lib-random

lib-random is a seeded pseudorandom number generator for many distributions.

This library uses a generator from the PCG Family.

Usage

ES6

import LibRandom from 'lib-random';

var r = new LibRandom(1);
console.log(r.rand());

...

ES5 in RunKit + npm

var LibRandom = require("lib-random").default

var r = new LibRandom(1)
console.log(r.rand())    

...

API

new LibRandom(seed = Date().now)

Utilities

Given an array, one entry will be returned uniformly at random (Output is an element of the array)

LibRandom.choose(arr)

Given an arrany, k entries will be returned uniformly at random with or without replacement (Output is an array)

LibRandom.chooseMany(arr, k, replacement = false)

The elements in an array will be shuffled (Output is an array)

LibRandom.shuffle(arr)

A random colorCode will be returned in the form, '#bada55' (Output is a string)

LibRandom.colorCode()

Uniform Distribution

Random floating point numbers on the interval [a, b) (Output is a number)

LibRandom.rand(a = 0, b = 1)

n Random floating point numbers on the interval [a, b) (Output is an array)

LibRandom.manyRand(n, a = 0, b = 1)

Random integers on the interval [a, b] (Output is a number)

LibRandom.randInt(a = 0, b = 2 ** 32 - 1)

n Random integers on the interval [a, b] (Output is an array)

LibRandom.manyRandInt(n, a = 0, b = 2 ** 32 - 1)

Normal Distribution

Currently, normally distributed random numbers are generated using the Box-Muller to generate random numbers.

The generating algorithm will be switched to the ziggurat algorithm once 64-bit arithmetic becomes faster in javascript.

Random floating point numbers (Output is a number)

LibRandom.randNormal(mean = 0, variance = 1)

n Random floating point numbers (Output is an array)

LibRandom.manyRandNormal(n, mean = 0, variance = 1)

Random integers (Output is a number)

LibRandom.randIntNormal(mean = 0, variance = 1)

n Random integers (Output is an array)

LibRandom.manyRandIntNormal(n, mean = 0, variance = 1)

Exponential Distribution

Random floating point numbers (Output is a number)

LibRandom.randExponential(lambda = 1)

n Random floating point numbers (Output is an array)

LibRandom.manyRandExponential(n, lambda = 1)

To Be Added

• Major probability distributions