[英]Numpy: 2D array access with 2D array of indices
I have two arrays, one is a matrix of index pairs,我有两个数组,一个是索引对矩阵,
a = array([[[0,0],[1,1]],[[2,0],[2,1]]], dtype=int)
and another which is a matrix of data to access at these indices另一个是在这些索引处访问的数据矩阵
b = array([[1,2,3],[4,5,6],[7,8,9]])
and I want to able to use the indices of a to get the entries of b
.我希望能够使用 a 的索引来获取
b
的条目。 Just doing:只是做:
>>> b[a]
does not work, as it gives one row of b for each entry in a
, ie不工作,因为它给出了B的一行中的每个条目
a
,即
array([[[[1,2,3],
[1,2,3]],
[[4,5,6],
[4,5,6]]],
[[[7,8,9],
[1,2,3]],
[[7,8,9],
[4,5,6]]]])
when I would like to use the index pair in the last axis of a
to give the two indices of b
:当我想使用
a
的最后一个轴中的索引对来给出b
的两个索引时:
array([[1,5],[7,8]])
Is there a clean way of doing this, or do I need to reshape b
and combine the columns of a
in a corresponding manner?是否有这样做的一个干净的方式,或者我需要重塑
b
和组合的列a
以相应的方式?
In my actual problem a
has about 5 million entries, and b
is 100-by-100, I'd like to avoid for loops.在我的实际问题中,
a
大约有 500 万个条目, b
是 100×100,我想避免 for 循环。
Actually, this works:实际上,这是有效的:
b[a[:, :, 0],a[:, :, 1]]
Gives array([[1, 5], [7, 8]])
.给出
array([[1, 5], [7, 8]])
。
For this case, this works对于这种情况,这有效
tmp = a.reshape(-1,2)
b[tmp[:,0], tmp[:,1]]
A more general solution, whenever you want to use a 2D array of indices of shape (n,m) with arbitrary large dimension m , named inds
, in order to access elements of another 2D array of shape (n,k), named B
:一个更通用的解决方案,每当您想要使用具有任意大维度 m的形状 (n,m) 索引的二维数组,命名为
inds
,以便访问另一个二维形状数组 (n,k) 的元素,命名为B
:
# array of index offsets to be added to each row of inds
offset = np.arange(0, inds.size, inds.shape[1])
# numpy.take(B, C) "flattens" arrays B and C and selects elements from B based on indices in C
Result = np.take(B, offset[:,np.newaxis]+inds)
Another solution, which doesn't use np.take
and I find more intuitive, is the following:另一个不使用
np.take
并且我觉得更直观的解决方案如下:
B[np.expand_dims(np.arange(B.shape[0]), -1), inds]
The advantage of this syntax is that it can be used both for reading elements from B
based on inds
(like np.take
), as well as for assignment.这种语法的优点是它既可以用于基于
inds
(如np.take
)从B
读取元素,也可以用于赋值。
You can test this by using, eg:您可以使用以下方法进行测试,例如:
B = 1/(np.arange(n*m).reshape(n,-1) + 1)
inds = np.random.randint(0,B.shape[1],(B.shape[0],B.shape[1]))
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