https://github.com/lovasoa/expectation-maximization

Multivariate gaussian fit with expectation–maximization (EM) algorithm in javascript.
https://github.com/lovasoa/expectation-maximization

Last synced: 10 months ago
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Multivariate gaussian fit with expectation–maximization (EM) algorithm in javascript.

• Host: GitHub
• URL: https://github.com/lovasoa/expectation-maximization
• Owner: lovasoa
• Created: 2016-12-17T21:50:30.000Z (about 9 years ago)
• Default Branch: master
• Last Pushed: 2016-12-20T13:52:13.000Z (about 9 years ago)
• Last Synced: 2024-10-30T07:00:19.295Z (about 1 year ago)
• Language: JavaScript
• Homepage: https://lovasoa.github.io/expectation-maximization/dist/
• Size: 1.02 MB
• Stars: 4
• Watchers: 5
• Forks: 3
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# expectation-maximization

[![Build Status](https://travis-ci.org/lovasoa/expectation-maximization.svg?branch=master)](https://travis-ci.org/lovasoa/expectation-maximization)

Expectation–maximization (EM) algorithm for n-dimensional vectors, implemented in javascript.

This allows to fit points with a multivariate gaussian mixture model.

This can be used for statistical classification of multivariate data, anomaly

detection, or predictive analytics.

## Goal

You have n-dimensional datapoints that belong to k groups.

With this algorithm, you can detect not only which point belongs to which group, but also what the features of each group (mean, covariance) are.

The algorithm works by repetitively estimating the probability of each point to belong to each group, and maximizing the parameters of the groups so that they best fit the data. [Detailed description of the algorithm on wikipedia](https://en.wikipedia.org/wiki/Expectation%E2%80%93maximization_algorithm)

![Visualization of the consecutive steps of the algorithm. Image from Wikipedia](https://upload.wikimedia.org/wikipedia/commons/6/69/EM_Clustering_of_Old_Faithful_data.gif)

## How to use

 ```js

var expectation_maximization = require('expectation-maximization');

// n-dimensional data points are stored as a simple array of array

var points = [

  [1,2], [3,4], [5,6]

];

var n_groups = 2; // We clusterize our data into two groups

var groups = expectation_maximization(points, n_groups);

////// result:

[

  {

    weight: .5, // weight (a-priori probabibility) of the group

    mu: [2, 3], // mean vector of the group

    sigma: [[1,0],[0,1]] // covariance matrix of the group

  },

  ... // other groups

]

 ```

## Documentation

Auto-generated *jsdoc* documentation

[is available here](https://github.com/lovasoa/expectation-maximization/tree/master/docs)

## Demo

A basic demo is availaible here:

https://lovasoa.github.io/expectation-maximization/dist/