选择随机种子并保存 [英] Choose random seed and save it
本文介绍了选择随机种子并保存的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！
问题描述
我想为numpy.random选择一个随机种子并将其保存到变量中.我可以使用numpy.random.seed(seed=None设置种子，但是如何让numpy选择一个随机种子并告诉您它是什么?
I would like to choose a random seed for numpy.random and save it to a variable. I can set the seed using numpy.random.seed(seed=None) but how do you get numpy to choose a random seed and tell you what it is?
默认情况下，数字似乎在Linux上使用/dev/urandom.
Number seems to use /dev/urandom on linux by default.
推荐答案
MT19937的完整状态RandomState底下的a> PRNG不能包含在单个(正常大小，例如32位或64位)整数中.它的状态为624个32位整数的数组.实际上，使用整数进行播种会运行更小，更简单的PRNG，以生成这624个字.这是人类手动将PRNG的状态手动设置为可以一致复制的 a 状态的简便方法.但是 most 指出PRNG不能还原为一个方便的32位整数.初始化程序PRNG不能以这种方式向后"工作.相反，RandomState的整个状态包含在该624条目数组中.您可以使用 get_state() 和 set_state() 方法.
The full state of the MT19937 PRNG that underlies RandomState cannot be contained in a single (normally-sized, e.g. 32-bit or 64-bit) integer. It has an array of 624 32-bit integers for its state. Seeding with an integer actually runs a smaller, simpler PRNG to generate those 624 words. It is just a convenient way for humans to manually set the state of the PRNG to a state that can be consistently replicated. But most states that the PRNG gets into cannot be reduced back to a convenient 32-bit integer. That initializer PRNG cannot work "backwards" in this way. Instead, the whole state of RandomState is contained in that 624-entry array. You can get this array and set it using the get_state() and set_state() methods.
>>> import numpy as np
>>> prng = np.random.RandomState()
>>> state = prng.get_state()
>>> state
('MT19937',
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3609356614, 2748019645, 638526248, 3265491895, 2839687161, 913026615, 2748040592, 975131382, 83378202, 4236013846, 764917668, 1887262417], dtype=uint32),
624,
0,
0.0)
>>> prng.random_sample()
0.20598058788141316
>>> prng.random_sample()
0.6864005375257146
>>> prng.random_sample()
0.08407651896523582
>>> prng.set_state(state)
>>> prng.random_sample()
0.20598058788141316
>>> prng.random_sample()
0.6864005375257146
您还可以腌制RandomState对象.我们使用get_state()数据实现了此功能，因此它将可靠地重现PRNG的状态.
You can also pickle RandomState objects. We implemented this using the get_state() data, so it will reliably reproduce the state of the PRNG. Depending on exactly what you want to do (you don't say), this is frequently the most convenient thing to do rather than mucking about with get_state() and set_state() manually.
>>> import cPickle
>>> pickled = cPickle.dumps(prng)
>>> prng.random_sample()
0.08407651896523582
>>> prng.random_sample()
0.3501860271954601
>>> prng2 = cPickle.loads(pickled)
>>> prng2.random_sample()
0.08407651896523582
>>> prng2.random_sample()
0.3501860271954601
这篇关于选择随机种子并保存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！
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