tools/test_RNG_seed3.R
In dschlaep/ecotoner: ecotoner - ecotones / ecological boundaries across environmental gradients
rm(list=ls(all=TRUE))
f1 <- function(x) {
kind10 <- RNGkind()[1]
if (exists(".Random.seed")) {
rs10 <- .Random.seed
rm(.Random.seed)
} else rs10 <- NULL
set.seed(x)
kind11 <- RNGkind()[1]
sample(1)
rs11 <- .Random.seed
if (exists(".Random.seed")) rm(.Random.seed)
f2 <- function(x) {
kind0 <- RNGkind()[1]
if (exists(".Random.seed")) {
rs0 <- .Random.seed
rm(.Random.seed)
} else rs0 <- NULL
RNGkind(kind = "L'Ecuyer-CMRG")
set.seed(x)
kind1 <- RNGkind()[1]
sample(1)
rs1 <- .Random.seed
if (exists(".Random.seed")) rm(.Random.seed)
list(rseed0 = rs0, kind0 = kind0, rseed1 = rs1, kind1 = kind1)
}
f2res <- f2(x)
kind20 <- RNGkind()[1]
if (exists(".Random.seed")) {
rs20 <- .Random.seed
rm(.Random.seed)
} else rs20 <- NULL
RNGkind(kind = "Wichmann-Hill")
set.seed(x)
kind21 <- RNGkind()[1]
sample(1)
rs21 <- .Random.seed
if (exists(".Random.seed")) rm(.Random.seed)
list(f1a = list(rseed0 = rs10, kind0 = kind10, rseed1 = rs11, kind1 = kind11),
f2 = f2res,
f1b = list(rseed0 = rs20, kind0 = kind20, rseed1 = rs21, kind1 = kind21))
}
kinds <- list()
rs <- list()
RNGkind(kind = "Marsaglia-Multicarry")
set.seed(5)
kinds[[1]] <- RNGkind()[1]
sample(1)
rs[[1]] <- .Random.seed
res1 <- f1(5)
kinds[[2]] <- RNGkind()[1]
rs[[2]] <- .Random.seed
res2 <- f1(5)
kinds[[3]] <- RNGkind()[1]
rs[[3]] <- .Random.seed
RNGkind("default")
kinds[[4]] <- RNGkind()
rs[[4]] <- .Random.seed
RNGkind(kind = kinds[[3]][1])
kinds[[5]] <- RNGkind()[1]
rs[[5]] <- .Random.seed
.Random.seed <- rs[[1]]
kinds[[6]] <- RNGkind()[1]
rs[[6]] <- .Random.seed
temp <- sapply(kinds, print)
#[1] "Marsaglia-Multicarry"
#[1] "Wichmann-Hill"
#[1] "Wichmann-Hill"
#[1] "Mersenne-Twister" "Inversion"
#[1] "Wichmann-Hill"
#[1] "Marsaglia-Multicarry"
temp <- sapply(rs, function(x) str(x))
