实现随机数发生器 [英] Implementation of random number generator

问题描述

可能重复：

随机数生成器如何工作？

我正在寻找一个随机数生成器在C / C ++的内部实现。基本上我有兴趣知道，当rand（）被调用时究竟发生了什么。在所有机器遵循一定的指令集后，如何随机！

编辑：想知道如何在C / C ++中实现一个。

I am looking for internal implementation of a random number generator in C/C++.Basically I am interested to know, what exactly happens when rand() is called. After all machine follows definite set of instructions, how can it be random!
Want to know how can I implement one in C/C++.

推荐答案

它们是伪随机数字生成器，而不是真正随机的。这通常是一个好的的东西，因为它允许你在涉及随机数字时更容易地再现错误。

They're pseudo-random number generators, not truly random ones. This is often a good thing since it allows you to reproduce bugs more easily where "random" numbers are involved.

获得随机数生成器，例如在Linux下读取 / dev / random ，但是通常不包含C库附带的常规生成器。

You can get random number generators, such as reading /dev/random under Linux but the normal ones that ship with C libraries generally aren't.

最简单的是线性同余发生器，其中：

The simplest one are linear congruential generators where:

n(x+1) = n(x) * A + C modulo M

< A ， C 和 M 。

LCG上的维基百科页面提供了各种实施所使用的一些示例值。例如，列出的 glibc 有 a = 1103515245，c = 12345，m = 2 ^ 31 一个简单的事情像：

Wikipedia's page on LCGs gives some sample values used by various implementations. For example, the glibc one listed there has a = 1103515245, c = 12345, m = 2^31 so it's a simple thing like:

static unsigned int seed = 1;
void srand (int newseed) {
    seed = (unsigned)newseed & 0x7fffffffU;
}
int rand (void) {
    seed = (seed * 1103515245U + 12345U) & 0x7fffffffU;
    return (int)seed;
}

Aside：glibc实现仍然有这个生成器在它内部（称为类型0生成器），但它也有一个fancier三重发生器，也是（可能）更好。

Aside: The glibc implementation still has this generator within it (called the Type 0 generator) but it also has a fancier trinomial generator as well, which is (presumably) better.

还有更复杂的（例如Mersenne捻线机）具有更大的周期时间（开始重复之前的时间）。

There are also more complex ones (such as the Mersenne twister) that have a much greater cycle time (time before starting to repeat).

任何真正的随机生成器必须使用真正的随机输入源，这是为什么 / dev / random 有时会阻塞（等待熵）而 / dev / urandom 不会。

Any truly random generator must use a truly random input source which is why /dev/random will sometimes block ("waiting for entropy") while /dev/urandom won't.

真正的随机源可能受按键，uers所需的数据，网络数据包内容，磁盘I / O模式，ICMP响应在网络上返回所需的时间以及各种其他令人惊奇的，主要是非显性的事情。

"Truly" random sources may be affected by timing between keystrokes, data enetered by uers, the contents of network packets, disk I/O patterns, time taken for an ICMP response to come back over the network and all sorts of other wondrous, mostly non-deteministic things.

除非你大量加入加密，正常随机数发生器将很好。

Unless you're heavily into crypto, normal random number generators will be just fine.

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