在两个方向上移动2D矩阵的有效方法? [英] Efficient way to shift 2D-matrices in both directions?

问题描述

给出一个二维矩阵，例如

Given a two dimensional matrix, e.g.

l = [[1,1,1],
     [2,5,2],
     [3,3,3]])

在列和行上执行移位操作的最有效方法是什么?

What is the most efficient way of implementing a shift operation on columns and rows?

例如

shift('up', l) 

[[2, 5, 2],
 [3, 3, 3],
 [1, 1, 1]]

但是

shift('left', l) 

[[1, 1, 1],
 [5, 2, 2],
 [3, 3, 3]]

由于此答案，我在两个深度上都使用了collections.deque，但同时出现了向上"或向下" 只需要移位1个，向左"或向右"需要N个移位(我的实现是对每行使用for循环).

I'm using collections.deque on both depths because of this answer but while a 'up' or 'down' only requires 1 shift, a 'left' or 'right' requires N shifts (my implementation is using a for cycle for each row).

在C语言中，我认为可以使用指针算法来改善这一点(请参见例如此答案).

In C I think this can be improved using pointer arithmetic (see e.g. this answer).

有没有更好的pythonic方法?

Is there a better pythonic way?

• 高效是指是否有避免N频移的方法.
• 我们可以假设矩阵是平方的.
• 可以就班了.

感谢martineau指出了问题的这些要点. 抱歉，我之前没有指出.

Thanks to martineau for pointing out these important points of the question. I'm sorry I didn't pointed them out before.

推荐答案

Numpy提供了一种名为roll()的方法来移动条目.

Numpy provides a method called roll() to shift entries.

>>> import numpy as np
>>> x = np.arange(9)
>>> x = x.reshape(3, 3)
>>> print(x)

[[0 1 2]
 [3 4 5]
 [6 7 8]]

>>> x = np.roll(x, -1, axis=0) # up
>>> print(x)

[[3 4 5]
 [6 7 8]
 [0 1 2]]

>>> x = np.roll(x, 1, axis=0) # down
>>> print(x)

[[0 1 2]
 [3 4 5]
 [6 7 8]]

>>> x = np.roll(x, 2, axis=1) # right
>>> print(x)

[[1 2 0]
 [4 5 3]
 [7 8 6]]

>>> x = np.roll(x, -2, axis=1) # left
>>> print(x)    

[[0 1 2]
 [3 4 5]
 [6 7 8]]

我猜想 Numpy 与大多数解决方案相比将非常有效
就矩阵运算而言，您将不会受到二维矩阵的束缚.

I guess that Numpy will be pretty efficient compared to most solutions
in terms of matrix operations and you won't be bound to a 2 dimensional matrix.

这篇关于在两个方向上移动2D矩阵的有效方法?的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！