numpy.einsum for朱莉娅? [英] numpy.einsum for Julia?

问题描述

我想知道如何在Julia中获得类似于numpy.einsum的功能.

I'm wondering how to get functionality similar to numpy.einsum in Julia.

具体来说，我想将一个三阶张量乘以一个第二张量(矩阵)，将这两个维度都缩小以产生一个一阶张量(向量).

Specifically, I have a 3rd order tensor that I'm looking to multiply by a 2nd tensor (matrix), contracting both of the dimensions to yield a 1st order tensor (vector).

当前，我正在使用PyCall，以便可以像这样使用numpy.einsum函数:

Currently, I'm using PyCall so that I can use the numpy.einsum function like so:

using PyCall
@pyimport numpy as np

a = rand(5,4,3)
b = rand(5,4)

c = np.einsum("ijk,ij", a,b)
size(c) == (3,)

依赖于调用python来进行张量数学是一种愚蠢的选择.我还认为，实施茱莉亚会带来速度优势.但是，我在julia中没有任何功能，蛮力求和要慢1-2个数量级.我可以使用哪些功能?

It feels kind of silly to rely on calling python in order to do tensor math. I also imagine that a julia implementation would have speed advantages. However, I haven't any function for this in julia, and the brute force summation is 1-2 orders of magnitude slower. What functions can I use?

推荐答案

sum(a.*b,(1,2))不需要做什么吗?

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