Different pseudo random numbers between C++ and Python implementations

Question

I am trying to reproduce the C++ code into Python 3.6, but the sequence of pseudo random numbers is different in each implementation. The seed are the same on both implementation and as far as I know, both use Mersenne Twister algorithm.

What am I doing wrong?

REMEMBER1: Both codes uses the SAME seed

REMEMBER2: As far as I know, both code uses functions that implemente the SAME algorithm (Mersenne Twister).

C++:

#include <random>
#include <iostream>
int main(int argc, char* argv[])
{  
    std::mt19937 gen(2);
    std::uniform_int_distribution<> dis(0, 61);

    for (int n=0; n<10; ++n)
        std::cout << dis(gen) << ' ';

    return 0;
}

Python 3.6:

import numpy as np
rng = np.random.RandomState(2)
for i in range(10):
    print(str(rng.randint(0, 62)))

Note: randint has an exclusive upper bound. That is why I use 61 on C++ code, but 62 on Python code.

Answer 1

You should note that C++'s standard library distributions, including std::uniform_int_distribution , use implementation-defined algorithms. In other words, these implementations may change depending on which C++ library implementation you choose, and those libraries may change those algorithms in the future. (This is in contrast to C++'s random engine classes, such as std::mt19937 , which do guarantee returning the same pseudorandom values from the same seed.) See also this answer .

Your best course of action is to implement or find a stable implementation of an RNG algorithm (such as an algorithm I describe in my article ) and implement methods to transform the random numbers they deliver. (There are certain things to keep in mind when choosing an RNG for a particular application; the first article I linked here has more information.)

Answer 2

There isn't one unique way of getting from a RNG to a single bounded int. See for example:

http://www.pcg-random.org/posts/bounded-rands.html

Which has several versions. Note that C++ and Python take different options here, hence you'll get a different sequence from the "same" RNG and seed.