numpy数组索引中的隐式转置 [英] Implicit transposing in numpy array indexing

问题描述

我遇到了一个奇怪的问题:

I came across a weird problem:

from numpy import zeros, arange
aa = zeros([1, 3, 10])
aa[0, :, arange(5)].shape

运行此代码可以得到(5,3)，但是我希望得到(3,5).

Running this gives me (5,3), but I'm expecting (3,5).

但是，运行以下命令可以使我得到(3,5).

However, running the following gives me (3,5) as expected.

aa = zeros([3, 10])
aa[:, arange(5)]

作为我的程序的一部分，此问题很容易解决，但这完全破坏了我的信念.

This is easy to fix as part of my program, but it completely ruined my belief.

我试图搜索已经回答但不知道要搜索什么的类似问题.

I tried to search for similar questions that have already been answered but have no idea what to search for.

谢谢您，春节快乐！

推荐答案

这是基本索引和高级索引混合的一种情况.第一个和最后一个索引是数字，中间是切片.它根据 0 和 arange(5)选择值，并在末尾附加:维度.

This is a case of mixed basic and advanced indexing. The 1st and last indexes are numeric, and the middle a slice. It selects values based the 0 and arange(5), and appends the : dimension at the end.

aa[0, :, :5].shape

应该产生您期望的(3,5).

should produce the (3,5) you expect.

http://docs.scipy.org/doc/numpy/reference/arrays.indexing.html#combining-advanced-and-basic-indexing

数位3D阵列在何时进行转置单步索引与两步索引对比了

y = x[0, :, mask]
z = x[0, :, :][:, mask]

请务必检查对我的回答的注释，以证明这是一个错误，并且将得到解决.

Be sure to check the comments to my answer, for the argument that this is a bug and will be fixed.

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