@tmanderson/marc

A tiny Markov chain generator in JavaScript

Easily create Markov models from a given set of observations to generate random sequences of other potential observations. Larger data sets can be pre-computed using the new transition generator.
Marc supports both time-homogeneous and higher-order Markov chains.

Usage

import Marc from 'marc';
// Our observations consist of four sentences from a rando's Twitter account
const observations = ['a sentence', 'another sentence', 'one more', 'and the last'];
// Give Marc the observations and tell it our token delimiter (' ')
const m = new Marc(observations, { delimiter: ' ', order: 1 });
// Generate a probable observation
const random = m.random();

Run the example

$> npm run example

Pre Compute transitions (recommended for large data sets)

With global install

$> npm install -g @tmanderson/marc
$> marc observations.json transitions.json

From repo

$> ./bin/index.js observations.json transitions.json

The output transitions.json file can be used with Marc via setTransitions or when creating an instance.

fetch('transitions.json')
  .then(res => res.json())
  .then(observations => {
    let marc = new Marc(observations, { delimiter: ' ', order: 0 }) // via constructor
    marc.setTransitions(observations); // or via transitions
  });

A few examples, some funny, others serious (from NYTimes homepage, 01-19-2018):

• Cuomo Looks at The Bike That Could Cost $11.52
• Vows: For Love of Oat Milk Merkel?
• ICE Detained My Life
• How the Worst Way to Help Travelers Choose Safe Destinations
• Brand to Pretend They’re Homeless Pro-Life Movement Has Plenty
• Why James Franco Could Cost $11.52
• Timeline: How Congress Breaks Down the Collusion We Were Waiting For?
• Military Shifts Focus to Trump’s Radical Honesty
• Trump Administration on #MeToo Moment Shape the Bitcoin Bubble
• Sundance Film Festival: 5 Movies to Thwart Federal Government