Seedable random number generator supporting many common distributions.
Welcome to the most random module on npm! 😜
npm install --save random
const random = require('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
random.uniform(min = 0, max = 1) // () => [ min, max )
random.uniformInt(min = 0, max = 1) // () => [ min, max ]
random.uniformBoolean() // () => [ false, true ]
// normal
random.normal(mu = 0, sigma = 1)
random.logNormal(mu = 0, sigma = 1)
// bernoulli
random.bernoulli(p = 0.5)
random.binomial(n = 1, p = 0.5)
random.geometric(p = 0.5)
// poisson
random.poisson(lambda = 1)
random.exponential(lambda = 1)
// misc
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
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:
const seedrandom = require('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()
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:
const random = require('random')
random.use('example_seedrandom_string')
// or
random.use(seedrandom('kittens'))
// or
random.use(Math.random)
Patches Math.random with this Random instance's PRNG.
Type: function ()
Restores a previously patched Math.random to its original value.
Type: function ()
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
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)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
Misc
Huge 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
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.
Did you know?
Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.
Research
Security News
Socket’s threat research team has detected six malicious npm packages typosquatting popular libraries to insert SSH backdoors.
Security News
MITRE's 2024 CWE Top 25 highlights critical software vulnerabilities like XSS, SQL Injection, and CSRF, reflecting shifts due to a refined ranking methodology.
Security News
In this segment of the Risky Business podcast, Feross Aboukhadijeh and Patrick Gray discuss the challenges of tracking malware discovered in open source softare.