Kohonen
A basic implementation of a Kohonen map in JavaScript
Install / Use
/learn @seracio/KohonenREADME
kohonen 
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.
Usage
Import lib
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';
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 | 1000 | | maxLearningCoef | | Number | no | .4 | | minLearningCoef | | Number | no | .1 | | 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: 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 | (neurons, step)=>{} |
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
mapping method returns grid position for each data provided on the constructor.
const myPositions = k.mapping();
umatrix method
umatrix method returns the U-Matrix of the grid (currently only with standardized vectors).
const umatrix = k.umatrix();
errors
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();
}
});
Example
We've developed a full example on a dedicated repository
