对于一维数组,可以通过 N 维整数数组对其进行索引,如下所示:
>>> rand = np.random.rand(9).astype(np.float32)
>>> rand
array([ 0.69786191, 0.09376735, 0.60141236, 0.35305005, 0.68340319,
0.0746202 , 0.11620298, 0.46607161, 0.90864712], dtype=float32)
>>> u = np.random.randint(0, 9, (2,2))
>>> u
array([[0, 6],
[5, 6]])
>>> rand[u]
array([[ 0.69786191, 0.11620298],
[ 0.0746202 , 0.11620298]], dtype=float32)
但我不能对二维数组做同样的事情:
>>> rand2d = np.random.rand(9).astype(np.float32).reshape(3,3)
>>> rand2d
array([[ 0.83248657, 0.75025952, 0.87252802],
[ 0.78049046, 0.92902303, 0.42035589],
[ 0.80461669, 0.49386421, 0.56518084]], dtype=float32)
>>> u = np.random.randint(0, 3, (2,2,2))
>>> u
array([[[2, 2],
[2, 2]],
[[0, 2],
[0, 1]]])
>>> rand2d[u]
array([[[[ 0.80461669, 0.49386421, 0.56518084],
[ 0.80461669, 0.49386421, 0.56518084]],
[[ 0.80461669, 0.49386421, 0.56518084],
[ 0.80461669, 0.49386421, 0.56518084]]],
[[[ 0.83248657, 0.75025952, 0.87252802],
[ 0.80461669, 0.49386421, 0.56518084]],
[[ 0.83248657, 0.75025952, 0.87252802],
[ 0.78049046, 0.92902303, 0.42035589]]]], dtype=float32)
虽然我期望的结果是:
[[rand2d[2, 2], rand2d[2, 2]],
[rand2d[0, 2], rand2d[0, 1]]] ==
[[0.56518084, 0.56518084],
[0.87252802, 0.75025952]]
如何在不迭代的情况下实现这一目标?
最佳答案
直接来自example in the docs :
>>>
>>> x
array([[ 0, 1, 2],
[ 3, 4, 5],
[ 6, 7, 8],
[ 9, 10, 11]])
>>>
>>> rows = np.array([[0,0],[3,3]])
>>> columns = np.array([[0,2],[0,2]])
>>> x[rows,columns]
array([[ 0, 2],
[ 9, 11]])
>>>
您可以看到它正在选择 (0,0)、(0,2) 和 (3,0),(3,2) 处的项目。
关于python - Numpy 2d 和可能的 N d 通过元组数组进行索引,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/27477295/