我需要写一个加权版本的random.choice(列表中的每个元素都有不同的被选择概率)。这是我想出的:
random.choice
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
对于我来说,此功能似乎过于复杂且难看。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。对于我来说,效率并不像代码的清洁度和可读性那么重要。
从1.7.0版开始,NumPy具有choice支持概率分布的功能。
NumPy
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,以便不替换绘制的项目。
probability_distribution
list_of_candidates
replace=False