如何在python中进行类别的加权随机样本 [英] How to do weighted random sample of categories in python

问题描述

给出一个元组列表，其中每个元组都由一个概率和一个项组成，我想根据其概率对一个项进行采样.例如，给列表[(.3，'a')，(.4，'b')，(.3，'c')]我想在40％的时间内对'b'进行采样.

Given a list of tuples where each tuple consists of a probability and an item I'd like to sample an item according to its probability. For example, give the list [ (.3, 'a'), (.4, 'b'), (.3, 'c')] I'd like to sample 'b' 40% of the time.

在python中执行此操作的规范方法是什么?

What's the canonical way of doing this in python?

我查看了似乎没有适当功能的随机模块，以及numpy.random，尽管其具有多项式功能，但似乎并未以很好的形式返回此问题的结果.我基本上是在matlab中寻找mnrnd之类的东西.

I've looked at the random module which doesn't seem to have an appropriate function and at numpy.random which although it has a multinomial function doesn't seem to return the results in a nice form for this problem. I'm basically looking for something like mnrnd in matlab.

非常感谢.

非常感谢所有答案.为了澄清，我不是在寻找有关如何编写采样方案的解释，而是要指出一种简单的方法，该方法是从给定一组对象和权重的多项式分布中采样，或者被告知不存在这样的函数在标准库中，所以应该自己编写.

Thanks for all the answers so quickly. To clarify, I'm not looking for explanations of how to write a sampling scheme, but rather to be pointed to an easy way to sample from a multinomial distribution given a set of objects and weights, or to be told that no such function exists in a standard library and so one should write one's own.

推荐答案

import numpy

n = 1000
pairs = [(.3, 'a'), (.3, 'b'), (.4, 'c')]
probabilities = numpy.random.multinomial(n, zip(*pairs)[0])
result = zip(probabilities, zip(*pairs)[1])
# [(299, 'a'), (299, 'b'), (402, 'c')]
[x[0] * x[1] for x in result]
# ['aaaaaaaaaa', 'bbbbbbbbbbbbbbbbbbb', 'cccccccccccccccccccc']

您想如何准确地接收结果?

How exactly would you like to receive the results?

这篇关于如何在python中进行类别的加权随机样本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！