是否有用于Python数组同步改组的习惯用法或API? [英] Is there an idiom or API for synchronized shuffling of Python arrays?

问题描述

NumPy(或TensorFlow)中是否有一个API用于执行多个数组的同步改组(具有相同的第一维)?

Is there an API in NumPy (or perhaps TensorFlow) for performing a synchronized shuffling of several arrays (with the same first dimension)?

例如，如果我将两个数组的维度分别为(N，A)和(N，B)，并且我想随机化每个数组的N个元素的顺序，同时保持第一个数组的元素与第二个.

For example, if I two arrays with dimensions (N, A) and (N, B), and I want to randomize the ordering of the N elements of each, while maintaining the association between the elements of the first array and the second.

是否有实现此目的的API或Python习惯用法?

Is there an API or Python idiom for accomplishing this?

请注意，将它们组合成N个元组的单个数组，然后用random.shuffle进行混洗，这可能是我可以接受的一个选项，但我无法使它起作用:将原始数组取回来因为combined_array[:,0]将对象具有维度(N，)作为元素，而不是维度(N，A)，所以混乱(正如我所管理的那样)，除非它是

Note that combining these into a single array of N tuples which are then shuffled with random.shuffle might be an option that I'd accept as an answer, but I can't get that to work: getting the original arrays back is (as near as I've managed) messy since combined_array[:,0] will have dimension (N,) with objects as elements, rather than dimension (N, A), unless it is manually rebuilt with something like [x for x in combined_array[:,0]

推荐答案

permutation = numpy.random.permutation(N)

arr1_shuffled = arr1[permutation]
arr2_shuffled = arr2[permutation]

选择一个排列并将其用于两个数组.

Pick one permutation and use it for both arrays.

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