# int [1:3] 401 1686395561 149529984
# int [1:4] 400 16752 21308 2998
# int [1:4] 400 16752 21308 2998
# int [1:626] 403 624 1767525564 1072758413 1870004202 1063702627 -833821112 26087337 -2059985066 -1726908961 ...
# int [1:4] 400 13587 11201 18402
# int [1:3] 401 1686395561 149529984
unlist(res1)[grepl("kind", names(unlist(res1)))]
# f1a.kind0 f1a.kind1 f2.kind0
#"Marsaglia-Multicarry" "Marsaglia-Multicarry" "Marsaglia-Multicarry"
# f2.kind1 f1b.kind0 f1b.kind1
# "L'Ecuyer-CMRG" "L'Ecuyer-CMRG" "Wichmann-Hill"
unlist(res2)[grepl("kind", names(unlist(res2)))]
# f1a.kind0 f1a.kind1 f2.kind0 f2.kind1 f1b.kind0
#"Wichmann-Hill" "Wichmann-Hill" "Wichmann-Hill" "L'Ecuyer-CMRG" "L'Ecuyer-CMRG"
# f1b.kind1
#"Wichmann-Hill"
# ==> setting RNGkind(kind = "XXX") updates .Random.seed to match kind, but this update is not reversible, i.e., switching from kind = X1 to X2 and back to X1 does not get back to the same .Random.seed
# ==> assigning an appropriate value to .Random.seed updates RNGkind() accordingly
# ==> setting RNGkind in any function carries forward to child-, parent-, and the global environment
#########################################
#########################################
library(parallel)
setwd("~/Dropbox/Work_Stuff/2_Research/Software/GitHub_Projects/ecotoner/tools")
cl <- parallel::makeCluster(2, type = "FORK", outfile = "RNG_test3.txt")
#cl <- parallel::makeCluster(2, type = "SOCK", outfile = "RNG_test3.txt")
RNGkind(kind = "default")
kinds <- list()
rs <- list()
set.seed(5)
kinds[[1]] <- RNGkind()[1]
sample(1)
rs[[1]] <- .Random.seed
res3 <- parallel::parLapply(cl, 1:4, f1)
names(res3) <- paste0("run", 1:4)
parallel::stopCluster(cl)
kinds[[2]] <- RNGkind()[1]
sample(1)
rs[[2]] <- .Random.seed
temp <- sapply(kinds, print)
#[1] "Mersenne-Twister"
#[1] "Mersenne-Twister"
temp <- sapply(rs, function(x) str(x))
# int [1:626] 403 1 -1802397187 26112759 1167250462 -480616744 -1319230917 2098946009 -1500478104 630330518 ...
# int [1:626] 403 2 -1802397187 26112759 1167250462 -480616744 -1319230917 2098946009 -1500478104 630330518 ...
identical(rs[[1]][-2], rs[[2]][-2])
# TRUE
unlist(res3)[grepl("kind", names(unlist(res3)))]
# run1.f1a.kind0 run1.f1a.kind1 run1.f2.kind0
#"Marsaglia-Multicarry" "Marsaglia-Multicarry" "Marsaglia-Multicarry"
# run1.f2.kind1 run1.f1b.kind0 run1.f1b.kind1
# "L'Ecuyer-CMRG" "L'Ecuyer-CMRG" "Wichmann-Hill"
# run2.f1a.kind0 run2.f1a.kind1 run2.f2.kind0
# "Wichmann-Hill" "Wichmann-Hill" "Wichmann-Hill"
# run2.f2.kind1 run2.f1b.kind0 run2.f1b.kind1
# "L'Ecuyer-CMRG" "L'Ecuyer-CMRG" "Wichmann-Hill"
# run3.f1a.kind0 run3.f1a.kind1 run3.f2.kind0
#"Marsaglia-Multicarry" "Marsaglia-Multicarry" "Marsaglia-Multicarry"
# run3.f2.kind1 run3.f1b.kind0 run3.f1b.kind1
# "L'Ecuyer-CMRG" "L'Ecuyer-CMRG" "Wichmann-Hill"
# run4.f1a.kind0 run4.f1a.kind1 run4.f2.kind0
# "Wichmann-Hill" "Wichmann-Hill" "Wichmann-Hill"
# run4.f2.kind1 run4.f1b.kind0 run4.f1b.kind1
# "L'Ecuyer-CMRG" "L'Ecuyer-CMRG" "Wichmann-Hill"
# ==> RNGkind() on the cluster at execution time does not and is not affected by the global environment's RNGkind()
# ==> RNGkind() on each cluster remains to the next function call
# ==> the initial state of RNGkind() on the cluster
# - cluster type = "FORK: then it depends on what RNGkind() was in the environment when the cluster was created
# - cluster type = "SOCK": then it will be the default value, i.e., "Mersenne-Twister"
#########################################
#########################################
library(parallel)
setwd("~/Dropbox/Work_Stuff/2_Research/Software/GitHub_Projects/ecotoner/tools")
kinds <- list()
rs <- list()
f2 <- function(i, pseeds) {
set_RNG_stream(pseeds[[1 + (1 + i) %% 2]])
f1(i)
}
RNGkind(kind = "Marsaglia-Multicarry")
set.seed(5)
kinds[[1]] <- RNGkind()[1]
sample(1)
rs[[1]] <- .Random.seed
#cl <- parallel::makeCluster(2, type = "FORK", outfile = "RNG_test3.txt")
cl <- parallel::makeCluster(2, type = "SOCK", outfile = "RNG_test3.txt")
clusterExport(cl, varlist = c("f2", "f1", "set_RNG_stream")) # only necessary for SOCKET-clusters
RNGkind(kind = "default")
set.seed(5)
kinds[[2]] <- RNGkind()[1]
sample(1)
rs[[2]] <- .Random.seed
pseeds <- prepare_RNG_streams(N = 2, iseed = 5)
res5 <- parallel::parLapply(cl, 1:4, f2, pseeds)
names(res5) <- paste0("run", 1:4)
parallel::stopCluster(cl)
kinds[[3]] <- RNGkind()[1]
sample(1)
rs[[3]] <- .Random.seed
temp <- sapply(kinds, print)
#[1] "Marsaglia-Multicarry"
#[1] "Mersenne-Twister"
#[1] "Mersenne-Twister"
temp <- sapply(rs, function(x) str(x))
