kohonen

A basic implementation of a Kohonen map in JavaScript, forked from here: https://github.com/seracio/kohonen

Beyond the basic Kohonen map or self organising map (SOM), the SOM discriminant index SOMDI is implemented to provide variable selection.

This has further been extended to provide classification based on SOMDI to identify the winning class for each neuron, and learning vector quantization (LVQ) as a method of supervised learning that can be layered in.

Example

Continue below for information on how to use the Kohonen class, or see the Raman Tools reposistory for a web app GUI using these functions. An example of the application can be found at:

http://raman.banbury.ch

Usage

Import lib

npm i https://github.com/cbanbury/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	options
neurons	grid of neurons	Array	yes
data	dataset	Array of Array	yes
labels	datset	Array	no
maxStep	step max to clamp	Number	no	10000
maxLearningCoef		Number	no	1
minLearningCoef		Number	no	.3
maxNeighborhood		Number	no	1
minNeighborhood		Number	no	.3
norm	normalisation type	String	no		'zcore', 'max'
distance	distance metric	String	no	Euclidian	'manhattan'
class_method	classificatin method	String	no	somdi	'hits'

// 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.

labels parameter is an array of integer labels to describe the class that each sample in the data belongs to.

The function of the constructor is:

• standardize the given data set
• initialize random weights for neurons
• bind data and labels, create additional weights for SOMDI if applicable

Example usage

// setup some dummy data (RGB colour values)
var data = [
  [1, 0, 0], 
  [0.8, 0.1, 0], 
  [0.2, 1, 0.2],
  [0, 0.3, 1]
];

// 0 = red, 1 = green, 2 = blue
var labels = [0, 0, 1, 2];

var neurons = hexagonHelper.generateGrid(4, 4);
const k = new Kohonen({
  data: data,
  labels: labels,
  neurons,
  maxStep: 1000,
  maxLearningCoef: 0.1,
  minLearningCoef: 0.001,
  maxNeighborhood: 3,
  minNeighborhood: 0.1
});

// train SOM
k.learn((neurons, step)=>{
  console.log(step);
});

// apply LVQ
k.LVQ();

// Grab results
SOM = k.mapping();
SOMDI_RED = k.SOMDI(0);
SOMDI_GREEN = k.SOMDI(1);
SOMDI_BLUE = k.SOMDI(2);

// make some predictions
var testData = [
    [0.9, 0.2, 0.3],
    [0, 0, 1]
];

predictions = k._predict(testData);