Numpy花哨的索引和赋值 [英] Numpy fancy indexing and assignment

问题描述

通常numpy强制分配的左侧和右侧匹配，例如，如果我 a [：] = b ， b 必须与 a 具有相同的形状或广播形式。但该规则似乎有例外：

Normally numpy forces the left and right side of an assignment to match, so for example if I do a[:] = b, b must be the same shape or broadcast to the same shape as a. But there seems to be an exception to that rule:

>>> a = np.arange(10)
>>> a
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
>>> b = a.copy()
>>> a[[0,1,2]] = b[::2]
>>> a
array([0, 2, 4, 3, 4, 5, 6, 7, 8, 9])
>>> a[np.arange(10)] = b[:2]
>>> a
array([0, 1, 0, 1, 0, 1, 0, 1, 0, 1])

它似乎只适用于1d数组，并且只有在赋值的左侧有花哨的索引，但我无法在任何地方找到此行为的文档。是否记录了这种行为，如果有的话，还有人可以举例说明它何时有用？

It seems to only work with 1d arrays and only if there is fancy indexing on the left side of the assignment, but I haven't been able to find documentation for this behavior anywhere. Is this behavior documented, if so where, and also can someone give an example of when it might be useful?

更新：

似乎numpy flatiter类型的行为也是这样，flatiter和fancy indexing之间是否存在一些我不知道的关联？

It seems that the numpy flatiter type behaves this way too, is there some connection between flatiter and fancy indexing that I don't know about?

>>> a.flat = [10,11]
>>> a
array([10, 11, 10, 11, 10, 11, 10, 11, 10, 11])
>>> a.flat[:] = [2,3,4]
>>> a
array([2, 3, 4, 2, 3, 4, 2, 3, 4, 2])
>>> a.flat = range(100)
>>> a
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])

推荐答案

我认为这种行为是以R及其祖先S / S-plus为模型的。这就是列表赋值（向量赋值）在那里工作的方式，它被称为回收。 R项目网站谈到了它，但我在此链接上找到了更有启发性的解释。在R中，矢量是测量的集合，因此以它的方式填充或修剪它是有意义的。这种逻辑有多少让它变得笨拙，为什么，这仍然是一个很好的问题。

I think this behavior is modeled on R and its ancestor S/S-plus. That's how list assignment ("vector" assignment) works there, and it's called "recycling". The R project website talks about it, but I found a more illuminating explanation at this link. In R, a vector is a collection of measurements, so it makes sense to pad or trim it the way it does. How much of this logic has made it to numpy, and why, is still a good question.

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