Expmax

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Description

Expmax is an expectation maximization (EM) library. It makes use of a gaussian mixture model.

This library allows data clustering of n-dimensional datasets given the amount of clusters wanted.

Overview

$3

bash
.
├── core
│   ├── expmax_core.ts
│   └── gaussian_mixture_core.ts
├── engines
│   └── expmax.ts
├── errors.ts
├── index.ts
├── types.ts
└── utils
    └── math.ts


$3

Please refer to the

NPM custom commands

 section
$3

First install the library

bash
npm install expmax


__Example:__

`typescript import {ExpMax} from 'expmax';

// Dataset should have data points with same vector space dimension const dataset: IDataset = { 'points': [ [1.1,1], [1,2], [2,2], [2,1], [15,15], [15,16], ], 'label':'test', };

const opts: IEmOptions = { 'clusterQt':2, // Quantity of clusters you want to fit 'maxEpochs':1000, // Maximum training cycles 'threshold': 2e-16 // Threshold (epsilon) used to define convergence }

const model = new ExpMax(dataset, opts); // Instanciate the model with random values const trainedModel = model.train() // Train it console.log(trainedModel); /* Output: [ { mu: [ 15.55, 16 ], sigma: [ [Array], [Array] ], vectorSpaceDim: 2, pi: 0.3333333333333333, gamma: [ 1.8570742387734104e-153, 3.512200996873401e-50, 3.128974029587457e-48, 7.275927146729349e-54, 1, 1 ] }, { mu: [ 1.525, 1.5 ], sigma: [ [Array], [Array] ], vectorSpaceDim: 2, pi: 0.6666666666666666, gamma: [ 1, 1, 1, 1, 5.362024468745314e-82, 1.955669841306763e-88 ] } ]*/`

`$3`

- .train(): Trains the model then return clusters -.update(newDataset): Updates dataset then trains the model and returns new clusters

`NPM custom commands`

- build: Build the JavaScript files. -build:watch: Build the JavaScript files in watch mode. -test: Run jest in test mode. -test:watch: Run jest in interactive test mode. -docs: Generate the docs directory. -lint`: Runs linter on the whole project.

Ressources

* https://towardsdatascience.com/gaussian-mixture-models-explained-6986aaf5a95
* https://perso.telecom-paristech.fr/bonald/documents/gmm.pdf

Credit

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

This lib helped me a great deal, thanks.

License

![License: MIT](https://opensource.org/licenses/MIT)

Bastien GUIHARD

Overview

bash
.
├── core
│   ├── expmax_core.ts
│   └── gaussian_mixture_core.ts
├── engines
│   └── expmax.ts
├── errors.ts
├── index.ts
├── types.ts
└── utils
└── math.ts

$3

Please refer to the

section

$3

First install the library

bash
npm install expmax

__Example:__

typescript
import {ExpMax} from 'expmax';

// Dataset should have data points with same vector space dimension
const dataset: IDataset = {
    'points': [
        [1.1,1],
        [1,2],
        [2,2],
        [2,1],
        [15,15],
        [15,16],
    ],
    'label':'test',
};

const opts: IEmOptions = {    
    'clusterQt':2, // Quantity of clusters you want to fit
    'maxEpochs':1000, // Maximum training cycles
    'threshold': 2e-16 // Threshold (epsilon) used to define convergence
}

const model = new ExpMax(dataset, opts); // Instanciate the model with random values
const trainedModel = model.train() // Train it
console.log(trainedModel);
/*
Output:
[
  {
    mu: [ 15.55, 16 ],
    sigma: [ [Array], [Array] ],
    vectorSpaceDim: 2,
    pi: 0.3333333333333333,
    gamma: [
      1.8570742387734104e-153,
      3.512200996873401e-50,
      3.128974029587457e-48,
      7.275927146729349e-54,
      1,
      1
    ]
  },
  {
    mu: [ 1.525, 1.5 ],
    sigma: [ [Array], [Array] ],
    vectorSpaceDim: 2,
    pi: 0.6666666666666666,
    gamma: [ 1, 1, 1, 1, 5.362024468745314e-82, 1.955669841306763e-88 ]
  }
]*/

$3

- .train()

: Trains the model then return clusters
-

.update(newDataset): Updates dataset then trains the model and returns new clusters