Kohonen NPM | npm.io

kohonen

A basic implementation of a Kohonen map in JavaScript

`We are still on an early stage of dev. Do not use this package until v1.0.0 has been released.

Usage

Import lib

npm i kohonen --save

Then, in your JS script :

import Kohonen, {hexagonHelper} from 'kohonen';

API

Kohonen

The Kohonen class is the main class.

Constructor

param name	definition	type	mandatory	default
neurons	grid of neurons	Array	yes
data	dataset	Array of Array	yes
maxStep	step max to clamp	Number	no	10000
maxLearningCoef		Number	no	1
minLearningCoef		Number	no	.3
maxNeighborhood		Number	no	1
minNeighborhood		Number	no	.3

// instanciate your Kohonen map
const k = new Kohonen({data, neurons});

// you can use the grid helper to generate a grid with 10x10 hexagons
const k = new Kohonen({data, neurons: hexagonHelper.generateGrid(10,10)});

neurons parameter should be a flat array of { pos: [x,y] }. pos array being the coordinate on the grid.

data parameter is an array of the vectors you want to display. There is no need to standardize your data, that will be done internally by scaling each feature to the 0,1 range.

Basically the constructor do :

standardize the given data set
initialize random weights for neurons using PCA's largests eigenvectors

training method

param name	definition	type	mandatory	default
log	func called after each step of learning process	Function	no	()=>{}

k.training();

training method iterates on random vectors picked on normalized data. If a log function is provided as a parameter, it will receive instance neurons and step as params.