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
The rdtsc instruction is a pretty reliable (and random) seed. In Windows it’s accessible via the __rdtsc() intrinsic. In GNU C, it’s accessible via: unsigned long long rdtsc(){ unsigned int lo,hi; __asm__ __volatile__ (“rdtsc” : “=a” (lo), “=d” (hi)); return ((unsigned long long)hi << 32) | lo; } The instruction measures the total pseudo-cycles since … Read more
A much more simple way of getting a seed is to generate one and store that as the seed. I am using this method for a game and want to give the player the option to generate the exact same world if he wishes too. So first I create a Random object without a seed … Read more
Should I use np.random.seed or random.seed? That depends on whether in your code you are using numpy’s random number generator or the one in random. The random number generators in numpy.random and random have totally separate internal states, so numpy.random.seed() will not affect the random sequences produced by random.random(), and likewise random.seed() will not affect … 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
You have made many correct observations already! Unless you’d like to seed both of the random generators, it’s probably simpler in the long run to choose one generator or the other. But if you do need to use both, then yes, you’ll also need to seed them both, because they generate random numbers independently of … Read more
Selecting a random permutation requires simultaneously more and less randomness than what your question implies. Let me explain. The bad news: need more randomness. The fundamental flaw in your approach is that it’s trying to choose between ~2226 possibilities using 64 bits of entropy (the random seed). To fairly choose between ~2226 possibilities you’re going … Read more