This Clojure/ClojureScript library provides consistent sampling and random number generation regardless of the environment. That means using the same intial seed we can produce the exact same results (sampled items or random numbers) whether in the JVM or in the browser.
cljx-sampling
is available as a Maven artifact from
Clojars.
For Leiningen:
This library has two useful namespaces:
cljx-sampling.random
: Offers seedable random number generation.cljx.sampling.core
: In-memory sampling over collections (borrows from bigml/sampling).
As we review each, feel free to follow along in the REPL:
user> (ns demo
(:require [cljx-sampling.random :as random]
[cljx-sampling.core :refer [sample]]))
cljx-sampling.random
provides seedable random number generation
using a 32-bit Xorshift.
While this random number generator is surprisingly strong for its
simplicity, it is not cryptographically secure.
To use, simply create
a random number generator and then call
next-boolean!
, next-int!
, next-double!
, or next-gaussian!
as
needed.
demo> (def rng (random/create))
demo> (random/next-boolean! rng)
true
demo> (random/next-int! rng)
3321045053
demo> (random/next-int! rng 8)
4
demo> (random/next-double! rng)
0.5436311555095017
demo> (random/next-double! rng 10)
8.794576390646398
demo> (random/next-gaussian! rng)
0.09573863197719758
When we create
a generator, we can provide a seed to make the
numbers deterministic.
demo> (def rng1 (random/create "foobar"))
demo> (def rng2 (random/create "foobar"))
demo> (repeatedly 10 #(random/next-int! rng1 100))
(51 14 22 53 21 32 9 97 63 65)
demo> (repeatedly 10 #(random/next-int! rng2 100))
(51 14 22 53 21 32 9 97 63 65)
cljx-sampling.core
provides in-memory random sampling over
collections. While the original population is kept in memory, the
resulting sample is a lazy sequence.
By default, sampling is done without replacement. This is equivalent to a lazy Fisher-Yates shuffle.
demo> (sample (range 5))
(2 3 1 0 4)
Setting :replace
as true will sample with replacement. Since there
is no limit to the number of items that may be sampled with
replacement from a population, the result will be an infinite length
list. So make sure to take
however many samples are needed.
demo> (take 10 (sample (range 5) :replace true))
(2 3 3 2 4 1 1 1 3 0)
Each call to sample
will return a new sample order.
demo> (sample (range 5))
(0 2 3 1 4)
demo> (sample (range 5))
(3 1 4 2 0)
Setting the :seed
parameter allows the sample order to be
deterministic.
demo> (sample (range 5) :seed 7)
(1 4 2 0 3)
demo> (sample (range 5) :seed 7)
(1 4 2 0 3)
demo> (sample (range 5) :seed "foobar")
(2 1 0 4 3)
demo> (sample (range 5) :seed "foobar")
(2 1 0 4 3)
A sample may be weighted using the :weigh
parameter. If the
parameter is supplied with a function that takes an item and produces
a non-negative weight, then the resulting sample will be weighted
accordingly.
demo> (take 5 (sample [:heads :tails]
:weigh {:heads 0.5 :tails 0.5}
:replace true))
(:tails :heads :heads :heads :tails)
The weights need not sum to 1.
demo> (->> (sample [:heads :tails]
:weigh {:heads 2 :tails 1}
:replace true)
(take 100)
(frequencies))
{:heads 66, :tails 34}
Copyright (C) 2014-2017 - Adam Ashenfelter
Distributed under the Apache License, Version 2.0.