One simple trick is to select each dimension from a gaussian distribution, then normalize: from random import gauss def make_rand_vector(dims): vec = [gauss(0, 1) for i in range(dims)] mag = sum(x**2 for x in vec) ** .5 return [x/mag for x in vec] For example, if you want a 7-dimensional random vector, select 7 random … Read more
Let’s assume that rand() generates a uniformly-distributed value I in the range [0..RAND_MAX], and you want to generate a uniformly-distributed value O in the range [L,H]. Suppose I in is the range [0..32767] and O is in the range [0..2]. According to your suggested method, O= I%3. Note that in the given range, there are … Read more
You are correct, rand() % N is not precisely uniformly distributed. Precisely how much that matters depends on the range of numbers you want and the degree of randomness you want, but if you want enough randomness that you’d even care about it you don’t want to use rand() anyway. Get a real random number … Read more
The problem is that you’re doing a modulo operation. This would be no problem if RAND_MAX would be evenly divisible by your modulus, but usually that is not the case. As a very contrived example, assume RAND_MAX to be 11 and your modulus to be 3. You’ll get the following possible random numbers and the … Read more
The Fibonacci sphere algorithm is great for this. It is fast and gives results that at a glance will easily fool the human eye. You can see an example done with processing which will show the result over time as points are added. Here’s another great interactive example made by @gman. And here’s a simple … Read more