arbitrary contraction index sets. (It > takes a while to get used to the fact that you don't need to call a library > function for every inner loop in Julia.) >
