random-cjs

random

Seedable random number generator supporting many common distributions.

Welcome to the most random module on npm! 😜

Highlights

• Simple API (make easy things easy and hard things possible)
• TypeScript support
• Supports node >= 14 and browser
• Seedable based on entropy or user input
• Plugin support for different pseudo random number generators (PRNGs)
• Sample from many common distributions
  • uniform, normal, poisson, bernoulli, etc
• Validates all user input
• Integrates with seedrandom

Install

npm install --save random
# or
yarn add random
# or
pnpm add random

Note: this package is a CommonJS port of the random package found here. Why CommonJS? Many of the applications developed in NodeJS use a packager like pkg or nexe neither of which support ESM modules. Therefore, this port was created to support those package managers directly.

Usage

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:

import seedrandom from 'seedrandom'

// change the underlying pseudo random number generator
// by default, Math.random is used as the underlying PRNG
random.use(seedrandom('foobar'))

// create a new independent random number generator (uses seedrandom under the hood)
const rng = random.clone('my-new-seed')

// create a second independent random number generator and use a seeded PRNG
const rng2 = random.clone(seedrandom('kittyfoo'))

// replace Math.random with rng.uniform
rng.patch()

// restore original Math.random
rng.unpatch()

You can also instantiate a fresh instance of Random:

import { Random } from 'random'
import seedrandom from 'seedrandom'

const rng = new Random()
const rng2 = new Random(seedrandom('tinykittens'))

API

Table of Contents

• Random
  • rng
  • clone
  • use
  • patch
  • unpatch
  • next
  • float
  • int
  • integer
  • bool
  • boolean
  • choice
  • uniform
  • uniformInt
  • uniformBoolean
  • normal
  • logNormal
  • bernoulli
  • binomial
  • geometric
  • poisson
  • exponential
  • irwinHall
  • bates
  • pareto

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)

---

rng

Type: function ()

---

clone

• See: RNG.clone

Creates a new Random instance, optionally specifying parameters to set a new seed.

Type: function (args, seed, opts): Random

• args ...any
• seed string? Optional seed for new RNG.
• opts object? Optional config for new RNG options.

---

use

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 ...any

Example:

import random from 'random'

random.use('example_seedrandom_string')
// or
random.use(seedrandom('kittens'))
// or
random.use(Math.random)

---

patch

Patches Math.random with this Random instance's PRNG.

Type: function ()

---

unpatch

Restores a previously patched Math.random to its original value.

Type: function ()

---

next

Convenience wrapper around this.rng.next()

Returns a floating point number in [0, 1).

Type: function (): number

---

float

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)

---

int

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)

---

integer

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)

---

bool

Samples a uniform random boolean value.

Convence wrapper around random.uniformBoolean()

Type: function (): boolean

---

boolean

Samples a uniform random boolean value.

Convence wrapper around random.uniformBoolean()

Type: function (): boolean

---

choice

Returns an item chosen uniformly at trandom 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 from

---

uniform

Generates 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)

---

uniformInt

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)

---

uniformBoolean

Generates a Discrete uniform distribution, with two possible outcomes, true or `false.

This method is analogous to flipping a coin.

Type: function (): function

---

normal

Generates a Normal distribution.

Type: function (mu, sigma): function

• mu number Mean (optional, default 0)
• sigma number Standard deviation (optional, default 1)

---

logNormal

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)

---

bernoulli

Generates a Bernoulli distribution.

Type: function (p): function

• p number Success probability of each trial. (optional, default 0.5)

---

binomial

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)

---

geometric

Generates a Geometric distribution.

Type: function (p): function

• p number Success probability of each trial. (optional, default 0.5)

---

poisson

Generates a Poisson distribution.

Type: function (lambda): function

• lambda number Mean (lambda > 0) (optional, default 1)

---

exponential

Generates an Exponential distribution.

Type: function (lambda): function

• lambda number Inverse mean (lambda > 0) (optional, default 1)

---

irwinHall

Generates an Irwin Hall distribution.

Type: function (n): function

• n number Number of uniform samples to sum (n >= 0) (optional, default 1)

---

bates

Generates a Bates distribution.

Type: function (n): function

• n number Number of uniform samples to average (n >= 1) (optional, default 1)

---

pareto

Generates a Pareto distribution.

Type: function (alpha): function

• alpha number Alpha (optional, default 1)

---

Todo

• Distributions

  • [x] uniform
  • [x] uniformInt
  • [x] uniformBoolean
  • [x] normal
  • [x] logNormal
  • [ ] chiSquared
  • [ ] cauchy
  • [ ] fischerF
  • [ ] studentT
  • [x] bernoulli
  • [x] binomial
  • [ ] negativeBinomial
  • [x] geometric
  • [x] poisson
  • [x] exponential
  • [ ] gamma
  • [ ] hyperExponential
  • [ ] weibull
  • [ ] beta
  • [ ] laplace
  • [x] irwinHall
  • [x] bates
  • [x] pareto

• Generators

  • [x] pluggable prng
  • [ ] port more prng from boost
  • [ ] custom entropy

• Misc

  • [x] browser support via rollup
  • [x] basic docs
  • [x] basic tests
  • [x] test suite
  • [x] initial release!
  • [x] typescript support

Related

• d3-random - D3's excellent random number generation library.
• seedrandom - Seedable pseudo random number generator.
• random-int - For the common use case of generating uniform random ints.
• random-float - For the common use case of generating uniform random floats.
• randombytes - Random crypto bytes for Node.js and the browser.

Credit

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!

Shoutout to Falkor Clark for donating the random-cjs CommonJS port for this project!

Lots of inspiration from d3-random (@mbostock and @svanschooten).

Some distributions and PRNGs are ported from C++ boost::random.

License

MIT © Travis Fischer

Support my OSS work by following me on twitter