我需要写一个加权版的random。选择(列表中的每个元素有不同的被选择的概率)。这是我想到的:

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

这个函数对我来说太复杂了,而且很丑。我希望这里的每个人都能提供一些改进的建议或其他方法。对我来说,效率没有代码的整洁和可读性重要。


当前回答

使用numpy

def choice(items, weights):
    return items[np.argmin((np.cumsum(weights) / sum(weights)) < np.random.rand())]

其他回答

从版本1.7.0开始,NumPy有一个支持概率分布的选择函数。

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

注意,probability_distribution是一个与list_of_candidate顺序相同的序列。您还可以使用关键字replace=False来更改行为,这样绘制的项就不会被替换。

通解:

import random
def weighted_choice(choices, weights):
    total = sum(weights)
    treshold = random.uniform(0, total)
    for k, weight in enumerate(weights):
        total -= weight
        if total < treshold:
            return choices[k]

假设你有

items = [11, 23, 43, 91] 
probability = [0.2, 0.3, 0.4, 0.1]

你有一个函数,它生成一个介于[0,1)之间的随机数(我们可以在这里使用random.random())。 现在求概率的前缀和

prefix_probability=[0.2,0.5,0.9,1]

现在,我们只需取一个0-1之间的随机数,然后使用二分搜索来查找该数字在prefix_probability中的位置。这个索引就是你的答案

代码是这样的

return items[bisect.bisect(prefix_probability,random.random())]

将权重排列成a 累积分布。 使用random.random()来选择一个随机的 浮点0.0 <= x < total。 搜索 用等分法进行分布。二等分的 如http://docs.python.org/dev/library/bisect.html#other-examples中的示例所示。

from random import random
from bisect import bisect

def weighted_choice(choices):
    values, weights = zip(*choices)
    total = 0
    cum_weights = []
    for w in weights:
        total += w
        cum_weights.append(total)
    x = random() * total
    i = bisect(cum_weights, x)
    return values[i]

>>> weighted_choice([("WHITE",90), ("RED",8), ("GREEN",2)])
'WHITE'

如果需要做出多个选择,可以将其分成两个函数,一个用于构建累积权重,另一个用于对随机点进行等分。

def weighted_choice(choices):
   total = sum(w for c, w in choices)
   r = random.uniform(0, total)
   upto = 0
   for c, w in choices:
      if upto + w >= r:
         return c
      upto += w
   assert False, "Shouldn't get here"