iRNGStream: Iterators returning distant random-number seeds.

In iterors: Fast, Compact Iterators and Tools

Iterators returning distant random-number seeds.

Description

The iRNGStream creates a sequence of random number seeds that are very "far apart" (2^127 steps) in the overall random number sequence, so that each can be used to make a parallel, psudo-independent random iterator. This uses parallel::nextRNGStream and the "L'Ecuyer-CMRG" generator.

Usage

iRNGStream(seed)

Arguments

seed	Either a single number to be passed to set.seed or a

Details

iRNGSubStream creates seeds that are somewhat less far apart (2^76 steps), which might be used as "substream" seeds.

Originally from the itertools package.

Value

An iteror which produces seed values. vector to be passed to nextRNGStream or nextRNGSubStream.

An iteror which yields successive seed values.

References

For more details on the L'Ecuyer-CMRG generator, see vignette("parallel", package="parallel").

See Also

set.seed, nextRNGStream, nextRNGSubStream

Examples

global.seed <- .Random.seed

rng.seeds <- iRNGStream(313)
print(nextOr(rng.seeds))
print(nextOr(rng.seeds))

# create three pseudo-independent and
# reproducible random number streams
it1 <- isample(c(0, 1), 1, seed=nextOr(rng.seeds))
it2 <- isample(c(0, 1), 1, seed=nextOr(rng.seeds))
it3 <- isample(c(0, 1), 1, seed=nextOr(rng.seeds))

all(.Random.seed == global.seed)
take(it1, 5, "numeric") # 0 0 0 1 1
take(it2, 5, "numeric") # 0 1 1 1 1
take(it3, 5, "numeric") # 1 1 1 0 0

# none of this affects the global seed
all(global.seed == .Random.seed)


# Compute random numbers in three parallel processes with three
# well-separated seeds. Requires package "foreach"
library(foreach)
foreach(1:3, rseed=iRNGSubStream(1970), .combine='c') %dopar% {
  RNGkind("L'Ecuyer-CMRG") # would be better to initialize workers only once
  assign('.Random.seed', rseed, pos=.GlobalEnv)
  runif(1)
}

iterors documentation built on May 31, 2023, 5:36 p.m.