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。
在Udacity免费课程AI for Robotics中，Sebastien Thurn对此进行了演讲。基本上，他用mod运算符%做了一个权重索引的圆形数组，将变量beta设为0，随机选择一个索引，
for循环遍历N，其中N是指标的数量，在for循环中，首先按公式增加beta:
Beta = Beta +来自{0…2 * Weight_max}
然后在for循环中嵌套一个while循环per:
while w[index] < beta:
beta = beta - w[index]
index = index + 1
select p[index]
然后到下一个索引，根据概率(或课程中介绍的情况下的归一化概率)重新采样。
在Udacity上找到第8课，机器人人工智能的第21期视频，他正在讲粒子滤波器。
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。
我可能已经来不及提供任何有用的东西了，但这里有一个简单，简短，非常有效的片段:
def choose_index(probabilies):
cmf = probabilies[0]
choice = random.random()
for k in xrange(len(probabilies)):
if choice <= cmf:
return k
else:
cmf += probabilies[k+1]
不需要排序你的概率或用你的cmf创建一个向量，它一旦找到它的选择就会终止。内存:O(1)，时间:O(N)，平均运行时间~ N/2。
如果你有权重，只需添加一行:
def choose_index(weights):
probabilities = weights / sum(weights)
cmf = probabilies[0]
choice = random.random()
for k in xrange(len(probabilies)):
if choice <= cmf:
return k
else:
cmf += probabilies[k+1]
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])
