如果你有一个加权字典而不是一个列表，你可以这样写

items = { "a": 10, "b": 5, "c": 1 } 
random.choice([k for k in items for dummy in range(items[k])])

注意(k, k范围的虚拟物品(物品[k])]产生这个列表(' a ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' c ', ' b ', ' b ', ' b ', ' b ', ' b ']

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

粗糙的，但可能足够:

import random
weighted_choice = lambda s : random.choice(sum(([v]*wt for v,wt in s),[]))

这有用吗?

# define choices and relative weights
choices = [("WHITE",90), ("RED",8), ("GREEN",2)]

# initialize tally dict
tally = dict.fromkeys(choices, 0)

# tally up 1000 weighted choices
for i in xrange(1000):
    tally[weighted_choice(choices)] += 1

print tally.items()

打印:

[('WHITE', 904), ('GREEN', 22), ('RED', 74)]

假设所有权重都是整数。它们的和不一定是100，我这么做只是为了让测试结果更容易理解。(如果权重是浮点数，则将它们都乘以10，直到所有权重>= 1。)

weights = [.6, .2, .001, .199]
while any(w < 1.0 for w in weights):
    weights = [w*10 for w in weights]
weights = map(int, weights)

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]

另一种方法是，假设我们的权重与元素数组中的元素的下标相同。

import numpy as np
weights = [0.1, 0.3, 0.5] #weights for the item at index 0,1,2
# sum of weights should be <=1, you can also divide each weight by sum of all weights to standardise it to <=1 constraint.
trials = 1 #number of trials
num_item = 1 #number of items that can be picked in each trial
selected_item_arr = np.random.multinomial(num_item, weights, trials)
# gives number of times an item was selected at a particular index
# this assumes selection with replacement
# one possible output
# selected_item_arr
# array([[0, 0, 1]])
# say if trials = 5, the the possible output could be 
# selected_item_arr
# array([[1, 0, 0],
#   [0, 0, 1],
#   [0, 0, 1],
#   [0, 1, 0],
#   [0, 0, 1]])

现在我们假设，我们要在一次试验中抽取3个项目。你可以假设有三个球R、G、B大量存在，它们的权重由权重数组给定，可能的结果如下:

num_item = 3
trials = 1
selected_item_arr = np.random.multinomial(num_item, weights, trials)
# selected_item_arr can give output like :
# array([[1, 0, 2]])

您还可以将要选择的项目数量视为一组中二项/多项试验的数量。所以，上面的例子仍然可以作为工作

num_binomial_trial = 5
weights = [0.1,0.9] #say an unfair coin weights for H/T
num_experiment_set = 1
selected_item_arr = np.random.multinomial(num_binomial_trial, weights, num_experiment_set)
# possible output
# selected_item_arr
# array([[1, 4]])
# i.e H came 1 time and T came 4 times in 5 binomial trials. And one set contains 5 binomial trails.

如果不介意使用numpy，可以使用numpy.random.choice。

例如:

import numpy

items  = [["item1", 0.2], ["item2", 0.3], ["item3", 0.45], ["item4", 0.05]
elems = [i[0] for i in items]
probs = [i[1] for i in items]

trials = 1000
results = [0] * len(items)
for i in range(trials):
    res = numpy.random.choice(items, p=probs)  #This is where the item is selected!
    results[items.index(res)] += 1
results = [r / float(trials) for r in results]
print "item\texpected\tactual"
for i in range(len(probs)):
    print "%s\t%0.4f\t%0.4f" % (items[i], probs[i], results[i])

如果你知道你需要提前做多少选择，你可以不像这样循环:

numpy.random.choice(items, trials, p=probs)