在python 3.5中使用random.choices()的另一种方法是什么

Question

由于在python 3.5中不提供random.choices((，因此该函数有其他方法吗？

Answer 1

按照Mark Dickinson 的建议将代码从python 3.6复制到

from itertools import accumulate as _accumulate, repeat as _repeat
from bisect import bisect as _bisect
import random
def choices(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.
"""
n = len(population)
if cum_weights is None:
if weights is None:
_int = int
n += 0.0    # convert to float for a small speed improvement
return [population[_int(random.random() * n)] for i in _repeat(None, k)]
cum_weights = list(_accumulate(weights))
elif weights is not None:
raise TypeError('Cannot specify both weights and cumulative weights')
if len(cum_weights) != n:
raise ValueError('The number of weights does not match the population')
bisect = _bisect
total = cum_weights[-1] + 0.0   # convert to float
hi = n - 1
return [population[bisect(cum_weights, random.random() * total, 0, hi)]
for i in _repeat(None, k)]

现在您可以平静地使用choices功能了！

Answer 2

我并没有完全爱上Venkatesh Mondi的答案，但它完全是实用的。

import random
def random_choices(population, weights=None, k=1):
"""
This function achieves the same result. 
If weights is not provided, it returns a list of k random choices made using random.
choice with no weighting. If weights is provided, it first normalizes the weights by 
dividing each weight by the sum of all the weights, and then returns a list of k random 
choices made using random.choice with the normalized weights.
"""
if weights is None:
return [random.choice(population) for _ in range(k)]
total = sum(weights)
weights = [weight/total for weight in weights]
cumulative_weights = [sum(weights[:i+1]) for i in range(len(weights))]
return [population[cumulative_weights.index(next(x for x in cumulative_weights if x >= random.random()))] for _ in range(k)]
#note you may also do something like: 
return population[weights.index(random.choice(weights))]
print(random_choices(population, weights=weights, k=1)[0])

这是一个我已经使用了一段时间的替代方案，我不确定是有人先写的还是我写的。