random.shuffle 使用注意

有关random.shuffle函数的局限性

刚刚踩的坑，记录一下，万一对别人有帮助呢。

本文重点： random.shuffle千万不要用于二维numpy.array（也就是矩阵）!!!

首先上证据

如下代码：

import random
import numpy as np
a = np.array([[1,2,3,4],
              [5,6,7,8]])
random.shuffle(a)
print(a)

可能对应输出：

[[1 2 3 4]
 [1 2 3 4]]

得到一个错误输出！
查看random.shuffle源码：

    def shuffle(self, x, random=None):
    """Shuffle list x in place, and return None.

    Optional argument random is a 0-argument function returning a
    random float in [0.0, 1.0); if it is the default None, the
    standard random.random will be used.

    """

    if random is None:
        randbelow = self._randbelow
        for i in reversed(range(1, len(x))):
            # pick an element in x[:i+1] with which to exchange x[i]
            j = randbelow(i+1)
            x[i], x[j] = x[j], x[i]
    else:
        _int = int
        for i in reversed(range(1, len(x))):
            # pick an element in x[:i+1] with which to exchange x[i]
            j = _int(random() * (i+1))
            x[i], x[j] = x[j], x[i]

只需要关注其中交换元素操作为 x[i], x[j] = x[j], x[i] （好骚的操作，我之前不知道有这种写法）。

自己写个代码测试一下，这种交换方式对于二维numpy.array会发生什么事情：

import numpy as np
a = np.array([[1,2,3,4],
              [5,6,7,8]])
a[0], a[1] = a[1], a[0]
print(a)

输出：

[[5 6 7 8]
 [5 6 7 8]]

显然这种方式不适合numpy.array的行交换（但是二维list就可以使用这种交换方式。可以自行证明，至于原因暂时不知道，求解答）。

打乱numpy.array正确的姿势当然是使用numpy自带的numpy.random.shuffle()

def shuffle(x): # real signature unknown; restored from __doc__
"""
shuffle(x)

        Modify a sequence in-place by shuffling its contents.

        This function only shuffles the array along the first axis of a
        multi-dimensional array. The order of sub-arrays is changed but
        their contents remains the same.

        Parameters
        ----------
        x : array_like
            The array or list to be shuffled.

        Returns
        -------
        None

        Examples
        --------
        >>> arr = np.arange(10)
        >>> np.random.shuffle(arr)
        >>> arr
        [1 7 5 2 9 4 3 6 0 8]

        Multi-dimensional arrays are only shuffled along the first axis:

        >>> arr = np.arange(9).reshape((3, 3))
        >>> np.random.shuffle(arr)
        >>> arr
        array([[3, 4, 5],
               [6, 7, 8],
               [0, 1, 2]])
"""
pass

注释中Multi-dimensional arrays are only shuffled along the first axis: 多维向量只是沿着第一个坐标轴进行重新排序。