我有一个形状(M*N,N)
的二维数组,实际上由M
,N*N
数组组成。我想以矢量化方式转置所有这些元素(N*N
矩阵)。例如,
import numpy as np
A=np.arange(1,28).reshape((9,3))
print "A before transposing:\n", A
for i in range(3):
A[i*3:(i+1)*3,:]=A[i*3:(i+1)*3,:].T
print "A after transposing:\n", A
此代码生成以下输出:
A before transposing:
[[ 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]]
A after transposing:
[[ 1 4 7]
[ 2 5 8]
[ 3 6 9]
[10 13 16]
[11 14 17]
[12 15 18]
[19 22 25]
[20 23 26]
[21 24 27]]
我期待的。但我想要矢量化版本。
答案 0 :(得分:8)
这是一种令人讨厌的方法,可以在一行中完成!
A.reshape((-1, 3, 3)).swapaxes(-1, 1).reshape(A.shape)
一步一步。重塑为(3, 3, 3)
>>> A.reshape((-1, 3, 3))
array([[[ 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]]])
然后在每个子阵列上执行类似转置的操作swapaxes
>>> A.reshape((-1, 3, 3)).swapaxes(-1, 1)
array([[[ 1, 4, 7],
[ 2, 5, 8],
[ 3, 6, 9]],
[[10, 13, 16],
[11, 14, 17],
[12, 15, 18]],
[[19, 22, 25],
[20, 23, 26],
[21, 24, 27]]])
最后重塑为(9, 3)
。
>>> A.reshape((-1, 3, 3)).swapaxes(-1, 1).reshape(A.shape)
array([[ 1, 4, 7],
[ 2, 5, 8],
[ 3, 6, 9],
[10, 13, 16],
[11, 14, 17],
[12, 15, 18],
[19, 22, 25],
[20, 23, 26],
[21, 24, 27]])
>>>
我认为,对于任何方法,必须复制数据,因为没有可以从以下结果生成结果的2d步幅/形状:
array([ 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])
(是吗?)在我的版本中,我认为数据会在最终的重塑步骤中被复制
答案 1 :(得分:3)
In [42]: x = np.arange(1,28).reshape((9,3))
In [43]: x
Out[43]:
array([[ 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]])
In [31]: r,c = x.shape
In [39]: z = np.vstack(np.hsplit(x.T,r/c))
In [45]: z
Out[45]:
array([[ 1, 4, 7],
[ 2, 5, 8],
[ 3, 6, 9],
[10, 13, 16],
[11, 14, 17],
[12, 15, 18],
[19, 22, 25],
[20, 23, 26],
[21, 24, 27]])