CODEWITHSUNDEEP
rrandomjuliamersenne-twister
Colin T Bowers
Colin T Bowers

Reputation: 18530

Generate identical random numbers in R and Julia

I'd like to generate identical random numbers in R and Julia. Both languages appear to use the Mersenne-Twister library by default, however in Julia 1.0.0:

julia> using Random
julia> Random.seed!(3)
julia> rand()
0.8116984049958615

Produces 0.811..., while in R:

set.seed(3)
runif(1)

produces 0.168.

Any ideas?

Related SO questions here and here.

My use case for those who are interested: Testing new Julia code that requires random number generation (e.g. statistical bootstrapping) by comparing output to that from equivalent libraries in R.

Upvotes: 18

Views: 1486

Answers (3)

rickhg12hs
rickhg12hs

Reputation: 11912

Pursuing the RCall suggestion made by @Khashaa, it's clear that you can set the seed and get the random numbers from R.

julia> using RCall

julia> RCall.reval("set.seed(3)")
RCall.NilSxp(16777344,Ptr{Void} @0x0a4b6330)

julia> a = zeros(Float64,20);

julia> unsafe_copy!(pointer(a), RCall.reval("runif(20)").pv, 20)
Ptr{Float64} @0x972f4860

julia> map(x -> @printf("%20.15f\n", x), a);
   0.168041526339948
   0.807516399072483
   0.384942351374775
   0.327734317164868
   0.602100674761459
   0.604394054040313
   0.124633444240317
   0.294600924244151
   0.577609919011593
   0.630979274399579
   0.512015897547826
   0.505023914156482
   0.534035353455693
   0.557249435689300
   0.867919487645850
   0.829708693316206
   0.111449153395370
   0.703688358888030
   0.897488264366984
   0.279732553754002

and from R:

> options(digits=15)
> set.seed(3)
> runif(20)
 [1] 0.168041526339948 0.807516399072483 0.384942351374775 0.327734317164868
 [5] 0.602100674761459 0.604394054040313 0.124633444240317 0.294600924244151
 [9] 0.577609919011593 0.630979274399579 0.512015897547826 0.505023914156482
[13] 0.534035353455693 0.557249435689300 0.867919487645850 0.829708693316206
[17] 0.111449153395370 0.703688358888030 0.897488264366984 0.279732553754002

** EDIT **

Per the suggestion by @ColinTBowers, here's a simpler/cleaner way to access R random numbers from Julia.

julia> using RCall

julia> reval("set.seed(3)");

julia> a = rcopy("runif(20)");

julia> map(x -> @printf("%20.15f\n", x), a);
   0.168041526339948
   0.807516399072483
   0.384942351374775
   0.327734317164868
   0.602100674761459
   0.604394054040313
   0.124633444240317
   0.294600924244151
   0.577609919011593
   0.630979274399579
   0.512015897547826
   0.505023914156482
   0.534035353455693
   0.557249435689300
   0.867919487645850
   0.829708693316206
   0.111449153395370
   0.703688358888030
   0.897488264366984
   0.279732553754002

Upvotes: 5

Dirk is no longer here
Dirk is no longer here

Reputation: 368191

That is an old problem.

Paul Gilbert addressed the same issue in the late 1990s (!!) when trying to assert that simulations in R (then then newcomer) gave the same result as those in S-Plus (then the incumbent).

His solution, and still the golden approach AFAICT: re-implement in fresh code in both languages as the this the only way to ensure identical seeding, state, ... and whatever else affects it.

Upvotes: 8

IRTFM
IRTFM

Reputation: 263301

See:

?set.seed

"Mersenne-Twister": From Matsumoto and Nishimura (1998). A twisted GFSR with period 2^19937 - 1 and equidistribution in 623 consecutive dimensions (over the whole period). The ‘seed’ is a 624-dimensional set of 32-bit integers plus a current position in that set.

And you might see if you can link to the same C code from both languages. If you want to see the list/vector, type:

.Random.seed

Upvotes: 2

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