# int [1:3] 401 1686395561 149529984
# int [1:626] 403 1 -1802397187 26112759 1167250462 -480616744 -1319230917 2098946009 -1500478104 630330518 ...
# int [1:626] 403 2 -1802397187 26112759 1167250462 -480616744 -1319230917 2098946009 -1500478104 630330518 ...
identical(rs[[2]][-2], rs[[3]][-2])
# TRUE
unlist(res5)[grepl("kind", names(unlist(res5)))]
# run1.f1a.kind0 run1.f1a.kind1 run1.f2.kind0 run1.f2.kind1 run1.f1b.kind0
#"L'Ecuyer-CMRG" "L'Ecuyer-CMRG" "L'Ecuyer-CMRG" "L'Ecuyer-CMRG" "L'Ecuyer-CMRG"
# run1.f1b.kind1 run2.f1a.kind0 run2.f1a.kind1 run2.f2.kind0 run2.f2.kind1
#"Wichmann-Hill" "L'Ecuyer-CMRG" "L'Ecuyer-CMRG" "L'Ecuyer-CMRG" "L'Ecuyer-CMRG"
# run2.f1b.kind0 run2.f1b.kind1 run3.f1a.kind0 run3.f1a.kind1 run3.f2.kind0
#"L'Ecuyer-CMRG" "Wichmann-Hill" "L'Ecuyer-CMRG" "L'Ecuyer-CMRG" "L'Ecuyer-CMRG"
# run3.f2.kind1 run3.f1b.kind0 run3.f1b.kind1 run4.f1a.kind0 run4.f1a.kind1
#"L'Ecuyer-CMRG" "L'Ecuyer-CMRG" "Wichmann-Hill" "L'Ecuyer-CMRG" "L'Ecuyer-CMRG"
# run4.f2.kind0 run4.f2.kind1 run4.f1b.kind0 run4.f1b.kind1
#"L'Ecuyer-CMRG" "L'Ecuyer-CMRG" "L'Ecuyer-CMRG" "Wichmann-Hill"
sapply(res5, function(x) sapply(x, function(y) y$rseed0))
# run1 run2 run3 run4
# [1,] 407 407 407 407
# [2,] 1157214768 -1828978842 1157214768 -1828978842
# [3,] -1674567567 -44835162 -1674567567 -44835162
# [4,] -1532971138 -788600610 -1532971138 -788600610
# [5,] -1249749529 520285260 -1249749529 520285260
# [6,] 1302496508 327419947 1302496508 327419947
# [7,] -253670963 2055199760 -253670963 2055199760
# [8,] 407 407 407 407
# [9,] -169270483 -1619336578 1225564623 -224501472
#[10,] -442010614 -714750745 -987490876 -1260231007
#[11,] 471000765 2099518112 -397489685 1524376091
#[12,] -222347416 158863565 540074546 921285527
#[13,] 1489374793 -1093870294 617851915 -1965393172
#[14,] 1855986652 1639909500 -2043818655 2035071489
#[15,] 407 407 407 407
#[16,] -169270483 -1619336578 1225564623 -224501472
#[17,] -442010614 -714750745 -987490876 -1260231007
#[18,] 471000765 2099518112 -397489685 1524376091
#[19,] -222347416 158863565 540074546 921285527
#[20,] 1489374793 -1093870294 617851915 -1965393172
#[21,] 1855986652 1639909500 -2043818655 2035071489
# ==> function 'prepare_RNG_streams' in combination with 'set_RNG_stream' works as designed both with FORK- and SOCKET-clusters
#########################################
#########################################
RNGkind(kind = "Marsaglia-Multicarry")
str(.Random.seed)
set.seed(5, kind = "L'Ecuyer-CMRG")
RNGkind()
str(.Random.seed)
dschlaep/ecotoner documentation built on May 15, 2019, 2:57 p.m.
