Numpy高级选择不起作用 [英] Numpy advanced selection not working

问题描述

有人可以帮我理解为什么有时高级选择不起作用以及我怎样做才能使它起作用(第二种情况)?

Can someone please help me to understand why sometimes the advanced selection doesn't work and what I can do to get it to work (2nd case)?

>>> import numpy as np
>>> b = np.random.rand(5, 14, 3, 2)

# advanced selection works as expected
>>> b[[0,1],[0,1]]
array([[[ 0.7575555 ,  0.18989068],
        [ 0.06816789,  0.95760398],
        [ 0.88358107,  0.19558106]],

       [[ 0.62122898,  0.95066355],
        [ 0.62947885,  0.00297711],
        [ 0.70292323,  0.2109297 ]]])

# doesn't work - why?
>>> b[[0,1],[0,1,2]]
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
ValueError: shape mismatch: objects cannot be broadcast to a single shape

# but this seems to work
>>> b[:,[0,1,2]]
array([[[[  7.57555496e-01,   1.89890676e-01],
         [  6.81678915e-02,   9.57603975e-01],
         [  8.83581071e-01,   1.95581063e-01]],

        [[  2.24896112e-01,   4.77818599e-01],
         [  4.29313861e-02,   8.61578045e-02],
         [  4.80092364e-01,   3.66821618e-01]],
...

更新

打破选择似乎可以解决问题，但是我不确定为什么这样做是必要的(或者是否有更好的方法来实现这一目标).

Breaking up the selection seems to resolve the problem, but I am unsure why this is necessary (or if there's a better way to achieve this).

>>> b.shape
(5, 14, 3, 2)
>>> b[[0,1]].shape
(2, 14, 3, 2)

# trying to separate indexing by dimension.
>>> b[[0,1]][:,[0,1,2]]
array([[[[ 0.7575555 ,  0.18989068],
         [ 0.06816789,  0.95760398],
         [ 0.88358107,  0.19558106]],

        [[ 0.22489611,  0.4778186 ],
         [ 0.04293139,  0.0861578 ],

推荐答案

您想要的

b[np.ix_([0, 1], [0, 1, 2])]

您还需要对b[[0, 1], [0, 1]]做同样的事情，因为这实际上并没有按照您的想法去做:

You also need to do the same thing for b[[0, 1], [0, 1]], because that's not actually doing what you think it is:

b[np.ix_([0, 1], [0, 1])]

---

这里的问题是高级索引所做的事情与您认为的完全不同.您误以为b[[0, 1], [0, 1, 2]]的意思是取下b的所有部分b[i, j]，其中i是0或1，而j是0、1或2".考虑到当您在索引表达式中有一个列表(如

---

The problem here is that advanced indexing does something completely different from what you think it does. You've made the mistake of thinking that b[[0, 1], [0, 1, 2]] means "take all parts b[i, j] of b where i is 0 or 1 and j is 0, 1, or 2". This is a reasonable mistake to make, considering that it seems to work that way when you have one list in the indexing expression, like

b[:, [1, 3, 5], 2]

实际上，对于数组A和一维整数数组I和J，A[I, J]是其中的数组

In fact, for an array A and one-dimensional integer arrays I and J, A[I, J] is an array where

A[I, J][n] == A[I[n], J[n]]

这自然地推广到了更多的索引数组，例如

This generalizes in the natural way to more index arrays, so for example

A[I, J, K][n] == A[I[n], J[n], K[n]]

以及更高维的索引数组，因此，如果I和J是二维的，则

and to higher-dimensional index arrays, so if I and J are two-dimensional, then

A[I, J][m, n] == A[I[m, n], J[m, n]]

它还将广播规则应用于索引数组，并将索引中的列表转换为数组.这比您预期的要强大得多，但这意味着要做您想做的事情，您需要类似

It also applies the broadcasting rules to the index arrays, and converts lists in the indexes to arrays. This is much more powerful than what you expected to happen, but it means that to do what you were trying to do, you need something like

b[[[0],
   [1]], [[0, 1, 2]]]

np.ix_是一个帮您完成此任务的助手，因此您不必写很多括号.

np.ix_ is a helper that will do that for you so you don't have to write a dozen brackets.

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