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)
- 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 via ow
- Integrates with seedrandom
- Supports node >= 6 and browser
Install
npm install --save random
Usage
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()
API
Table of Contents
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, defaultMath.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:
const random = require('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, default0
) -
max
number Upper bound (float, exclusive) (optional, default1
)
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, default0
) -
max
number Upper bound (integer, inclusive) (optional, default1
)
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, default0
) -
max
number Upper bound (integer, inclusive) (optional, default1
)
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
uniform
Generates a Continuous uniform distribution.
Type: function (min, max): function
-
min
number Lower bound (float, inclusive) (optional, default0
) -
max
number Upper bound (float, exclusive) (optional, default1
)
uniformInt
Generates a Discrete uniform distribution.
Type: function (min, max): function
-
min
number Lower bound (integer, inclusive) (optional, default0
) -
max
number Upper bound (integer, inclusive) (optional, default1
)
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
logNormal
Generates a Log-normal distribution.
Type: function (mu, sigma): function
-
mu
number Mean of underlying normal distribution (optional, default0
) -
sigma
number Standard deviation of underlying normal distribution (optional, default1
)
bernoulli
Generates a Bernoulli distribution.
Type: function (p): function
-
p
number Success probability of each trial. (optional, default0.5
)
binomial
Generates a Binomial distribution.
Type: function (n, p): function
-
n
number Number of trials. (optional, default1
) -
p
number Success probability of each trial. (optional, default0.5
)
geometric
Generates a Geometric distribution.
Type: function (p): function
-
p
number Success probability of each trial. (optional, default0.5
)
poisson
Generates a Poisson distribution.
Type: function (lambda): function
-
lambda
number Mean (lambda > 0) (optional, default1
)
exponential
Generates an Exponential distribution.
Type: function (lambda): function
-
lambda
number Inverse mean (lambda > 0) (optional, default1
)
irwinHall
Generates an Irwin Hall distribution.
Type: function (n): function
-
n
number Number of uniform samples to sum (n >= 0) (optional, default1
)
bates
Generates a Bates distribution.
Type: function (n): function
-
n
number Number of uniform samples to average (n >= 1) (optional, default1
)
pareto
Generates a Pareto distribution.
Type: function (alpha): function
-
alpha
number Alpha (optional, default1
)
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!
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
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.
License
MIT © Travis Fischer