如何"stash" 随机状态生成器状态

Question

我正在为随机数生成器播种以获得可重现的结果：

import random

SEED = 32412542
random.seed(SEED)

我想让它 return "non-reproducible" 只为程序的一部分设置随机值，如：

import random

SEED = 32412542
random.seed(SEED)

my_list = [1, 2, 3, 4, 5]

res = random.sample(my_list, len(my_list))  # I would like result of this to be the same between runs of the program.

# Do some reproducible calculations, such as training neural network.
print(res)  # E.g. prints [3, 2, 4, 1, 5]

# What to do here?
res = random.sample(my_list, len(my_list))  # I would like result of this to be different between runs.

# Do some non-reproducible calculations, such as picking neural network parameters randomly.
print(res)  # Prints some random order.

res = random.sample(my_list, len(my_list))  # I would like result of this to be the same between runs of the program.

# Do some reproducible calculations, such as training neural network.
print(res)  # E.g. prints [2, 3, 1, 4, 5]

到目前为止我想出的是在我希望它变得不可复制之前不带参数播种，然后用 SEED 值重新播种：

import random

SEED = 32412542
random.seed(SEED)

my_list = [1, 2, 3, 4, 5]

res = random.sample(my_list, len(my_list))
print(res)  # Prints: [3, 2, 4, 1, 5]

random.seed()
res = random.sample(my_list, len(my_list))
print(res)  # Prints some random order.

random.seed(SEED)
res = random.sample(my_list, len(my_list))
print(res)  # Prints: [3, 2, 4, 1, 5], so exactly what has been printed before.

问题是，在重新播种后，会产生完全相同的一组随机值（显然 - 最后这是用特定值播种的目的），我不想发生这种情况。我想以某种方式恢复随机生成器的先前状态。这可能吗？

Answer 1

您不能使用 random 函数执行此操作，但可以通过创建 Random class 的实例来实现。 As the documentation states:

Class Random can also be subclassed if you want to use a different basic generator of your own devising: in that case, override the random(), seed(), getstate(), and setstate() methods. Optionally, a new generator can supply a getrandbits() method — this allows randrange() to produce selections over an arbitrarily large range.

示例：

>>> import random
>>> r = random.Random()
>>> r.randint(1, 1000)
545
>>> r.randint(1, 1000)
349
>>> r.randint(1, 1000)
745
>>> r.randint(1, 1000)
792
>>> state = r.getstate()
>>> r.randint(1, 1000)
52
>>> r.randint(1, 1000)
799
>>> r.randint(1, 1000)
586
>>> r.randint(1, 1000)
581
>>> r.setstate(state)
>>> r.randint(1,1000)
52
>>> r.randint(1,1000)
799
>>> r.randint(1,1000)
586
>>> r.randint(1,1000)
581

---

其实you can even using the functions from the random module，我的错：

random.getstate() Return an object capturing the current internal state of the generator. This object can be passed to setstate() to restore the state.

random.setstate(state) state should have been obtained from a previous call to getstate(), and setstate() restores the internal state of the generator to what it was at the time getstate() was called.