TL;DR Don't forget to seed; don't maintain your own g.
The RNGs here are merely the deterministic part of a full random number generation suite. For proper operation, they need to be seeded with a high-quality entropy source.
Suitable entropy sources are provided by sub-libraries nocrypto.unix , nocrypto.lwt and nocrypto.xen . Although this module exposes a more fine-grained interface, allowing manual seeding of generators, this is intended either for implementing entropy-harvesting modules, or very specialized purposes. Users of this library should almost certainly use one of the above entropy libraries, and avoid manually managing the generator seeding.
Similarly, although it is possible to swap the default generator and gain control over the random stream, this is also intended for specialized applications such as testing or similar scenarios where the RNG needs to be fully deterministic, or as a component of deterministic algorithms which internally rely on pseudorandom streams.
In the general case, users should not maintain their local instances of g . All of the generators in a process have to compete for entropy, and it is likely that the overall result will have lower effective unpredictability.
The recommended way to use these functions is either to accept an optional generator and pass it down, or to ignore the generator altogether, as illustrated in the examples .
A generator (PRNG) with its state.
exception Unseeded_generator Thrown when using an uninitialized generator .
Ready-to-use RNG algorithms.
create module uses a module conforming to the Generator signature to instantiate the generic generator g .
g is the state to use, otherwise a fresh one is created.
seed can be provided to immediately reseed the generator with.
strict puts the generator into a more standards-conformant, but slighty slower mode. Useful if the outputs need to match published test-vectors.
Default generator. Functions in this module use this generator when not explicitly supplied one.
Swapping the generator is a way to subvert the random-generation process e.g. to make it fully deterministic.
generator defaults to Fortuna .
val block : g option-> intCreates a suite of generating functions over a numeric type.
val prime : ?g:g -> ?msb:int -> int -> Z.t prime ~g ~msb bits generates a prime smaller than 2^bits, with msb most significant bits set.
prime ~g ~msb:1 bits (the default) yields a prime in the interval [2^(bits - 1), 2^bits - 1].
val safe_prime : ?g:g -> int -> Z.t * Z.t safe_prime ~g bits gives a prime pair (g, p) such that p = 2g + 1 and p has bits significant bits.
Generating a random 13-byte Cstruct.t
let cs = Rng.generate 13Generating a list of Cstruct.tgenerator :
let rec f1 ?g ~n i =
if i < 1 then [] else Rng.generate ?g n :: f1 ?g ~n (i - 1)Generating a Z.t smaller than 10 and an int64 in the range [3, 7]:
let f2 ?g () = Rng.(Z.gen ?g ~$10, Int64.gen_r 3L 8L)Creating a local Fortuna instance and using it as a key-derivation function:
let f3 secret =
let g = Rng.(create ~seed:secret (module Generators.Fortuna)) in
Rng.generate ~g 32Generating a 17-bit prime with two leading bits set:
let p = Rng.prime ~msb:2 17Fisher-Yates shuffle:
let f4 ?g arr =
let n = Array.length arr in
arr |> Array.iter @@ fun i ->
let j = Rng.Int.gen_r ?g i n in
let (a, b) = (arr.(i), arr.(j)) in
arr.(i) <- b ; arr.(j) <- a Type definition to satisfy MirageOS RANDOM signature