A Julia RNG with stable streams
Author JuliaRandom
21 Stars
Updated Last
1 Year Ago
Started In
April 2020


Build Status

This package intends to provide a simple RNG with stable streams, suitable for tests in packages which need reproducible streams of random numbers across Julia versions. Indeed, the Julia RNGs provided by default are documented to have non-stable streams (which for example enables some performance improvements).

The StableRNG type provided by this package strives for stability, but if bugs which require breaking this promise are found, a new major version will be released with the fix.

StableRNG is currently an alias for LehmerRNG, and implements a well understood linear congruential generator (LCG); an LCG is not state of the art, but is fast and is believed to have reasonably good statistical properties [1], suitable at least for tests of a wide range of packages. The choice of this particular RNG is based on its simplicity, which limits the chances for bugs. Note that only StableRNG is exported from the package, and should be the only type used in client code; LehmerRNG might be renamed, or might be made a distinct type from StableRNG in any upcoming minor (i.e. non-breaking) release.

Currently, this RNG requires explicit seeding (in the constructor or via Random.seed!), i.e. no random seed will be chosen for the user as is the case in e.g. MersenneTwister().

The stable (guaranteed) API is

  • construction: rng = StableRNG(seed::Integer) (in particular the alias LehmerRNG is currently not part of the API)
  • seeding: Random.seed!(rng::StableRNG, seed::Integer) (with 0 <= seed <= typemax(UInt64))
  • rand(rng, X) where X is any of the standard bit Integer types (Bool, Int8, Int16, Int32, Int64, Int128, UInt8, UInt16, UInt32, UInt64, UInt128)
  • rand(rng, X), randn(rng, X), randexp(rng, X) where X is a standard bit AbstractFloat types (Float16, Float32, Float64)
  • array versions for these types, including the mutating methods rand!, randn! and randexp!
  • rand(rng, ::AbstractArray) (e.g. rand(rng, 1:9)); the streams are the same on 32-bits and 64-bits architectures
  • shuffle(rng, ::AbstractArray) and shuffle!(rng, ::AbstractArray)

Note that the generated streams of numbers for scalars and arrays are the same, i.e. rand(rng, X, n) is equal to [rand(rng, X) for _=1:n] for a given rng state.

Please open an issue for missing needed APIs.

[1] LehmerRNG is implemented after the specific constants published by Melissa E. O'Neill in this C++ implementation, and passes the Big Crush test (thanks to Kristoffer Carlsson for running it). See also for example this blog post.


In your tests, simply initialize an RNG with a given seed, and use it instead of the default provided one, e.g.

rng = StableRNG(123)
A = randn(rng, 10, 10) # instead of randn(10, 10)
@test inv(inv(A))  A