如何迅速地从大小为N 9阵列创建n个3×3矩阵排列？ [英] How to rapidly create array of N 3x3 matrices from 9 arrays of size N?

问题描述

假设我有大小N. 9阵列（A，B，C，... J），我想创建n个3×3矩阵的一个新的阵列，使得例如

Assume that I have 9 arrays (A, B, C, .. J) of size N. I want to create a new array of N 3x3 matrices such that e.g.

matrices[i] = [[A[i], B[i], C[i]],
               [D[i], E[i], F[i]],
               [G[i], H[i], J[i]]]

有一个简单的解决方案是增加每次进入阵列矩阵在for循环为：

A simple solution is to add each entry to the array matrices in a for-loop as:

for i in range(len(matrices)):
    matrices[i] = [[A[i], B[i], C[i]],
            [D[i], E[i], F[i]],
            [G[i], H[i], J[i]]]

任何人有关于如何能够以更快，量化的方式避免for循环做一些建议吗？如果存在一些聪明的索引操作什么的。

Anybody got some tips on how this can be done in a faster, vectorized way avoiding the for-loop? If there exists some smart indexing operations or something.

推荐答案

一个办法是堆放在那些柱的 np.column_stack 和的 np.reshape -

One approach would be to stack those in columns with np.column_stack and reshape with np.reshape -

np.column_stack((A,B,C,D,E,F,G,H,J)).reshape(-1,3,3)

串联使用 np.concatenate 被称为是更快，所以使用它与 2D转置与重塑 -

np.concatenate((A,B,C,D,E,F,G,H,J)).reshape(9,-1).T.reshape(-1,3,3)

另一个与 np.concatenate ， 3D转置与重塑 -

np.concatenate((A,B,C,D,E,F,G,H,J)).reshape(3,3,-1).transpose(2,0,1)

运行测试 -

Runtime tests -

In [59]: # Setup input arrays
    ...: N = 1000
    ...: A = np.random.randint(0,9,(N,))
    ...: B = np.random.randint(0,9,(N,))
    ...: C = np.random.randint(0,9,(N,))
    ...: D = np.random.randint(0,9,(N,))
    ...: E = np.random.randint(0,9,(N,))
    ...: F = np.random.randint(0,9,(N,))
    ...: G = np.random.randint(0,9,(N,))
    ...: H = np.random.randint(0,9,(N,))
    ...: J = np.random.randint(0,9,(N,))
    ...: 

In [60]: %timeit np.column_stack((A,B,C,D,E,F,G,H,J)).reshape(-1,3,3)
10000 loops, best of 3: 84.4 µs per loop

In [61]: %timeit np.concatenate((A,B,C,D,E,F,G,H,J)).reshape(9,-1).T.reshape(-1,3,3)
100000 loops, best of 3: 15.8 µs per loop

In [62]: %timeit np.concatenate((A,B,C,D,E,F,G,H,J)).reshape(3,3,-1).transpose(2,0,1)
100000 loops, best of 3: 14.8 µs per loop

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