random

Seedable random number generator supporting many common distributions.

Welcome to the most random module on npm! 😜

Highlights

• Simple TS API with zero dependencies
• Seedable
• Plugin support for different pseudo random number generators
• Includes many common distributions
  • uniform, normal, poisson, bernoulli, etc
• Replacement for seedrandom which hasn't been updated in over 5 years
• Supports all modern JS/TS runtimes

Install

1npm install random

Usage

1import random from 'random'
2
3// quick uniform shortcuts
4random.float((min = 0), (max = 1)) // uniform float in [ min, max )
5random.int((min = 0), (max = 1)) // uniform integer in [ min, max ]
6random.boolean() // true or false
7
8// uniform distribution
9random.uniform((min = 0), (max = 1)) // () => [ min, max )
10random.uniformInt((min = 0), (max = 1)) // () => [ min, max ]
11random.uniformBoolean() // () => [ false, true ]
12
13// normal distribution
14random.normal((mu = 0), (sigma = 1))
15random.logNormal((mu = 0), (sigma = 1))
16
17// bernoulli distribution
18random.bernoulli((p = 0.5))
19random.binomial((n = 1), (p = 0.5))
20random.geometric((p = 0.5))
21
22// poisson distribution
23random.poisson((lambda = 1))
24random.exponential((lambda = 1))
25
26// misc distribution
27random.irwinHall(n)
28random.bates(n)
29random.pareto(alpha)

For convenience, several common uniform samplers are exposed directly:

1random.float() // 0.2149383367670885
2random.int(0, 100) // 72
3random.boolean() // true
4
5// random array item
6random.choice([1, true, 'foo']) // 'foo'
7
8// sample multiple items without replacement
9random.sample([1, true, 'foo'], 2) // [true, 'foo']
10
11const dist = random.sampler([1, true, 'foo'], 2)
12dist() // [true, 'foo']
13dist() // ['foo', 1]
14dist() // [1, true]
15
16// shuffle arrays
17random.shuffle([1, true, 'foo']) // ['foo', 1, true]
18
19const dist = random.shuffler([1, true, 'foo'])
20dist() // [true, 'foo', 1]
21dist() // ['foo', true, 1]
22dist() // [1, 'foo', true]

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.

1// create a normal distribution with default params (mu=0 and sigma=1)
2const normal = random.normal()
3normal() // 0.4855465422678824
4normal() // -0.06696771815439678
5normal() // 0.7350852689834705
6
7// create a poisson distribution with default params (lambda=1)
8const poisson = random.poisson()
9poisson() // 0
10poisson() // 4
11poisson() // 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:

1// change the underlying pseudo random number generator seed.
2// by default, Math.random is used as the underlying PRNG, but it is not seedable,
3// so if a seed is given, we use an ARC4 PRNG (the same one used by `seedrandom`).
4random.use('my-seed')
5
6// create a new independent random number generator with a different seed
7const rng = random.clone('my-new-seed')
8
9// create a third independent random number generator using a custom PRNG
10import seedrandom from 'seedrandom'
11const rng2 = random.clone(seedrandom('kitty-seed'))

You can also instantiate a fresh instance of Random:

1import { Random } from 'random'
2
3const rng = new Random() // (uses Math.random)
4const rng2 = new Random('my-seed-string')
5const rng3 = new Random(() => {
6  /* custom PRNG */ return Math.random()
7})

API

Table of Contents

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

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:

1import random from 'random'
2
3random.use('example_seedrandom_string')
4// or
5random.use(seedrandom('kittens'))
6// or
7random.use(Math.random)

---

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 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 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)

---

weibull

Generates a Weibull distribution.

Type: function (lambda, k): function

• lambda number lambda, the scale parameter (lambda > 0)
• k number k, the shape parameter (k > 0)

---

Todo

• Distributions

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

• Generators

  • pluggable prng
  • port more prng from boost / seedrandom

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.
• jshash prngs

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!

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

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

License

MIT © Travis Fischer

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