mersenne-twister | 易学教程

What algorithm does Math.random use?

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问题 Since I've been studying Computer Science, whenever random numbers come up, it's always Mersenne Twister. There's never even a question, no alternative. Just, use Mersenne Twister. So what does JavaScript's Math.random use? It seems like it ought to use Mersenne Twister, since it's apparently without peer, but I can't find any reference to whether it does or not. Does anyone know what it relies on, and/or why it isn't MT, if that's the case? 回答1: It's likely implementation specific. The

Random numbers for multiple threads

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Problem I intend to write a C++11 application for Linux which does some numerical simulation (not cryptography) based on approximately one million pseudorandom 32bit numbers. To speed things up, I'd like to perform the simulation in parallel threads using all cores of a desktop CPU. I'd like to use the Mersenne Twister mt19937 provided by boost as the PRNG, and I guess that for performance reasons I should have one such PRNG per thread. Now I'm unsure about how to seed them in order to avoid generating the same subsequence of random numbers in multiple threads. Alternatives Here are the

Open-source implementation of Mersenne Twister in Python? [closed]

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问题 Closed. This question is off-topic. It is not currently accepting answers. Want to improve this question? Update the question so it's on-topic for Stack Overflow. Closed 4 years ago . Is there any good open-source implementation of Mersenne Twister and other good random number generators in Python available? I would like to use in for teaching math and comp sci majors? I am also looking for the corresponding theoretical support. Edit: Source code of Mersenne Twister is readily available in

Does std::mt19937 require warmup?

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I've read that many pseudo-random number generators require many samples in ordered to be "warmed up". Is that the case when using std::random_device to seed std::mt19937, or can we expect that it's ready after construction? The code in question: #include <random> std::random_device rd; std::mt19937 gen(rd()); bames53 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

Random numbers for multiple threads

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问题 Problem I intend to write a C++11 application for Linux which does some numerical simulation (not cryptography) based on approximately one million pseudorandom 32bit numbers. To speed things up, I'd like to perform the simulation in parallel threads using all cores of a desktop CPU. I'd like to use the Mersenne Twister mt19937 provided by boost as the PRNG, and I guess that for performance reasons I should have one such PRNG per thread. Now I'm unsure about how to seed them in order to avoid

How do I scale down numbers from rand()?

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The following code outputs a random number each second: int main () { srand(time(NULL)); // Seeds number generator with execution time. while (true) { int rawRand = rand(); std::cout << rawRand << std::endl; sleep(1); } } How might I size these numbers down so they're always in the range of 0-100? 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; All the