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rm(list=ls(all=TRUE))
f1 <- function(x) {
kind10 <- RNGkind()[1]
if (exists(".Random.seed")) {
rs10 <- .Random.seed
rm(.Random.seed)
} else rs10 <- NULL
set.seed(x)
kind11 <- RNGkind()[1]
sample(1)
rs11 <- .Random.seed
if (exists(".Random.seed")) rm(.Random.seed)
f2 <- function(x) {
kind0 <- RNGkind()[1]
if (exists(".Random.seed")) {
rs0 <- .Random.seed
rm(.Random.seed)
} else rs0 <- NULL
RNGkind(kind = "L'Ecuyer-CMRG")
set.seed(x)
kind1 <- RNGkind()[1]
sample(1)
rs1 <- .Random.seed
if (exists(".Random.seed")) rm(.Random.seed)
list(rseed0 = rs0, kind0 = kind0, rseed1 = rs1, kind1 = kind1)
}
f2res <- f2(x)
kind20 <- RNGkind()[1]
if (exists(".Random.seed")) {
rs20 <- .Random.seed
rm(.Random.seed)
} else rs20 <- NULL
RNGkind(kind = "Wichmann-Hill")
set.seed(x)
kind21 <- RNGkind()[1]
sample(1)
rs21 <- .Random.seed
if (exists(".Random.seed")) rm(.Random.seed)
list(f1a = list(rseed0 = rs10, kind0 = kind10, rseed1 = rs11, kind1 = kind11),
f2 = f2res,
f1b = list(rseed0 = rs20, kind0 = kind20, rseed1 = rs21, kind1 = kind21))
}
kinds <- list()
rs <- list()
RNGkind(kind = "Marsaglia-Multicarry")
set.seed(5)
kinds[[1]] <- RNGkind()[1]
sample(1)
rs[[1]] <- .Random.seed
res1 <- f1(5)
kinds[[2]] <- RNGkind()[1]
rs[[2]] <- .Random.seed
res2 <- f1(5)
kinds[[3]] <- RNGkind()[1]
rs[[3]] <- .Random.seed
RNGkind("default")
kinds[[4]] <- RNGkind()
rs[[4]] <- .Random.seed
RNGkind(kind = kinds[[3]][1])
kinds[[5]] <- RNGkind()[1]
rs[[5]] <- .Random.seed
.Random.seed <- rs[[1]]
kinds[[6]] <- RNGkind()[1]
rs[[6]] <- .Random.seed
temp <- sapply(kinds, print)
#[1] "Marsaglia-Multicarry"
#[1] "Wichmann-Hill"
#[1] "Wichmann-Hill"
#[1] "Mersenne-Twister" "Inversion"
#[1] "Wichmann-Hill"
#[1] "Marsaglia-Multicarry"
temp <- sapply(rs, function(x) str(x))
