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
Function arc4random returns an UInt32. If you get a value higher than Int.max, the Int(…) cast will crash. Using Int(arc4random_uniform(UInt32(items.count))) should be a better solution. (Blame the strange crash messages in the Alpha version…)
numpy.random.Generator.choice offers a replace argument to sample without replacement: from numpy.random import default_rng rng = default_rng() numbers = rng.choice(20, size=10, replace=False) If you’re on a pre-1.17 NumPy, without the Generator API, you can use random.sample() from the standard library: print(random.sample(range(20), 10)) You can also use numpy.random.shuffle() and slicing, but this will be less efficient: a … Read more
Java doubles are in IEEE-754 format, therefore they have a 52-bit fraction; between any two adjacent powers of two (inclusive of one and exclusive of the next one), there will therefore be 2 to the 52th power different doubles (i.e., 4503599627370496 of them). For example, that’s the number of distinct doubles between 0.5 included and … Read more
Let me talk about random integer generating algorithms that are “optimal” in terms of the number of random bits it uses on average. In the rest of this post, we will assume we have a “true” random generator that can produce unbiased and independent random bits. In 1976, D. E. Knuth and A. C. Yao … Read more
For very simple pseudorandom-looking stuff, I use this oneliner that I found on the internet somewhere: float rand(vec2 co){ return fract(sin(dot(co, vec2(12.9898, 78.233))) * 43758.5453); } You can also generate a noise texture using whatever PRNG you like, then upload this in the normal fashion and sample the values in your shader; I can dig … Read more