import numpy as np
# define our matrix M
M = np.arange(6*8).reshape(6,8)
当我们看 M 时:
> M
> array(
[[ 0, 1, 2, 3, 4, 5, 6, 7],
[ 8, 9, 10, 11, 12, 13, 14, 15],
[16, 17, 18, 19, 20, 21, 22, 23],
[24, 25, 26, 27, 28, 29, 30, 31],
[32, 33, 34, 35, 36, 37, 38, 39],
[40, 41, 42, 43, 44, 45, 46, 47]])
> M.shape
> (6, 8)
现在我们得到每一行的最大参数:
> z = np.argmax(M, axis=1)
> array([7, 7, 7, 7, 7, 7])
因此,我们得到了每一行的最大元素的索引。如果我们想取回这些值,我们可以简单地对 M 进行切片:
> M[np.arange(M.shape[0]), z]
> array([ 7, 15, 23, 31, 39, 47])
np.arange(M.shape[0]) 为每一行创建索引,而z 覆盖列的索引。因此,我们正在提取每一行的第 n 个元素(根据z 的条目)。
为了证明这适用于任意“排序”数组:
> M2 = np.ravel(np.copy(M))
> np.random.shuffle(M2)
> M2 = M2.reshape(M.shape[0], M.shape[1])
> M2
> array(
[[34, 16, 5, 32, 31, 2, 17, 38],
[33, 18, 9, 46, 20, 4, 39, 30],
[10, 41, 35, 23, 0, 24, 45, 14],
[28, 36, 8, 22, 11, 15, 7, 44],
[27, 1, 25, 6, 3, 19, 47, 37],
[40, 42, 29, 21, 12, 43, 26, 13]])
> z2 = np.argmax(M2, axis=1)
> array([7, 3, 6, 7, 6, 5])
> M2[np.arange(M2.shape[0]), z2]
> array([38, 46, 45, 44, 47, 43])