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是否有任何现成的库，以便 numpy 程序可以使用英特尔硬件 prng（rdrand）来填充随机数的缓冲区？

如果失败了，有人可以为我可以适应或使用的一些 C 代码指明正确的方向（我将 CPython 和 Cython 与 numpy 一起使用，因此最小的包装器 shd 就足够了）。

我想要的随机生成器是 [0,1) 之间的统一随机数。

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3 回答 3

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This code will use /dev/urandom (Unix) or CryptGenRandom APIs (Windows). Which RNG is used, hardware or software, is dependent on the operating system.

If you want to control exactly which generator is used, you must query it through its hardware driver or library. When you have the random bits as a string, you proceeed similarly to this code, using np.fromstring.

Normally we can trust the operating system to use the best entropy sources for its cryptographic services, including the random bit generator. If there is a hardware RNG it is likely to be used, usually combination with other entropy sources.

import os
import numpy as np

def cryptorand(n):
    a = np.fromstring(os.urandom(n*4), dtype=np.uint32) >> 5
    b = np.fromstring(os.urandom(n*4), dtype=np.uint32) >> 6
    return (a * 67108864.0 + b) / 9007199254740992.0

Here is the distribution of 1,000,000 random deviates generated with this method on Mac OS X. As you can see it is quite uniform on [0,1):

enter image description here

If you need really strong cryptographic random deviates, you can use /dev/random instead of /dev/urandom. This applies only to Unix-like systems, not Windows:

import numpy as np

def cryptorand(n):
    with open('/dev/random','rb') as rnd:
        a = np.fromstring(rnd.read(n*4), dtype=np.uint32) >> 5
        b = np.fromstring(rnd.read(n*4), dtype=np.uint32) >> 6
        return (a * 67108864.0 + b) / 9007199254740992.0

Note that this function might block, unlike the version that uses os.urandom as entropy source.

(Edit1: Updated normalization to equate that of NumPy)

Edit 2: The comments indicates the reason for the question was speed, not cryptographic strength. The purpose of hardware RNG is not speed but strength, so it makes the question invalid. However, a fast and good PRNG which can be an alternative to the Mersenne Twister is George Marsaglia's multiply-with-carry generator. Here is a simple implementation in Cython:

import numpy as np
cimport numpy as cnp

cdef cnp.uint32_t gw = 152315241 # any number except 0 or 0x464fffff
cdef cnp.uint32_t gz = 273283728 # any number except 0 or 0x9068ffff

def rand(cnp.intp_t n):
    """Generate n random numbers using George Marsaglia's 
    multiply-with-carry method."""
    global gw, gz
    cdef cnp.uint32_t w, z, a, b
    cdef cnp.intp_t i
    cdef cnp.ndarray x = cnp.PyArray_SimpleNew(1, &n, cnp.NPY_FLOAT64)
    cdef cnp.float64_t *r = <cnp.float64_t*> cnp.PyArray_DATA(x)
    w,z = gw,gz
    for i in range(n): 
        z = 36969 * (z & 65535) + (z >> 16)
        w = 18000 * (w & 65535) + (w >> 16)
        a = (z << 16) + w
        z = 36969 * (z & 65535) + (z >> 16)
        w = 18000 * (w & 65535) + (w >> 16)
        b = (z << 16) + w
        r[i] = (a * 67108864.0 + b) / 9007199254740992.0
    gw,gz = w,z
    return x

Beware that neither Mersenne Twister nor multiply-with-carry have cryptographic strength.

于 2014-03-27T12:44:04.543 回答
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是的。 有一个 rdrand 模块。

于 2015-12-28T10:36:21.547 回答
0

这里有一篇论文（http://iopscience.iop.org/article/10.3847/1538-4357/aa7ede/meta;jsessionid=A9DA9DDB925E6522D058F3CEEC7D0B21.ip-10-40-2-120）和（非付费版本）此处 ( https://arxiv.org/abs/1707.02212 ) 描述了如何使用英特尔安全密钥，这是一种在芯片上实现的加密安全随机数生成器。它可以通过 RdRand 和 RdSeed 指令访问。

但是作者似乎说你应该去用 C/C++ 而不是 Python 来实现它。rdrand python 模块的运行速度比 Python 默认随机数生成器慢约 100 倍，比 Numpy 中的慢约 1000 倍（论文第 5.2 节）。

于 2017-11-13T22:03:10.643 回答