创建索引矩阵/索引列表的更简单方法? [英] Simpler way to create a matrix/list of indices?

问题描述

我想知道创建二维数组的最简单方法是什么，该数组对于每一行都有另一个多维数组的索引.

I wonder what could be the easiest way to create a bi-dimensional array, that has for each row the indices to another multi-dimensional array.

例如，假设我有一个4x4立方体，则索引矩阵"如下:

For example, let's say I have a cube 4x4, the "indices matrix" would be the following:

np.concatenate([
    np.expand_dims(curr.ravel(),axis=0).T
    for curr
    in np.meshgrid(
        np.arange(4),
        np.arange(4),
        np.arange(4)
    )
],axis=1)

具有以下结果:

array([[0, 0, 0],
      [0, 0, 1],
      [0, 0, 2],
      [0, 0, 3],
      [1, 0, 0],
      [1, 0, 1],
      ...
      [2, 3, 2],
      [2, 3, 3],
      [3, 3, 0],
      [3, 3, 1],
      [3, 3, 2],
      [3, 3, 3]])

除了第二列似乎应该代替第一列之外，还有没有更多的"numpythonic"方式以更紧凑的方式创建相同或相似的矩阵?

Besides the fact that it seems that the second column should be in place of the first, is there a more "numpythonic" way to create the same or similar matrix in a more compact way?

如果存在一个仅接受任意多维数组并返回其索引表的函数，那就太好了.

It would be nice if existed a function that just takes an arbitrary multi-dimensional array and returns it's index table.

推荐答案

您可以使用 np.indices :

You could use np.indices:

>>> a = np.random.random((4,4,4))
>>> np.indices(a.shape).reshape((a.ndim, -1)).T
array([[0, 0, 0],
       [0, 0, 1],
       [0, 0, 2],
       [0, 0, 3],
       [0, 1, 0],
       [0, 1, 1],
[...]
       [3, 3, 2],
       [3, 3, 3]])

还有其他实用程序，例如 np.ndindex ，具体取决于您的用例. (FWIW，我认为以您想要的形式获取坐标不会像您认为的那样有用，但是YMMV.)

There are also other utilities like np.ndindex, depending on your use case. (FWIW I don't think getting the coordinates in the form you're looking for is going to be as helpful as you might think it is, but YMMV.)

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