The pseudo-random algorithm that we normally use has this
Seed input which is like the initial value that determines the random result, so if you re-compute that algorithm with the same
Seed, you get the same random values, if changed, you get another random values.
So to create random points, you first create a random object using the
Seed. Then you use this randomizer to get random values for each point coordinate. This gives you a very iregular distribution, different from what
Populate returns. So what it does is at each new random point, it evaluates if it is too close to the rest of the points already included, and if it is, it discards it and tries other random coordinates, looking for a uniform density.
It’s that basic from the outside, and there are no parameters to vary its distribution in GH1. However, you could use a similar algorithm to iteratively add a random point to the collection and choose its distribution, or you could do it in other ways such as creating extra points and discarding the ones you don’t want.