我可以创建一个本地 numpy 随机种子吗? [英] Can I create a local numpy random seed?

问题描述

有一个函数，foo，它使用了np.random 功能.我想控制 foo 使用的种子，但不实际更改函数本身.我该怎么做?

基本上我想要这样的东西:

bar() # 应该有正常的种子with np.random.seed(0): # 不起作用富()bar() # 应该有正常的种子

<小时>

解决方案如这个:

rng = random.Random(42)数字 = rng.randint(10, 20)

在这种情况下不起作用，因为我无法访问 foo 的内部工作原理(或者我错过了什么??).

解决方案

你可以将全局随机状态保存在一个临时变量中，并在你的函数完成后重置它:

导入上下文库将 numpy 导入为 [e-mail] temp_seed(种子):状态 = np.random.get_state()np.random.seed(种子)尝试:屈服最后:np.random.set_state(状态)

演示:

<预><代码>>>>np.random.seed(0)>>>np.random.randn(3)数组([1.76405235，0.40015721，0.97873798])>>>np.random.randn(3)数组([2.2408932，1.86755799，-0.97727788])>>>np.random.seed(0)>>>np.random.randn(3)数组([1.76405235，0.40015721，0.97873798])>>>使用 temp_seed(5):... np.random.randn(3)数组([0.44122749，-0.33087015，2.43077119])>>>np.random.randn(3)数组([2.2408932，1.86755799，-0.97727788])

There is a function, foo, that uses the np.random functionality. I want to control the seed that foo uses, but without actually changing the function itself. How do I do this?

Essentially I want something like this:

bar() # should have normal seed
with np.random.seed(0): # Doesn't work
    foo()
bar() # should have normal seed

---

Solutions like this:

rng = random.Random(42)
number = rng.randint(10, 20)

doesn't work in this case, as I don't have access to the inner workings of foo (or am I missing something??).

解决方案

You could keep the global random state in a temporary variable and reset it once your function is done:

import contextlib
import numpy as np

@contextlib.contextmanager
def temp_seed(seed):
    state = np.random.get_state()
    np.random.seed(seed)
    try:
        yield
    finally:
        np.random.set_state(state)

Demo:

>>> np.random.seed(0)
>>> np.random.randn(3)
array([1.76405235, 0.40015721, 0.97873798])
>>> np.random.randn(3)
array([ 2.2408932 ,  1.86755799, -0.97727788])

>>> np.random.seed(0)
>>> np.random.randn(3)
array([1.76405235, 0.40015721, 0.97873798])
>>> with temp_seed(5):
...     np.random.randn(3)                                                                                        
array([ 0.44122749, -0.33087015,  2.43077119])
>>> np.random.randn(3)
array([ 2.2408932 ,  1.86755799, -0.97727788])

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