# int [1:3] 401 1686395561 149529984
# int [1:4] 400 16752 21308 2998
# int [1:4] 400 16752 21308 2998
# int [1:626] 403 624 1767525564 1072758413 1870004202 1063702627 -833821112 26087337 -2059985066 -1726908961 ...
# int [1:4] 400 13587 11201 18402
# int [1:3] 401 1686395561 149529984
unlist(res1)[grepl("kind", names(unlist(res1)))]
# f1a.kind0 f1a.kind1 f2.kind0
#"Marsaglia-Multicarry" "Marsaglia-Multicarry" "Marsaglia-Multicarry"
# f2.kind1 f1b.kind0 f1b.kind1
# "L'Ecuyer-CMRG" "L'Ecuyer-CMRG" "Wichmann-Hill"
unlist(res2)[grepl("kind", names(unlist(res2)))]
# f1a.kind0 f1a.kind1 f2.kind0 f2.kind1 f1b.kind0
#"Wichmann-Hill" "Wichmann-Hill" "Wichmann-Hill" "L'Ecuyer-CMRG" "L'Ecuyer-CMRG"
# f1b.kind1
#"Wichmann-Hill"
# ==> setting RNGkind(kind = "XXX") updates .Random.seed to match kind, but this update is not reversible, i.e., switching from kind = X1 to X2 and back to X1 does not get back to the same .Random.seed
# ==> assigning an appropriate value to .Random.seed updates RNGkind() accordingly
# ==> setting RNGkind in any function carries forward to child-, parent-, and the global environment
#########################################
#########################################
library(parallel)
setwd("~/Dropbox/Work_Stuff/2_Research/Software/GitHub_Projects/ecotoner/tools")
cl <- parallel::makeCluster(2, type = "FORK", outfile = "RNG_test3.txt")
#cl <- parallel::makeCluster(2, type = "SOCK", outfile = "RNG_test3.txt")
RNGkind(kind = "default")
kinds <- list()
rs <- list()
set.seed(5)
kinds[[1]] <- RNGkind()[1]
sample(1)
rs[[1]] <- .Random.seed
res3 <- parallel::parLapply(cl, 1:4, f1)
names(res3) <- paste0("run", 1:4)
parallel::stopCluster(cl)
kinds[[2]] <- RNGkind()[1]
sample(1)
rs[[2]] <- .Random.seed
temp <- sapply(kinds, print)
#[1] "Mersenne-Twister"
#[1] "Mersenne-Twister"
temp <- sapply(rs, function(x) str(x))
# int [1:626] 403 1 -1802397187 26112759 1167250462 -480616744 -1319230917 2098946009 -1500478104 630330518 ...
# int [1:626] 403 2 -1802397187 26112759 1167250462 -480616744 -1319230917 2098946009 -1500478104 630330518 ...
identical(rs[[1]][-2], rs[[2]][-2])
# TRUE
unlist(res3)[grepl("kind", names(unlist(res3)))]
# run1.f1a.kind0 run1.f1a.kind1 run1.f2.kind0
#"Marsaglia-Multicarry" "Marsaglia-Multicarry" "Marsaglia-Multicarry"
# run1.f2.kind1 run1.f1b.kind0 run1.f1b.kind1
# "L'Ecuyer-CMRG" "L'Ecuyer-CMRG" "Wichmann-Hill"
# run2.f1a.kind0 run2.f1a.kind1 run2.f2.kind0
# "Wichmann-Hill" "Wichmann-Hill" "Wichmann-Hill"
# run2.f2.kind1 run2.f1b.kind0 run2.f1b.kind1
# "L'Ecuyer-CMRG" "L'Ecuyer-CMRG" "Wichmann-Hill"
# run3.f1a.kind0 run3.f1a.kind1 run3.f2.kind0
#"Marsaglia-Multicarry" "Marsaglia-Multicarry" "Marsaglia-Multicarry"
# run3.f2.kind1 run3.f1b.kind0 run3.f1b.kind1
# "L'Ecuyer-CMRG" "L'Ecuyer-CMRG" "Wichmann-Hill"
# run4.f1a.kind0 run4.f1a.kind1 run4.f2.kind0
# "Wichmann-Hill" "Wichmann-Hill" "Wichmann-Hill"
# run4.f2.kind1 run4.f1b.kind0 run4.f1b.kind1
# "L'Ecuyer-CMRG" "L'Ecuyer-CMRG" "Wichmann-Hill"
# ==> RNGkind() on the cluster at execution time does not and is not affected by the global environment's RNGkind()
# ==> RNGkind() on each cluster remains to the next function call
# ==> the initial state of RNGkind() on the cluster
# - cluster type = "FORK: then it depends on what RNGkind() was in the environment when the cluster was created
# - cluster type = "SOCK": then it will be the default value, i.e., "Mersenne-Twister"
#########################################
#########################################
library(parallel)
setwd("~/Dropbox/Work_Stuff/2_Research/Software/GitHub_Projects/ecotoner/tools")
kinds <- list()
rs <- list()
f2 <- function(i, pseeds) {
set_RNG_stream(pseeds[[1 + (1 + i) %% 2]])
f1(i)
}
RNGkind(kind = "Marsaglia-Multicarry")
set.seed(5)
kinds[[1]] <- RNGkind()[1]
sample(1)
rs[[1]] <- .Random.seed
#cl <- parallel::makeCluster(2, type = "FORK", outfile = "RNG_test3.txt")
cl <- parallel::makeCluster(2, type = "SOCK", outfile = "RNG_test3.txt")
clusterExport(cl, varlist = c("f2", "f1", "set_RNG_stream")) # only necessary for SOCKET-clusters
RNGkind(kind = "default")
set.seed(5)
kinds[[2]] <- RNGkind()[1]
sample(1)
rs[[2]] <- .Random.seed
pseeds <- prepare_RNG_streams(N = 2, iseed = 5)
res5 <- parallel::parLapply(cl, 1:4, f2, pseeds)
names(res5) <- paste0("run", 1:4)
parallel::stopCluster(cl)
kinds[[3]] <- RNGkind()[1]
sample(1)
rs[[3]] <- .Random.seed
temp <- sapply(kinds, print)
#[1] "Marsaglia-Multicarry"
#[1] "Mersenne-Twister"
#[1] "Mersenne-Twister"
temp <- sapply(rs, function(x) str(x))
