Use std::random_device to generate the seed. It’ll provide non-deterministic random numbers, provided your implementation supports it. Otherwise it’s allowed to use some other random number engine. std::mt19937_64 prng; seed = std::random_device{}(); prng.seed(seed); operator() of std::random_device returns an unsigned int, so if your platform has 32-bit ints, and you want a 64-bit seed, you’ll need to … Read more
If you are using C++ and are concerned about good distribution you can use TR1 C++11 <random>. #include <random> std::random_device rseed; std::mt19937 rgen(rseed()); // mersenne_twister std::uniform_int_distribution<int> idist(0,100); // [0,100] std::cout << idist(rgen) << std::endl;
For C++, Boost.Random is probably what you’re looking for. It has support for MT (among many other algorithms), and can collect entropy via the nondet_random class. Check it out! 🙂
Mersenne Twister is a shift-register based pRNG (pseudo-random number generator) and is therefore subject to bad seeds with long runs of 0s or 1s that lead to relatively predictable results until the internal state is mixed up enough. However the constructor which takes a single value uses a complicated function on that seed value which … Read more
The short answer is that you simply can’t (at least not in general). The Mersenne Twister RNG used by numpy has 219937-1 possible internal states, whereas a single 64 bit integer has only 264 possible values. It’s therefore impossible to map every RNG state to a unique integer seed. You can get and set the … Read more