Hi all,
I think what I�ve done is something different. Inside the Fortran subroutine, I
have a subroutine for setting the seed value of the RNG of GNU Fortran which I
think it is not related to the R RNG like the one below:
subroutine initrandomseedsinr(temp)
implicit none
integer :: n
integer, intent(in):: temp
integer, dimension(:), allocatable :: seed
call random_seed(size = n)
allocate(seed(n))
seed = temp
call random_seed(PUT = seed)
deallocate(seed)
end subroutine initrandomseedsinr
, where temp is an argument of the Fortran subroutine as well as in the wrapper
R function. This will be related to the RNG method used in the GNU Fortran that
build on GCC not to the R. I am not sure if I am right on this, but tried with
using RNGkind(sample.kind = "Rounding") and it doesn�t help. The difference in
the results were not major. The output at the end of running the functions kept
having very similar results, but still have the issue of reproducing exact
results which I need it for relating work that is based on the package.
Many thanks,
Waleed
Sent from Mail<https://go.microsoft.com/fwlink/?LinkId=550986> for Windows 10
From: Avraham Adler<mailto:avraham.ad...@gmail.com>
Sent: �Friday,� �January �17,� �2020 �06:30 ��
To: Ivan Krylov<mailto:krylov.r...@gmail.com>
Cc: ���� ��� ���� �������<mailto:wkmtie...@qu.edu.sa>; R Package
Development<mailto:r-package-devel@r-project.org>
Subject: Re: [R-pkg-devel] For reproducibility issue
Hi.
If it helps, I call the R RNG from Fortran in my Delaporte package
[1], also using iso_c_bindings. Specifically, I have the following C
code [2]:
void F77_SUB(unifrnd) (int *n, double *x){
GetRNGstate();
for (int i = 0; i < *n; ++i){
*(x + i) = unif_rand();
}
PutRNGstate();
}
and call it in Fortran like so [3]:
subroutine rdelap_f(n, a, na, b, nb, l, nl, vars) bind(C, name="rdelap_f_")
external unifrnd
integer(kind = c_int), intent(in), value :: n, na, nb, nl
real(kind = c_double), intent(out), dimension(n) :: vars
real(kind = c_double), intent(in) :: a(na), b(nb), l(nl)
real(kind = c_double), dimension(n) :: p
integer(kind = c_int) :: lg, lt
call unifrnd(n, p)
lt = 1_c_int
lg = 0_c_int
call qdelap_f(p, n, a, na, b, nb, l, nl, lt, lg, vars)
end subroutine rdelap_f
The package passes CRAN tests (at least as of now) on anything between
GCC 4 and GCC9 [4].
Hope that helps,
Avi
[1] https://bitbucket.org/aadler/delaporte/src/master/
[2] https://bitbucket.org/aadler/delaporte/src/master/src/utils_and_wrappers.c
[3] https://bitbucket.org/aadler/delaporte/src/master/src/delaporte.f95
[4] https://cran.r-project.org/web/checks/check_results_Delaporte.html
On Fri, Jan 17, 2020 at 2:39 PM Ivan Krylov <krylov.r...@gmail.com> wrote:
>
> On Fri, 17 Jan 2020 13:55:39 +0000
> ���� ��� ���� ������� <wkmtie...@qu.edu.sa> wrote:
>
> > So, does anyone have an idea of how to solve this issue.
>
> "Writing R Extensions", 1.6. Writing portable packages:
>
> >> Compiled code should not call the system random number generators
> >> such as rand, drand48 and random, but rather use the interfaces to
> >> R�s RNGs described in Random numbers. In particular, if more than
> >> one package initializes the system RNG (e.g. via srand), they will
> >> interfere with each other.
>
> >> Nor should the C++11 random number library be used, nor any other
> >> third-party random number generators such as those in GSL.
>
> It somewhat less convenient to call the R random number generator from
> Fortran than it would be from C or C++, but still possible. There is a
> F77-style example of such use [1], but since you are already using
> iso_c_binding, you should be able to declare the C API [2] right in the
> Fortran source:
>
> subroutine GetRNGState() bind(c)
> end subroutine
>
> subroutine PutRNGstate() bind(c)
> end subroutine
>
> As a bonus, you get to use the R distribution functions [3], without
> the need to implement them yourself from uniformly distributed samples:
>
> function rnorm(mu, sigma) bind(c) result(ret)
> use intrinsic, iso_c_binding, only: c_double
> real(c_double), value :: mu, sigma
> real(c_double) ret
> end function
>
> function rgamma(shape, scale) bind(c) result(ret)
> use intrinsic, iso_c_binding, only: c_double
> real(c_double), value :: shape, scale
> real(c_double) ret
> end function
>
> (The prototypes above are unchecked; I haven't written any Fortran 2003
> in more than a year.)
>
> --
> Best regards,
> Ivan
>
> [1]:
> https://cran.r-project.org/doc/manuals/R-exts.html#index-Random-numbers-in-Fortran
>
> [2]: https://cran.r-project.org/doc/manuals/R-exts.html#Random-numbers
>
> [3]:
> https://cran.r-project.org/doc/manuals/R-exts.html#Distribution-functions
>
> ______________________________________________
> R-package-devel@r-project.org mailing list
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