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Seeding a user supplied random number generator in R

I'm having some trouble with seeding a user defined RNG in R. It seems that

set.seed(123, kind='user', normal.kind='user')

Does not actually pass 123 to the user defined RNG initialization.

I went back to the documentation available at ?Random.user and tried the example code given there, with the minor modification that I print the seed passed to the user_unif_init function (full code below).

Steps to reproduce:

  1. Paste code below in urand.c
  2. Run R CMD SHLIB urand.c
  3. Open R
  4. Run the following commands:

    > dyn.load('urand.so')
    > set.seed(123, kind='user', normal.kind='user')
    Received seed: 720453763
    Received seed: 303482705 // any other numbers than 123
    

Here's the full code I used in urand.c:

// ##  Marsaglia's congruential PRNG

#include <stdio.h>
#include <R_ext/Random.h>

static Int32 seed;
static double res;
static int nseed = 1;

double * user_unif_rand()
{
    seed = 69069 * seed + 1;
    res = seed * 2.32830643653869e-10;
    return &res;
}

void  user_unif_init(Int32 seed_in) {
    printf("Received seed: %u\n", seed_in);
    seed = seed_in;
}
int * user_unif_nseed() { return &nseed; }
int * user_unif_seedloc() { return (int *) &seed; }

/*  ratio-of-uniforms for normal  */
#include <math.h>
static double x;

double * user_norm_rand()
{
    double u, v, z;
    do {
        u = unif_rand();
        v = 0.857764 * (2. * unif_rand() - 1);
        x = v/u; z = 0.25 * x * x;
        if (z < 1. - u) break;
        if (z > 0.259/u + 0.35) continue;
    } while (z > -log(u));
    return &x;
}

Any help would be greatly appreciated!

like image 635
GjjvdBurg Avatar asked Dec 15 '17 01:12

GjjvdBurg


2 Answers

It appears that R scrambles the user supplied seed in RNG.c as follows:

for(j = 0; j < 50; j++)
    seed = (69069 * seed + 1)

(link to source)

Trying to unscramble this would be a way to get the original seed back.

UPDATE

Unscrambling can be done through the multiplicative inverse of 69069 as follows:

Int32 unscramble(Int32 scrambled)
{
        int j;
        Int32 u = scrambled;
        for (j=0; j<50; j++) {
                u = ((u - 1) * 2783094533);
        }
        return u;
}

Plugging this in my user_unif_init() function solves the problem.

like image 54
GjjvdBurg Avatar answered Nov 15 '22 10:11

GjjvdBurg


The seed that is forwarded to the RNG is different from the provided seed, however, it is reproducible when a "normal" workflow is used. This then gives reproducible random numbers:

dyn.load('urand.so')
RNGkind("user", "user")
#> Received seed: 1844983443
set.seed(123)
#> Received seed: 303482705
runif(10)
#>  [1] 0.42061954 0.77097033 0.14981063 0.27065365 0.77665767 0.96882090
#>  [7] 0.49077135 0.08621131 0.52903479 0.90398294
set.seed(123)
#> Received seed: 303482705
runif(10)
#>  [1] 0.42061954 0.77097033 0.14981063 0.27065365 0.77665767 0.96882090
#>  [7] 0.49077135 0.08621131 0.52903479 0.90398294

(Note that I have changed your urand.c slightly to use Rprintf from R_ext/Print.h.)


Edit: If you need control over the seed (why?), than you can do it yourself: replace user_unif_init, user_unif_nseed and user_unif_seedloc with

void set_seed(int * seed_in) {
    Rprintf("Received seed: %u\n", *seed_in);
    seed = *seed_in;
}

And call it explicitly:

dyn.load('urand.so')
RNGkind("user", "user")
set_seed <- function(seed) {
  invisible(.C("set_seed", seed_in = as.integer(seed)))
}
set_seed(123)
#> Received seed: 123
runif(10)
#>  [1] 0.00197801 0.61916849 0.34846373 0.04152509 0.09669026 0.29923760
#>  [7] 0.04184693 0.32557942 0.44473242 0.22339845
set_seed(123)
#> Received seed: 123
runif(10)
#>  [1] 0.00197801 0.61916849 0.34846373 0.04152509 0.09669026 0.29923760
#>  [7] 0.04184693 0.32557942 0.44473242 0.22339845

Edit 2: Have alook into the source at https://svn.r-project.org/R/trunk/src/main/RNG.c:

static void RNG_Init(RNGtype kind, Int32 seed)
{
    int j;

    BM_norm_keep = 0.0; /* zap Box-Muller history */

    /* Initial scrambling */
    for(j = 0; j < 50; j++)
    seed = (69069 * seed + 1);
    [...]

These 50 LCG rounds are responsible for the difference. My guess is that the authors of R assume that typical user supplied seeds are small and are therefore not random enough for a seed.

like image 28
Ralf Stubner Avatar answered Nov 15 '22 11:11

Ralf Stubner