numpy:替换数组中的随机元素

问题描述

我已经在Google上搜索了一下，但没有发现任何好处答案.

I already googled a bit and didn't find any goodanswers.

问题是，我有一个二维的numpy数组，我想在随机位置替换其某些值.

The thing is, I have a 2d numpy array and I'd like toreplace some of its values at random positions.

我使用numpy.random.choice创建了一些答案阵列的遮罩.不幸的是，这并没有创造原始数组上的视图，因此我无法替换其值.

I found some answers using numpy.random.choice to createa mask for the array. Unfortunately this does not createa view on the original array so I can not replace its values.

所以这是我想做的事的一个例子.

So here is an example of what I'd like to do.

想象一下我有一个带有浮点值的二维数组.

Imagine I have 2d array with float values.

[[ 1., 2., 3.],
 [ 4., 5., 6.],
 [ 7., 8., 9.]]

然后我想替换任意数量的元素.如果我可以调整参数会很好多少元素将被替换.可能的结果如下所示:

And then I'd like to replace an arbitrary amount ofelements. It would be nice if I could tune with a parameterhow many elements are going to be replaced.A possible result could look like this:

[[ 3.234, 2., 3.],
 [ 4., 5., 6.],
 [ 7., 8., 2.234]]

我想不出什么好办法来实现这一目标.感谢您的帮助.

I couldn't think of nice way to accomplish this.Help is appreciated.

编辑

感谢所有快速答复.

推荐答案

只需使用相同形状的任意一个遮罩您的输入数组即可.

Just mask your input array with a random one of the same shape.

import numpy as np

# input array
x = np.array([[ 1., 2., 3.], [ 4., 5., 6.], [ 7., 8., 9.]])

# random boolean mask for which values will be changed
mask = np.random.randint(0,2,size=x.shape).astype(np.bool)

# random matrix the same shape of your data
r = np.random.rand(*x.shape)*np.max(x)

# use your mask to replace values in your input array
x[mask] = r[mask]

产生这样的东西:

[[ 1.          2.          3.        ]
 [ 4.          5.          8.54749399]
 [ 7.57749917  8.          4.22590641]]

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