我需要编写一个加权版本的 random.choice(列表中的每个元素都有不同的被选中概率)。这就是我想出的:
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它或替代方法的建议。效率对我来说并不像代码的简洁性和可读性那么重要。
从 1.7.0 版本开始,NumPy 具有choice支持概率分布的功能。
choice
from numpy.random import choice draw = choice(list_of_candidates, number_of_items_to_pick, p=probability_distribution)
请注意,这probability_distribution是一个与 的顺序相同的序列list_of_candidates。您还可以使用关键字replace=False来更改行为,以便绘制的项目不会被替换。
probability_distribution
list_of_candidates
replace=False