使用集合显示非确定性行为的种子 Python RNG [英] Seeded Python RNG showing non-deterministic behavior with sets

问题描述

在尝试从集合中选择伪随机元素时，我看到了非确定性行为，即使 RNG 已播种(示例代码如下所示).为什么会发生这种情况，我是否应该期望其他 Python 数据类型表现出类似的行为?

I'm seeing non-deterministic behavior when trying to select a pseudo-random element from sets, even though the RNG is seeded (example code shown below). Why is this happening, and should I expect other Python data types to show similar behavior?

注意:我只在 Python 2.7 上测试过，但它可以在两台不同的 Windows 计算机上重现.

Notes: I've only tested this on Python 2.7, but it's been reproducible on two different Windows computers.

类似问题:Python 随机种子中的问题不使用遗传编程示例代码 可能是类似的.根据我的测试，我的假设是集合内运行到运行的内存分配差异导致为相同的 RNG 状态选取不同的元素.

Similar Issue: The issue at Python random seed not working with Genetic Programming example code may be similar. Based on my testing, my hypothesis is that run-to-run memory allocation differences within the sets is leading to different elements getting picked up for the same RNG state.

到目前为止，我还没有在 Python 文档中发现任何关于 set 或 random 的警告/问题.

So far I haven't found any mention of this kind of caveat/issue in the Python docs for set or random.

示例代码(randTest 产生不同的输出运行到运行):

Example Code (randTest produces different output run-to-run):

import random

''' Class contains a large set of pseudo-random numbers. '''
class bigSet:
    def __init__(self):
        self.a = set()
        for n in range(2000):
            self.a.add(random.random())
        return


''' Main test function. '''
def randTest():
    ''' Seed the PRNG. '''
    random.seed(0)

    ''' Create sets of bigSet elements, presumably many memory allocations. ''' 
    b = set()
    for n in range (2000):
        b.add(bigSet())

    ''' Pick a random value from a random bigSet. Would have expected this to be deterministic. '''    
    c = random.sample(b,1)[0]
    print('randVal: ' + str(random.random()))           #This value is always the same
    print('setSample: ' + str(random.sample(c.a,1)[0])) #This value can change run-to-run
    return

推荐答案

它与可变对象的对象实例化有关.如果我创建 frozenset 的 set 它确实给出了确定性的结果；

It has to do with object instantiation of mutable objects. if I create a set of frozenset it does give a deterministic result;

Python 2.7.11 (default, Jan  9 2016, 15:47:04) 
[GCC 4.2.1 Compatible FreeBSD Clang 3.4.1 (tags/RELEASE_34/dot1-final 208032)] on freebsd10
Type "help", "copyright", "credits" or "license" for more information.
>>> import random
>>> random.seed(0)
>>> set(frozenset(random.random() for i in range(5)) for j in range(5))
set([frozenset([0.7298317482601286, 0.3101475693193326, 0.8988382879679935, 0.47214271545271336, 0.6839839319154413]), frozenset([0.5833820394550312, 0.4765969541523558, 0.4049341374504143, 0.30331272607892745, 0.7837985890347726]), frozenset([0.7558042041572239, 0.5046868558173903, 0.9081128851953352, 0.28183784439970383, 0.6183689966753316]), frozenset([0.420571580830845, 0.25891675029296335, 0.7579544029403025, 0.8444218515250481, 0.5112747213686085]), frozenset([0.9097462559682401, 0.8102172359965896, 0.9021659504395827, 0.9827854760376531, 0.25050634136244054])])
>>> random.seed(0)
>>> set(frozenset(random.random() for i in range(5)) for j in range(5))
set([frozenset([0.7298317482601286, 0.3101475693193326, 0.8988382879679935, 0.47214271545271336, 0.6839839319154413]), frozenset([0.5833820394550312, 0.4765969541523558, 0.4049341374504143, 0.30331272607892745, 0.7837985890347726]), frozenset([0.7558042041572239, 0.5046868558173903, 0.9081128851953352, 0.28183784439970383, 0.6183689966753316]), frozenset([0.420571580830845, 0.25891675029296335, 0.7579544029403025, 0.8444218515250481, 0.5112747213686085]), frozenset([0.9097462559682401, 0.8102172359965896, 0.9021659504395827, 0.9827854760376531, 0.25050634136244054])])
>>> 

如果我没记错的话，CPython 使用(可变)对象的内存位置作为它的 id 和哈希键.

If I'm not mistaken, CPython uses a (mutable) object's memory location as it's id and as the key for hashing.

因此，虽然对象的内容总是相同的，但它的 id 会有所不同；

So while the contents of the objects will always be the same, it's id will be different;

In [13]: random.seed(0)

In [14]: k = set()

In [15]: for n in range (20):
    k.add(bigSet())
   ....:     

In [16]: for x in k:
    print(id(x))
   ....:     
34856629808
34856629864
34856631936
34856630424
34856629920
34856631992
34856630480
34856629976
34856632048
34856631040
34856630536
34856632104
34856630032
34856630592
34856630088
34856632160
34856629752
34856629696
34856630760
34856630256

In [17]: random.seed(0)

In [18]: k = set()

In [19]: for n in range (20):
   ....:         k.add(bigSet())
   ....:     

In [20]: for x in k:
   ....:         print(id(x))
   ....:     
34484534800
34856629808
34484534856
34856629864
34856631936
34856630424
34856629920
34856631992
34484534968
34856629976
34856630480
34856632048
34856631040
34484535024
34484535080
34484535136
34856632216
34484534688
34484534912
34484534744

一种可能的解决方案是对冻结集进行子类化.

A possible solution would be to subclass a frozen set.

这篇关于使用集合显示非确定性行为的种子 Python RNG的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！