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)

我需要做这样的事情非常快速非常简单，从搜索的想法，我终于建立了这个模板。其思想是以json的形式从api接收加权值，这里是由dict模拟的。

然后将其转换为一个列表，其中每个值都与它的权重成比例地重复，只需使用random。选择从列表中选择一个值。

我尝试了10次、100次和1000次迭代。分布似乎很稳定。

def weighted_choice(weighted_dict):
    """Input example: dict(apples=60, oranges=30, pineapples=10)"""
    weight_list = []
    for key in weighted_dict.keys():
        weight_list += [key] * weighted_dict[key]
    return random.choice(weight_list)

我看了指向的其他线程，并在我的编码风格中提出了这种变化，这返回了用于计数的索引，但返回字符串很简单(注释返回替代):

import random
import bisect

try:
    range = xrange
except:
    pass

def weighted_choice(choices):
    total, cumulative = 0, []
    for c,w in choices:
        total += w
        cumulative.append((total, c))
    r = random.uniform(0, total)
    # return index
    return bisect.bisect(cumulative, (r,))
    # return item string
    #return choices[bisect.bisect(cumulative, (r,))][0]

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

tally = [0 for item in choices]

n = 100000
# tally up n weighted choices
for i in range(n):
    tally[weighted_choice(choices)] += 1

print([t/sum(tally)*100 for t in tally])

下面是使用numpy的另一个版本的weighted_choice。传入weights向量，它将返回一个由0组成的数组，其中包含一个1，表示所选择的bin。该代码默认只进行一次绘制，但您可以传入绘制的数量，并且将返回每个绘制的bin的计数。

如果权重向量的和不等于1，它将被规范化，使之等于1。

import numpy as np

def weighted_choice(weights, n=1):
    if np.sum(weights)!=1:
        weights = weights/np.sum(weights)

    draws = np.random.random_sample(size=n)

    weights = np.cumsum(weights)
    weights = np.insert(weights,0,0.0)

    counts = np.histogram(draws, bins=weights)
    return(counts[0])

如果您的加权选项列表是相对静态的，并且您希望频繁采样，则可以执行一个O(N)预处理步骤，然后使用相关答案中的函数在O(1)中进行选择。

# run only when `choices` changes.
preprocessed_data = prep(weight for _,weight in choices)

# O(1) selection
value = choices[sample(preprocessed_data)][0]

使用numpy

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