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

如果你没有提前定义你想要选择多少项(所以,你没有做k=10这样的事情),你只有概率,你可以做下面的事情。注意,你的概率加起来不需要等于1,它们可以相互独立:

soup_items = ['pepper', 'onion', 'tomato', 'celery'] 
items_probability = [0.2, 0.3, 0.9, 0.1]

selected_items = [item for item,p in zip(soup_items,items_probability) if random.random()<p]
print(selected_items)
>>>['pepper','tomato']

其他回答

import numpy as np
w=np.array([ 0.4,  0.8,  1.6,  0.8,  0.4])
np.random.choice(w, p=w/sum(w))

下面是Python 3.6标准库中包含的版本:

import itertools as _itertools
import bisect as _bisect

class Random36(random.Random):
    "Show the code included in the Python 3.6 version of the Random class"

    def choices(self, population, weights=None, *, cum_weights=None, k=1):
        """Return a k sized list of population elements chosen with replacement.

        If the relative weights or cumulative weights are not specified,
        the selections are made with equal probability.

        """
        random = self.random
        if cum_weights is None:
            if weights is None:
                _int = int
                total = len(population)
                return [population[_int(random() * total)] for i in range(k)]
            cum_weights = list(_itertools.accumulate(weights))
        elif weights is not None:
            raise TypeError('Cannot specify both weights and cumulative weights')
        if len(cum_weights) != len(population):
            raise ValueError('The number of weights does not match the population')
        bisect = _bisect.bisect
        total = cum_weights[-1]
        return [population[bisect(cum_weights, random() * total)] for i in range(k)]

来源:https://hg.python.org/cpython/file/tip/Lib/random.py l340

如果你碰巧有Python 3,并且害怕安装numpy或编写自己的循环,你可以这样做:

import itertools, bisect, random

def weighted_choice(choices):
   weights = list(zip(*choices))[1]
   return choices[bisect.bisect(list(itertools.accumulate(weights)),
                                random.uniform(0, sum(weights)))][0]

因为你可以用一袋管道适配器做任何东西!尽管……我必须承认,尼德的回答虽然稍长一些,但比较容易理解。

我不喜欢它们的语法。我只想具体说明这些项目是什么以及每项的权重是多少。我意识到我可以用随机。选项,但我很快就写了下面的类。

import random, string
from numpy import cumsum

class randomChoiceWithProportions:
    '''
    Accepts a dictionary of choices as keys and weights as values. Example if you want a unfair dice:


    choiceWeightDic = {"1":0.16666666666666666, "2": 0.16666666666666666, "3": 0.16666666666666666
    , "4": 0.16666666666666666, "5": .06666666666666666, "6": 0.26666666666666666}
    dice = randomChoiceWithProportions(choiceWeightDic)

    samples = []
    for i in range(100000):
        samples.append(dice.sample())

    # Should be close to .26666
    samples.count("6")/len(samples)

    # Should be close to .16666
    samples.count("1")/len(samples)
    '''
    def __init__(self, choiceWeightDic):
        self.choiceWeightDic = choiceWeightDic
        weightSum = sum(self.choiceWeightDic.values())
        assert weightSum == 1, 'Weights sum to ' + str(weightSum) + ', not 1.'
        self.valWeightDict = self._compute_valWeights()

    def _compute_valWeights(self):
        valWeights = list(cumsum(list(self.choiceWeightDic.values())))
        valWeightDict = dict(zip(list(self.choiceWeightDic.keys()), valWeights))
        return valWeightDict

    def sample(self):
        num = random.uniform(0,1)
        for key, val in self.valWeightDict.items():
            if val >= num:
                return key
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"