如何让升压::随机和Matlab产生相同随机数 [英] How to let Boost::random and Matlab produce the same random numbers

问题描述

要检查我的C ++ code，我希望能够让升压::随机和Matlab产生相同随机数。

To check my C++ code, I would like to be able to let Boost::Random and Matlab produce the same random numbers.

因此​​，对于升压我用code：

So for Boost I use the code:

boost::mt19937 var(static_cast<unsigned> (std::time(0)));
boost::uniform_int<> dist(1, 6);
boost::variate_generator<boost::mt19937&, boost::uniform_int<> > die(var, dist);
die.engine().seed(0);     
for(int i = 0; i < 10; ++i) {
    std::cout << die() << " ";
}      
std::cout    << std::endl;

将会产生（程序的每次运行）：结果
4 4 5 6 4 6 4 6 3 4

而对于MATLAB我使用：

And for matlab I use:

RandStream.setDefaultStream(RandStream('mt19937ar','seed',0));
randi(6,1,10)

将会产生（程序的每次运行）：结果
5 6 1 6 4 1 2 4 6 6

这是奇怪的，因为都使用相同的算法，相同的种子。
我怎么错过？

Which is bizarre, since both use the same algorithm, and same seed. What do I miss?

看来，Python的（使用numpy的）和Matlab似乎不相上下，在随机统一编号：
Matlab的

It seems that Python (using numpy) and Matlab seems comparable, in the random uniform numbers: Matlab

RandStream.setDefaultStream（RandStream（'mt19937ar'，'种子'，203））;兰特（1,10）

RandStream.setDefaultStream(RandStream('mt19937ar','seed',203));rand(1,10)

0.8479 0.1889 0.4506 0.6253 0.9697 0.2078 0.5944 0.9115 0.2457 0.7743

的Python：
random.seed（203）; random.random（10）

Python: random.seed(203);random.random(10)

阵列（[0.84790006，0.18893843，0.45060688，0.62534723，0.96974765，
        0.20780668，0.59444858，0.91145688，0.24568615，0.77430378]）

C ++升压

0.8479 0.667228 0.188938 0.715892 0.450607 0.0790326 0.625347 0.972369 0.969748 0.858771

这是等同于以往其他Python和Matlab值...

Which is identical to ever other Python and Matlab value...

推荐答案

我有其他的答案同意，指出这些发电机都没有绝对。它们可以根据实现产生不同的结果。我认为，最简单的解决办法是实现自己的发电机。它看起来令人生畏（梅森倍捻机是肯定的方式），但看看 Xorshift ，一个极其简单但强大的一个。我复制维基百科的链接中给出的C实现：

I have to agree with the other answers, stating that these generators are not "absolute". They may produce different results according to the implementation. I think the simplest solution would be to implement your own generator. It might look daunting (Mersenne twister sure is by the way) but take a look at Xorshift, an extremely simple though powerful one. I copy the C implementation given in the Wikipedia link :

uint32_t xor128(void) {
  static uint32_t x = 123456789;
  static uint32_t y = 362436069;
  static uint32_t z = 521288629;
  static uint32_t w = 88675123;
  uint32_t t;

  t = x ^ (x << 11);
  x = y; y = z; z = w;
  return w = w ^ (w >> 19) ^ (t ^ (t >> 8));
}

要具有相同的种子，只要把你想INT X，Y，Z的任何值，W（除（0,0,0,0）我相信）。你只需要确保Matlab与C ++同时使用32位对于这些unsigned int类型。

To have the same seed, just put any values you want int x,y,z,w (except(0,0,0,0) I believe). You just need to be sure that Matlab and C++ use both 32 bit for these unsigned int.

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