mersenne-twister

Q: How do I generate (many) uniformly distributed integers from a-priory unknown ranges? What is the prefered way in terms of performance (milions of generated numbers)? Context: In my app I have to generate many pseudo random numbers in many places. I use singleton pattern for the generator to maintain reproducibility of the app's run. Distribution is always uniform in my case, but the problem is that there are far too many possible ranges to pre-made the distribution object in C++11 style. What I tried: There are two obvious solutions to this, first is to have one-time distribution objects

This is actually not as simple as I first thought. In the absence of a hardware RNG, what is the best way to seed a Mersenne Twister? Or should I say, what is an acceptable way to seed a a Mersenne Twister RNG that is used to generate UUID's? There is a nice discussion of pseudo-random number generators here including a section on the proper seeding of PRNGs (see rule 3), which uses md5sum and /dev/random or /dev/urandom to generate seeds. This also includes a number of PRNG alogrithms which are a lot easier to code up (< 10 lines of code) than the MT but are arguably just as good (long

The following imports NumPy and sets the seed. import numpy as np np.random.seed(42) However, I'm not interested in setting the seed but more in reading it. random.get_state() does not seem to contain the seed. The documentation doesn't show an obvious answer. How do I retrieve the current seed used by numpy.random , assuming I did not set it manually? I want to use the current seed to carry over for the next iteration of a process. The short answer is that you simply can't (at least not in general). The Mersenne Twister RNG used by numpy has 2 19937 -1 possible internal states, whereas a

I'm working on a program that runs Monte Carlo simulation; specifically, I'm using a Metropolis algorithm. The program needs to generate possibly billions of "random" numbers. I know that the Mersenne twister is very popular for Monte Carlo simulation, but I would like to make sure that I am seeding the generator in the best way possible. Currently I'm computing a 32-bit seed using the following method: mt19937_64 prng; //pseudo random number generator unsigned long seed; //store seed so that every run can follow the same sequence unsigned char seed_count; //to help keep seeds from repeating

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? It's likely implementation specific. The ECMAScript specification does not force any algorithm, so a Linux JavaScript implementation might very well use

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 various languages such as C ( random.py ) or pseudocode (Wikipedia) but I could not find one in Python. Mersenne Twister is an implementation that is used by standard python library. You can see it in random.py file in your python distribution. On my system (Ubuntu 9.10) it is in /usr/lib