问题与种子一个伪随机数发生器不止一次？ [英] Issues with seeding a pseudo-random number generator more than once?

问题描述

我已经看到了相当多的建议，不是每次执行一次以上的伪随机数生成器种子，但从来没有一个彻底的解释。当然，很容易看出为什么下面的（C / C ++）例子不是个好主意：

I've seen quite a few recommendations for not seeding pseudo-random number generators more than once per execution, but never accompanied by a thorough explanation. Of course, it is easy to see why the following (C/C++) example is not a good idea:

int get_rand() {
  srand(time(NULL));
  return rand();
}

<每秒数次产生重复结果。

since calling get_rand several times per second produces repeated results.

但是，以下示例仍然是可接受的解决方案？

MyRand.h

#ifndef MY_RAND_H
#define MY_RAND_H

class MyRand
{
  public:
    MyRand();
    int get_rand() const;
  private:
    static unsigned int seed_base;
};

#endif

MyRand.cpp / p>

MyRand.cpp

#include <ctime>
#include <cstdlib>
#include "MyRand.h"

unsigned int MyRand::seed_base = static_cast<unsigned int>(time(NULL));

MyRand::MyRand()
{
  srand(seed_base++);
}

int MyRand::get_rand() const
{
  return rand();
}

main.cpp
$ b

main.cpp

#include <iostream>
#include "MyRand.h"

int main(int argc, char *argv[]) 
{
  for (int i = 0; i < 100; i++) 
  {
    MyRand r;
    std::cout << r.get_rand() << " ";
  }
}

即使 MyRand ：s构造函数被快速连续调用几次，每次调用 srand 都有不同的参数。显然，这不是线程安全的，但是再次不是 rand 。

i.e. even though MyRand:s constructor is called several times in rapid succession, each call to srand has a different parameter. Obviously, this is not thread-safe, but then again neither is rand.

推荐答案

每次调用伪随机数生成器函数时，生成器都会采用一些内部状态，并生成伪随机数和新的内部状态。仔细选择用于转换内部状态的算法，使得输出看起来是随机的。

Each time you call a pseudo-random number generator function, the generator takes some internal state and produces a pseudo-random number and a new internal state. The algorithm for transforming the internal state is carefully chosen so the output appears random.

当你播种随机数生成器时，你基本上设置这个内部状态。如果将内部状态重置为某个可预测的值，则会丢失随机性的外观。

When you seed the random number generator, you're basically setting this internal state. If you reset the internal state to some predictable value, you'll lose the appearance of randomness.

例如，一个流行的简单RNG是线性并发生成器。数字生成如下：

For example, a popular, simple RNG is a linear congruential generator. Numbers are generated like this:

X[n+1] = (a X[n] + c) mod m

在这种情况下，X [n + 1]是结果和新的内部状态。如果你按照上面的建议每次生成生成器，你会得到一个如下的序列：

In this case, X[n+1] is both the result and the new internal state. If you seed the generator every time as you suggest above, you'll get a sequence that looks like this:

{(ab + c) mod m, (a(b+1) + c) mod m, (a(b+2) + c) mod m, ...}

其中b是 seed_base 。这看起来不是随机的。

where b is your seed_base. This doesn't look random at all.

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