For information on using the package, see the stable documentation. Use the in-development documentation for the version of the documentation, which contains the unreleased features.
Pkg.add("PoissonRandom")
# Simple Poisson random
pois_rand(λ)
# Using another RNG
using RandomNumbers
rng = Xorshifts.Xoroshiro128Plus()
pois_rand(rng, λ)It mixes two methods. A simple count method and a method from a normal approximation. See this blog post for details.
using RandomNumbers,
Distributions, BenchmarkTools, StaticArrays,
RecursiveArrayTools, Plots, PoissonRandom
labels = ["count_rand", "ad_rand", "pois_rand", "Distributions.jl"]
rng = Xorshifts.Xoroshiro128Plus()
function n_count(rng, λ, n)
tmp = 0
for i in 1:n
tmp += PoissonRandom.count_rand(rng, λ)
end
end
function n_pois(rng, λ, n)
tmp = 0
for i in 1:n
tmp += pois_rand(rng, λ)
end
end
function n_ad(rng, λ, n)
tmp = 0
for i in 1:n
tmp += PoissonRandom.ad_rand(rng, λ)
end
end
function n_dist(λ, n)
tmp = 0
for i in 1:n
tmp += rand(Poisson(λ))
end
end
function time_λ(rng, λ, n)
t1 = @elapsed n_count(rng, λ, n)
t2 = @elapsed n_ad(rng, λ, n)
t3 = @elapsed n_pois(rng, λ, n)
t4 = @elapsed n_dist(λ, n)
@SArray [t1, t2, t3, t4]
end
# Compile
time_λ(rng, 5, 5000000)
# Run with a bunch of λ
times = VectorOfArray([time_λ(rng, n, 5000000) for n in 1:20])'
plot(times, labels = labels, lw = 3)
So this package ends up about 30% or so faster than Distributions.jl (the method at the far edge is λ-independent so that goes on forever).