acausal

acausal is a Typescript module that makes it easy to create, edit and generate pseudo random data from Weighted Random Distributions and Markov Chains.

Design Philosophy

Immutable: all classes are built on top of pure functions which do not mutate state, ensuring that models retain their integrity, and making them easy to use with Redux.
Portable: all classes are easily serializable and deserializable into data transfer objects, making them easy to store, transfer, and rebuild regardless of whether it's on the client or the server.
Easy to Use: all APIs are written to prioritize developer usability, making it easy to rapidly prototype and implement new models.
Minimal Dependencies: acausal only depends on random-js and scalr, (which formerly was part of acausal, but separated out for the 2.0.0 release).

Basic Examples:

import { MarkovChain, Distribution, Random } from 'acausal';

// Random Rarity Distribution
const dist = new Distribution({ seed: 1 });
dist.add('Green', 10);    // Common
dist.add('Blue', 5);      // Uncommon
dist.add('Purple', 1);    // Rare

dist.pick(10);

/* Results in:
[
  'Green',  'Green',  'Green',  'Blue',  'Green',
  'Blue',  'Purple', 'Green',  'Green',  'Green'
]
*/

// Markov Chain Name Generator
const mc = new MarkovChain({ seed: 1 });
mc.addSequence('alice'.split(''));
mc.addSequence('bob'.split(''));
mc.addSequence('erwin'.split(''));

console.log(mc.generate({ order: 1 }));

/* Results in:

[ 'a', 'l', 'i', 'n' ]

*/

// Random Numbers
const rand = new Random({ seed: 1 });

rand.integer(1, 6); // Roll 1d6

// Results in: 6

Installation

Run:

npm install -s acausal

Gocausal

acausal is also implemented in Golang. You can find the module here:

Gocausal

Random Distributions

A Random Distribution is a simple model which can simulate picks from a weighted distribution of items.

Distributions can be used to model random draws from a discrete collection of items, where each item has a different probability of appearing.

Example Use Cases:

Simulating drawing a hand from a standard deck of cards (see below).
Simulating the outcome of a game of roulette (or nearly any casino game).
Generating eye or hair color for a fictional person.
Generating the spectral class of fictional stars.
Modeling how many McDonalds meals you'd need to buy to win Monopoly.
Modeling any pseudo-random system through observation.

Distribution Quickstart Example - Card Deck:

import { Distribution } from 'acausal';

// Create a Deck of Cards
const suits = ['♣️', '♦️', '♥️', '♠️'];
const ranks = ['A', '2', '3', '4', '5', '6', '7', '8', '9', '10', 'J', 'Q', 'K'];

// Combine the Suits and Ranks
const cards = suits.reduce((last, suit) => {
  return [...last, ...ranks.map(rank => `${rank}${suit}`)];
}, []);

/* Should result in:
[
  'A♣️',  '2♣️',  '3♣️',  '4♣️', '5♣️', '6♣️', '7♣️',
  '8♣️',  '9♣️',  '10♣️', 'J♣️', 'Q♣️', 'K♣️', 'A♦️',
  '2♦️',  '3♦️',  '4♦️',  '5♦️', '6♦️', '7♦️', '8♦️',
  '9♦️',  '10♦️', 'J♦️',  'Q♦️', 'K♦️', 'A♥️', '2♥️',
  '3♥️',  '4♥️',  '5♥️',  '6♥️', '7♥️', '8♥️', '9♥️',
  '10♥️', 'J♥️',  'Q♥️',  'K♥️', 'A♠️', '2♠️', '3♠️',
  '4♠️',  '5♠️',  '6♠️',  '7♠️', '8♠️', '9♠️', '10♠️',
  'J♠️',  'Q♠️',  'K♠️'
]
*/

// Create weighted source data for the Distribution
const src = cards.reduce((last, card) => ({ ...last, [card]: 1 }), {});

/* Should result in:
{
  'A♣️': 1,
  '2♣️': 1,
  '3♣️': 1,
  ...
  'J♠️': 1,
  'Q♠️': 1,
  'K♠️': 1,
}
*/

// Create the Distribution from the deck.
const deck = new Distribution({
  seed: 23,       // Random Seed - if this is empty it will be generated.
  source: src,    // The weighted source to generate the normalized Distribution from.
});

// Add in 2 Jokers
deck.add('🃏', 2);

// Generate 4 picks from the deck without replacement.
const picks = deck.pick(4, undefined, true);
console.log(picks);

/* Should print:

[ 'J♣️', '10♠️', '3♦️', '9♣️' ]

*/

You can learn more about how to use Random Distributions with acausal in the Random Distribution Quickstart.

Markov Chains

A Markov Chain is a mathematical model of a system in which the future state of the system depends only on its present state.

Markov Chains are usually generated by building a statistical model off of sample data, such as a list of names, which can then be used to output sequences which resemble the sampled data. A useful property of this process is that sample data can be "mixed" together like paint to achieve a desired result.

For example, if you wanted to generate names which sounded like a mix of Irish and Japanese, you could generate a Markov Chain from a sample of Irish and Japanese names and the resulting model would be able to output names that mixed the two.

Markov Chain Quickstart Example - Name Generator:

import { MarkovChain } from 'acausal';

// Sample Data
const jpNames = ['honoka', 'akari', 'himari', 'mei', 'ema'];
const ieNames = ['grace', 'fiadh', 'emily', 'sophie', 'ava'];
const names = [...jpNames, ...ieNames];

// Prepare Data Source - the class expects an array of arrays.
const src = names.map(name => name.split(''));

/* Should result in:
[
  [ 'h', 'o', 'n', 'o', 'k', 'a' ],
  [ 'a', 'k', 'a', 'r', 'i' ],
  [ 'h', 'i', 'm', 'a', 'r', 'i' ],
  [ 'm', 'e', 'i' ],
  [ 'e', 'm', 'a' ],
  [ 'g', 'r', 'a', 'c', 'e' ],
  [ 'f', 'i', 'a', 'd', 'h' ],
  [ 'e', 'm', 'i', 'l', 'y' ],
  [ 's', 'o', 'p', 'h', 'i', 'e' ],
  [ 'a', 'v', 'a' ]
]
*/

// Create the Markov Chain from the source data.
const chain = new MarkovChain({
  seed: 33,       // Random Seed - if this is empty it will be generated.
  maxOrder: 2,    // Maximum Order - Chain will generate orders up to this value.
  sequences: src, // Source data, expects an array of arrays.
});

// Generate 5 picks.
for (let i = 0; i < 3; i += 1) {
  const pick = chain.generate({
    min: 4,       // Min Picks - This will force the model to pick at least 4 times.
    max: 10,      // Max Picks - Stops generation after 10 picks if no end has been reached.
    order: 2,     // Order - The largest gram size used to calculate the next pick.
    strict: false // Strict Order - Dynamically adjusts order up or down each pick if false.
  });
  console.log(pick.join(''));
}

/* Should print:

    sophimari
    emari
    hie

*/

You can learn more about how to use Markov Chains with acausal in the Markov Chain Quickstart

Extended API Documentation

For documentation of underlying classes and functions, please see the API documentation.