Each time you set the same seed, you get the same sequence. So of course if you’re setting the seed to the time in a fast loop, you’ll probably call it with the same seed many times. In your case, as you’re calling your randInt function until you have a different value, you’re waiting for … Read more
To support the answer by Alexander Vogt, I’ll generalize. The intrinsic random_number(u) returns a real number u (or an array of such) from the uniform distribution over the interval [0,1). [That is, it includes 0 but not 1.] To have a discrete uniform distribution on the integers {n, n+1, …, m-1, m} carve the continuous … Read more
This should be considerably faster than both answers above and supports seeding: curl -XGET ‘localhost:9200/_search’ -d ‘{ “query”: { “function_score” : { “query” : { “match_all”: {} }, “random_score” : {} } } }’; See: https://github.com/elasticsearch/elasticsearch/issues/1170
As you know, “truly random” means each of the bits is independent of everything else as well as uniformly distributed. However, this ideal is hard, if not impossible, to achieve in practice. In general, the closest way to get “truly random data” in practice is to gather hard-to-guess bits from nondeterministic sources, then condense those … Read more
You need to run math.randomseed() once before using math.random(), like this: math.randomseed(os.time()) One possible problem is that the first number may not be so “randomized” in some platforms. So a better solution is to pop some random number before using them for real: math.randomseed(os.time()) math.random(); math.random(); math.random() Reference: Lua Math Library
A random number generator has a state — that’s actually a necessary feature. The next “random” number is a function of the previous number and the seed/state. The purists call them pseudo-random number generators. The numbers will pass statistical tests for randomness, but aren’t — actually — random. The sequence of random values is finite … Read more
My suggestion would be an extension function on IntRange to create randoms like this: (0..10).random() TL;DR Kotlin >= 1.3, one Random for all platforms As of 1.3, Kotlin comes with its own multi-platform Random generator. It is described in this KEEP. The extension described below is now part of the Kotlin standard library, simply use … Read more
You need to run math.randomseed() once before using math.random(), like this: math.randomseed(os.time()) From your comment that you saw the first number is still the same. This is caused by the implementation of random generator in some platforms. The solution is to pop some random numbers before using them for real: math.randomseed(os.time()) math.random(); math.random(); math.random() Note … Read more
The random number generator produces pseudo-random numbers. To get different numbers each run, you need to initialise the random seed at the start of your program. This picks a different starting position in the pseudo-random stream.
There is also this algorithm: Oh, and more seriously: Random number generators use mathematical formulas that transfer set of numbers to another one. If, for example, you take a constant number N and another number n_0, and then take the value of n mod N (the modulo operator), you will get a new number n_1, … Read more