使用集合显示不确定性行为的种子Python RNG

Question

当我试图从集合中选择一个伪随机元素时，我看到了非确定性行为，即使RNG是种子（下面显示的示例代码）。为什么会发生这种情况，我是否应该期望其他Python数据类型显示类似的行为？在

注意：我只在Python2.7上测试过这个，但它在两台不同的Windows计算机上都是可复制的。在

相似问题：Python random seed not working with Genetic Programming example code处的问题可能类似。根据我的测试，我的假设是，集合中运行到运行的内存分配差异导致不同的元素为同一RNG状态选取。在

到目前为止，我还没有在Python文档中找到任何关于set或random的警告/问题。在

示例代码（randTest生成不同的输出运行）：

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

Answer 1

我很确定您是正确的，这个问题是由set的运行间内存分配差异引起的。当我将程序更改为使用列表而不是集合时，我得到了确定性行为：

import random

''' Class contains a large list of pseudo-random numbers. '''
class bigList:
    def __init__(self):
        self.a = [random.random() for n in range(2000)]

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

    ''' Create lists of bigList elements, presumably many memory allocations. '''
    b = [bigList() for n in range(2000)]

    ''' 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
    # and so is this now...
    print('setSample: ' + str(random.sample(c.a, 1)[0]))

randTest()

Answer 2

它与可变对象的对象实例化有关。如果我创建一个set的frozenset，它确实会给出一个确定的结果

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])])
>>> 

如果我没搞错的话，CPython使用（可变）对象的内存位置作为它的id和散列键。在

因此，虽然对象的内容总是相同的，但它的id将不同

^{pr2}$

一个可能的解决方案是将一个冻结集的子类化。在