# int [1:3] 401 1686395561 149529984
# int [1:626] 403 1 -1802397187 26112759 1167250462 -480616744 -1319230917 2098946009 -1500478104 630330518 ...
# int [1:626] 403 2 -1802397187 26112759 1167250462 -480616744 -1319230917 2098946009 -1500478104 630330518 ...
identical(rs[[2]][-2], rs[[3]][-2])
# TRUE
unlist(res5)[grepl("kind", names(unlist(res5)))]
# run1.f1a.kind0 run1.f1a.kind1 run1.f2.kind0 run1.f2.kind1 run1.f1b.kind0
#"L'Ecuyer-CMRG" "L'Ecuyer-CMRG" "L'Ecuyer-CMRG" "L'Ecuyer-CMRG" "L'Ecuyer-CMRG"
# run1.f1b.kind1 run2.f1a.kind0 run2.f1a.kind1 run2.f2.kind0 run2.f2.kind1
#"Wichmann-Hill" "L'Ecuyer-CMRG" "L'Ecuyer-CMRG" "L'Ecuyer-CMRG" "L'Ecuyer-CMRG"
# run2.f1b.kind0 run2.f1b.kind1 run3.f1a.kind0 run3.f1a.kind1 run3.f2.kind0
#"L'Ecuyer-CMRG" "Wichmann-Hill" "L'Ecuyer-CMRG" "L'Ecuyer-CMRG" "L'Ecuyer-CMRG"
# run3.f2.kind1 run3.f1b.kind0 run3.f1b.kind1 run4.f1a.kind0 run4.f1a.kind1
#"L'Ecuyer-CMRG" "L'Ecuyer-CMRG" "Wichmann-Hill" "L'Ecuyer-CMRG" "L'Ecuyer-CMRG"
# run4.f2.kind0 run4.f2.kind1 run4.f1b.kind0 run4.f1b.kind1
#"L'Ecuyer-CMRG" "L'Ecuyer-CMRG" "L'Ecuyer-CMRG" "Wichmann-Hill"
sapply(res5, function(x) sapply(x, function(y) y$rseed0))
# run1 run2 run3 run4
# [1,] 407 407 407 407
# [2,] 1157214768 -1828978842 1157214768 -1828978842
# [3,] -1674567567 -44835162 -1674567567 -44835162
# [4,] -1532971138 -788600610 -1532971138 -788600610
# [5,] -1249749529 520285260 -1249749529 520285260
# [6,] 1302496508 327419947 1302496508 327419947
# [7,] -253670963 2055199760 -253670963 2055199760
# [8,] 407 407 407 407
# [9,] -169270483 -1619336578 1225564623 -224501472
#[10,] -442010614 -714750745 -987490876 -1260231007
#[11,] 471000765 2099518112 -397489685 1524376091
#[12,] -222347416 158863565 540074546 921285527
#[13,] 1489374793 -1093870294 617851915 -1965393172
#[14,] 1855986652 1639909500 -2043818655 2035071489
#[15,] 407 407 407 407
#[16,] -169270483 -1619336578 1225564623 -224501472
#[17,] -442010614 -714750745 -987490876 -1260231007
#[18,] 471000765 2099518112 -397489685 1524376091
#[19,] -222347416 158863565 540074546 921285527
#[20,] 1489374793 -1093870294 617851915 -1965393172
#[21,] 1855986652 1639909500 -2043818655 2035071489
# ==> function 'prepare_RNG_streams' in combination with 'set_RNG_stream' works as designed both with FORK- and SOCKET-clusters
#########################################
#########################################
RNGkind(kind = "Marsaglia-Multicarry")
str(.Random.seed)
set.seed(5, kind = "L'Ecuyer-CMRG")
RNGkind()
str(.Random.seed)
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