Seedable random number generator supporting many common distributions.
Welcome to the most random module on npm! 😜
seedrandom which hasn't been updated in over 5 yearsnpm install random
import random from 'random'
// quick uniform shortcuts
random.float((min = 0), (max = 1)) // uniform float in [ min, max )
random.int((min = 0), (max = 1)) // uniform integer in [ min, max ]
random.boolean() // true or false
// uniform distribution
random.uniform((min = 0), (max = 1)) // () => [ min, max )
random.uniformInt((min = 0), (max = 1)) // () => [ min, max ]
random.uniformBoolean() // () => [ false, true ]
// normal distribution
random.normal((mu = 0), (sigma = 1))
random.logNormal((mu = 0), (sigma = 1))
// bernoulli distribution
random.bernoulli((p = 0.5))
random.binomial((n = 1), (p = 0.5))
random.geometric((p = 0.5))
// poisson distribution
random.poisson((lambda = 1))
random.exponential((lambda = 1))
// misc distribution
random.irwinHall(n)
random.bates(n)
random.pareto(alpha)
For convenience, several common uniform samplers are exposed directly:
random.float() // 0.2149383367670885
random.int(0, 100) // 72
random.boolean() // true
// random array item
random.choice([1, true, 'foo']) // 'foo'
All distribution methods return a thunk (function with no params), which will return a series of independent, identically distributed random variables from the specified distribution.
// create a normal distribution with default params (mu=1 and sigma=0)
const normal = random.normal()
normal() // 0.4855465422678824
normal() // -0.06696771815439678
normal() // 0.7350852689834705
// create a poisson distribution with default params (lambda=1)
const poisson = random.poisson()
poisson() // 0
poisson() // 4
poisson() // 1
Note that returning a thunk here is more efficient when generating multiple samples from the same distribution.
You can change the underlying PRNG or its seed as follows:
// change the underlying pseudo random number generator seed.
// by default, Math.random is used as the underlying PRNG, but it is not seedable,
// so if a seed is given, we use an ARC4 PRNG (the same one used by `seedrandom`).
random.use('my-seed')
// create a new independent random number generator with a different seed
const rng = random.clone('my-new-seed')
// create a third independent random number generator using a custom PRNG
import seedrandom from 'seedrandom'
const rng2 = random.clone(seedrandom('kitty-seed'))
You can also instantiate a fresh instance of Random:
import { Random } from 'random'
const rng = new Random() // (uses Math.random)
const rng2 = new Random('my-seed-string')
const rng3 = new Random(() => {
/* custom PRNG */ return Math.random()
})
Seedable random number generator supporting many common distributions.
Defaults to Math.random as its underlying pseudorandom number generator.
Type: function (rng)
rng (RNG | function) Underlying pseudorandom number generator. (optional, default Math.random)Type: function ()
Creates a new Random instance, optionally specifying parameters to
set a new seed.
Type: function (args, seed, opts): Random
Sets the underlying pseudorandom number generator used via
either an instance of seedrandom, a custom instance of RNG
(for PRNG plugins), or a string specifying the PRNG to use
along with an optional seed and opts to initialize the
RNG.
Type: function (args)
args ...anyExample:
import random from 'random'
random.use('example_seedrandom_string')
// or
random.use(seedrandom('kittens'))
// or
random.use(Math.random)
Convenience wrapper around this.rng.next()
Returns a floating point number in [0, 1).
Type: function (): number
Samples a uniform random floating point number, optionally specifying lower and upper bounds.
Convence wrapper around random.uniform()
Type: function (min, max): number
min number Lower bound (float, inclusive) (optional, default 0)max number Upper bound (float, exclusive) (optional, default 1)Samples a uniform random integer, optionally specifying lower and upper bounds.
Convence wrapper around random.uniformInt()
Type: function (min, max): number
min number Lower bound (integer, inclusive) (optional, default 0)max number Upper bound (integer, inclusive) (optional, default 1)Samples a uniform random integer, optionally specifying lower and upper bounds.
Convence wrapper around random.uniformInt()
Type: function (min, max): number
min number Lower bound (integer, inclusive) (optional, default 0)max number Upper bound (integer, inclusive) (optional, default 1)Samples a uniform random boolean value.
Convence wrapper around random.uniformBoolean()
Type: function (): boolean
Samples a uniform random boolean value.
Convence wrapper around random.uniformBoolean()
Type: function (): boolean
Returns an item chosen uniformly at random from the given array.
Convence wrapper around random.uniformInt()
Type: function choice <T> (array: Array<T>): T | undefined
array Array Array of items to sample fromGenerates a Continuous uniform distribution.
Type: function (min, max): function
min number Lower bound (float, inclusive) (optional, default 0)max number Upper bound (float, exclusive) (optional, default 1)Generates a Discrete uniform distribution.
Type: function (min, max): function
min number Lower bound (integer, inclusive) (optional, default 0)max number Upper bound (integer, inclusive) (optional, default 1)Generates a Discrete uniform distribution,
with two possible outcomes, true or `false.
This method is analogous to flipping a coin.
Type: function (): function
Generates a Normal distribution.
Type: function (mu, sigma): function
Generates a Log-normal distribution.
Type: function (mu, sigma): function
mu number Mean of underlying normal distribution (optional, default 0)sigma number Standard deviation of underlying normal distribution (optional, default 1)Generates a Bernoulli distribution.
Type: function (p): function
p number Success probability of each trial. (optional, default 0.5)Generates a Binomial distribution.
Type: function (n, p): function
n number Number of trials. (optional, default 1)p number Success probability of each trial. (optional, default 0.5)Generates a Geometric distribution.
Type: function (p): function
p number Success probability of each trial. (optional, default 0.5)Generates a Poisson distribution.
Type: function (lambda): function
lambda number Mean (lambda > 0) (optional, default 1)Generates an Exponential distribution.
Type: function (lambda): function
lambda number Inverse mean (lambda > 0) (optional, default 1)Generates an Irwin Hall distribution.
Type: function (n): function
n number Number of uniform samples to sum (n >= 0) (optional, default 1)Generates a Bates distribution.
Type: function (n): function
n number Number of uniform samples to average (n >= 1) (optional, default 1)Generates a Pareto distribution.
Type: function (alpha): function
alpha number Alpha (optional, default 1)Distributions
Generators
Thanks go to Andrew Moss for the TypeScript port and for helping to maintain this package.
Shoutout to Roger Combs for donating the random npm package for this project!
Lots of inspiration from d3-random (@mbostock and @svanschooten).
Some distributions and PRNGs are ported from C++ boost::random.
MIT © Travis Fischer
Support my OSS work by following me on twitter 
FAQs
Seedable random number generator supporting many common distributions.
The npm package random receives a total of 30,688 weekly downloads. As such, random popularity was classified as popular.
We found that random demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 0 open source maintainers collaborating on the project.
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