如何将numpy random.choice应用于概率值矩阵(矢量化解决方案) [英] How to apply numpy random.choice to a matrix of probability values (Vectorized solution)

问题描述

我遇到的问题如下

我有一个带有3个值的一维整数列表(或np.array)

I have a 1-D list of integers (or np.array) with 3 values

l = [0,1,2]

我有一个二维概率列表(为简单起见，我们将使用两行)

I have a 2-D list of probabilities (for simplicity, we'll use two rows)

P = 
[[0.8, 0.1, 0.1],
 [0.3, 0.3, 0.4]]

我想要的是numpy.random.choice(a=l, p=P)，其中P(概率分布)中的每一行都应用于l.因此，我希望从带有概率的[0,1,2]中抽取一个随机样本. dist. [0.8，0.1，0.1]首先，然后是概率. dist. [0.3，0.3，0.4]接下来，给我两个输出.

What I want is numpy.random.choice(a=l, p=P), where each row in P (probability distribution) is applied to l. So, I want a random sample to be drawn from [0,1,2] with prob. dist. [0.8, 0.1, 0.1] first, then with prob. dist. [0.3, 0.3, 0.4] next, to give me two outputs.

=====更新======

===== Update ======

我可以用于循环或列表理解，但是我正在寻找一种快速/矢量化的解决方案.

I can use for loops or list comprehension, but I am looking for a fast/vectorized solution.

推荐答案

这是一种方法.

以下是几率:

In [161]: p
Out[161]: 
array([[ 0.8 ,  0.1 ,  0.1 ],
       [ 0.3 ,  0.3 ,  0.4 ],
       [ 0.25,  0.5 ,  0.25]])

c保存累积分布:

In [162]: c = p.cumsum(axis=1)

生成一组均匀分布的样本...

Generate a set of uniformly distributed samples...

In [163]: u = np.random.rand(len(c), 1)

...然后查看它们在c中适合"的位置:

...and then see where they "fit" in c:

In [164]: choices = (u < c).argmax(axis=1)

In [165]: choices
Out[165]: array([1, 2, 2])

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