A basic implementation of a Kohonen map in JavaScript

Disclaimer: this is a toy implementation of the SOM algorithm, you should probably consider using a more solid library in R or Python.

npm i d3-array d3-scale d3-random lodash ml-pca @seracio/kohonen --save

Then, in your JS script :

import { Kohonen, generateGrid } from '@seracio/kohonen';

The Kohonen class is the main class.

// 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: 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

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.

mapping method returns grid position for each data provided on the constructor.

const myPositions = k.mapping();

umatrix method returns the U-Matrix of the grid (currently only with standardized vectors).

const umatrix = k.umatrix();

There are some heavy calculations in those 2 methods ; if you use them in the training callback (log), it's better not to use it on every step.

k.topographicError();
k.quantizationError();

k.training((neurons, step) => {
    if (step % 20 === 0) {
        k.topographicError();
        k.quantizationError();
    }
});

We've developed a full example on a dedicated repository

• The Self-Organizing Map (SOM)
• d3
• lodash/fp
• ml-pca
• Loadings vs eigenvector in PCA
• SOM tutorial
• Shyam M. Guthikonda