加权版本的random.choice

问题描述

我需要写一个加权版本的random.choice(列表中的每个元素被选择的可能性都不同).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

此功能对我来说似乎过于复杂，而且很难看.我希望这里的每个人都可以提出一些改进建议或替代方法.对我而言，效率并不像代码的清洁度和可读性那么重要.

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版开始，NumPy的 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是与list_of_candidates相同顺序的序列.您还可以使用关键字replace=False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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