Hi all,

I could need some help to understand how I can implement (clean) new RNGs. I have the following in mind: Given I have a (large) set of random numbers (e.g. real random numbers from some experiment) or a sequence of random numbers that I want to be able to manipulate [yes, I really want to do this and I know what I am doing ;)], how could I construct an RNG object which simply goes through the array and returns the next random number, which I can pass to my julia code that is already running with MersenneTwister?

I have the following â€śworkingâ€ť solution derived from Random.jl, but it is not complete and possibly not the cleanest solution:

```
using Random
import Random: rand, SamplerTrivial, CloseOpen12_64, CloseOpen01_64, BitInteger, UInt52Raw, CloseOpen12, SamplerUnion, SamplerType
mutable struct CustomRandomNumbers <: AbstractRNG
random_numbers::Vector{Float64}
index_current::Int
function CustomRandomNumbers(random_numbers::Vector{Float64})
new(random_numbers, 0)
end
end
function gen_rand01(r::CustomRandomNumbers)
r.index_current += 1
if checkbounds(Bool, r.random_numbers, r.index_current)
return @inbounds r.random_numbers[r.index_current]
else
throw(DomainError(r.index_current, "CustomRandomNumbers object has no further random numbers left and is configured in mode `:static`"))
end
end
### helper functions
rand_inbounds(r::CustomRandomNumbers, ::CloseOpen12_64) = gen_rand01(r) + 1
rand_inbounds(r::CustomRandomNumbers, ::CloseOpen01_64=CloseOpen01()) =
rand_inbounds(r, CloseOpen12()) - 1.0
rand_inbounds(r::CustomRandomNumbers, ::UInt52Raw{T}) where {T<:BitInteger} =
reinterpret(UInt64, rand_inbounds(r, CloseOpen12())) % T
#### generation
function rand(r::CustomRandomNumbers, x::SamplerTrivial{UInt52Raw{UInt64}})
rand_inbounds(r, x[])
end
rand(r::CustomRandomNumbers, sp::SamplerTrivial{CloseOpen12_64}) = rand_inbounds(r, sp[])
function test_custom_rng()
seed = 1000
rng = CustomRandomNumbers(rand(MersenneTwister(seed),100))
rng_MT = MersenneTwister(seed)
println("uniform floats:")
for (r1, r2) in zip(rand(rng, 10), rand(rng_MT,10))
println(r1, " vs ", r2)
end
println("\nexponentially distributed floats:")
for (r1, r2) in zip(randexp(rng, 10), randexp(rng_MT,10))
println(r1, " vs ", r2)
end
println("\nrandom int from range:")
rand(rng, 1:10)
end
```

The first two tests work, the last sampling from a range does not work. Obviously I failed in porting all generating functions from MersenneTwister to my example, in part because I did not understand what they were really doing. Can somebody help me?

Or am I approaching the problem from the wrong angle and there is a simple and straight-forward alternative?

Thanks for any help.