random.choice 的加权版本 [英] A weighted version of random.choice

问题描述

我需要编写 random.choice 的加权版本(列表中的每个元素都有不同的被选中概率).这是我想出的:

I needed to write a weighted version of random.choice (each element in the list has a different probability for being selected). This is what I came up with:

def weightedChoice(choices):
    """Like random.choice, but each element can have a different chance of
    being selected.

    choices can be any iterable containing iterables with two items each.
    Technically, they can have more than two items, the rest will just be
    ignored.  The first item is the thing being chosen, the second item is
    its weight.  The weights can be any numeric values, what matters is the
    relative differences between them.
    """
    space = {}
    current = 0
    for choice, weight in choices:
        if weight > 0:
            space[current] = choice
            current += weight
    rand = random.uniform(0, current)
    for key in sorted(space.keys() + [current]):
        if rand < key:
            return choice
        choice = space[key]
    return None

这个功能对我来说似乎过于复杂，而且丑陋.我希望这里的每个人都可以提供一些改进建议或替代方法.对我来说，效率不如代码整洁和可读性重要.

This function seems overly complex to me, and ugly. I'm hoping everyone here can offer some suggestions on improving it or alternate ways of doing this. Efficiency isn't as important to me as code cleanliness and readability.

推荐答案

从 1.7.0 版本开始，NumPy 有了 choice 支持概率分布的函数.

Since version 1.7.0, NumPy has a choice function that supports probability distributions.

from numpy.random import choice
draw = choice(list_of_candidates, number_of_items_to_pick,
              p=probability_distribution)

请注意，probability_distribution 是一个与 list_of_candidates 顺序相同的序列.您还可以使用关键字 replace=False 来更改行为，以便不替换绘制的项目.

Note that probability_distribution is a sequence in the same order of list_of_candidates. You can also use the keyword replace=False to change the behavior so that drawn items are not replaced.

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