Random data

extern crate rand;
use rand::RngCore;
fn main() {
// get some random data:
let mut data = [0u8; 8];
rand::thread_rng().fill_bytes(&mut data);
println!("{:?}", data)
}

What is randomness?

What does random mean? Colloquially the word can mean simply unexpected or unknown, but we need to be a bit more precise than that. Wikipedia gives us a more specific definition:

Randomness is the lack of pattern or predictability in events.

We can take this further: lack of pattern implies there is no bias; in other words, all possible values are equally likely.

To understand what a random value is, we still need a context: what pool of numbers can our random value come from?

  • To give a simple example, consider dice: they have values 1, 2, 3, 4, 5 and 6, and an unbiased (fair) die will make each number equally likely, with probability ⅙th.
  • Now lets take a silly example: the natural numbers (1, 2, 3, etc.). These numbers have no limit. So if you were to ask for an unbiased random natural number, 1, 5, 1000, 1 million, 1 trillion — all would be equally likely. In fact, for any natural number k, the numbers 1, 2, ..., k are an infinitely small fraction of all the natural numbers, which means the chance of picking a unbiased number from this range is effectively 1/∞ = 0. Put another way: for any natural number, we expect an unbiased random value to be bigger. This is impossible, so there cannot be any such thing as an unbiased random natural number.
  • Another example: real numbers between 0 and 1. Real numbers include all the fractions, irrational numbers like π and √2, and all multiples of those... there are infinitely many possibilities, even in a small range like (0, 1), so simply saying "all possibilities are equally likely" is not enough. Instead we interpret lack of pattern in a different way: every interval of equal size is equally likely; for example we could subdivide the interval 0,1 into 0,½ and ½,1 and toss a coin to decide which interval our random sample comes from. Say we pick ½,1 we can then toss another coin to decide between ½,¾ and ¾,1, restricting our random value to an interval of size ¼. We can repeat this as many times as necessary to pick a random value between 0 and 1 with as much precision as we want — although we should realise that we are not choosing an exact value but rather just a small interval.

What we have defined (or failed to define) above are uniform random number distributions, or simply uniform distributions. There are also non-uniform distributions, as we shall see later. It's also worth noting here that a uniform distribution does not imply that its samples will be evenly spread (try rolling six dice: you probably won't get 1, 2, 3, 4, 5, 6).

To bring us back to computing, we can now define what a uniformly distributed random value (an unbiased random value) is in several contexts:

  • u32: a random number between 0 and u32::MAX where each value is equally likely
  • BigInt: since this type has no upper bound, we cannot produce an unbiased random value (it would be infinitely large, and use infinite amounts of memory)
  • f64: we treat this as an approximation of the real numbers, and, by convention, restrict to the range 0 to 1 (if not otherwise specified). We will come back to the conversions used later; for now note that these produce 52-53 bits of precision (depending on which conversion is used, output will be in steps of ε or ε/2, where 1+ε is the smallest representable value greater than 1).

Random data

As seen above, the term "random number" is meaningless without context. "Random data" typically means a sequence of random bytes, where for each byte, each of the 256 possible values are equally likely.

RngCore::fill_bytes produces exactly this: a sequence of random bytes.

If a sequence of unbiased random bytes of the correct length is instead interpreted as an integer — say a u32 or u64 — the result is an unbiased integer. Since this conversion is trivial, RngCore::next_u32 and RngCore::next_u64 are part of the same trait. (In fact the conversion is often the other way around — algorithmic generators usually work with integers internally, which are then converted to whichever form of random data is required.)