用于多维 ndarray 的 argsort

问题描述

我正在尝试获取索引以按最后一个轴对多维数组进行排序，例如

>>>a = np.array([[3,1,2],[8,9,2]])

我想要索引 i 使得，

>>>[i]数组([[1, 2, 3],[2, 8, 9]])

基于 numpy.argsort 的文档我认为它应该这样做，但我收到错误:

>>>a[np.argsort(a)]索引错误:索引 2 超出轴 0 的范围，大小为 2

我需要以相同的方式重新排列其他相同形状的数组(例如数组 b 使得 a.shape == b.shape)...所以

>>>b = np.array([[0,5,4],[3,9,1]])>>>双]数组([[5,4,0],[9,3,1]])

解决方案

以上答案现在有点过时了，因为 numpy 1.15 中添加了新功能以使其更简单；take_along_axis (https://docs.scipy.org/doc/numpy-1.15.1/reference/generated/numpy.take_along_axis.html) 允许你做:

>>>a = np.array([[3,1,2],[8,9,2]])>>>np.take_along_axis(a, a.argsort(axis=-1),axis=-1)数组([[1 2 3][2 8 9]])

I'm trying to get the indices to sort a multidimensional array by the last axis, e.g.

>>> a = np.array([[3,1,2],[8,9,2]])

And I'd like indices i such that,

>>> a[i]
array([[1, 2, 3],
       [2, 8, 9]])

Based on the documentation of numpy.argsort I thought it should do this, but I'm getting the error:

>>> a[np.argsort(a)]
IndexError: index 2 is out of bounds for axis 0 with size 2

---

Edit: I need to rearrange other arrays of the same shape (e.g. an array b such that a.shape == b.shape) in the same way... so that

>>> b = np.array([[0,5,4],[3,9,1]])
>>> b[i]
array([[5,4,0],
       [9,3,1]])

解决方案

The above answers are now a bit outdated, since new functionality was added in numpy 1.15 to make it simpler; take_along_axis (https://docs.scipy.org/doc/numpy-1.15.1/reference/generated/numpy.take_along_axis.html) allows you to do:

>>> a = np.array([[3,1,2],[8,9,2]])
>>> np.take_along_axis(a, a.argsort(axis=-1), axis=-1)
array([[1 2 3]
       [2 8 9]])

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