@seracio/kohonen
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    3.0.2 • Public • Published

    kohonen Build Status

    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

    capture

    (Re)sources

    Keywords

    none

    Install

    npm i @seracio/kohonen

    DownloadsWeekly Downloads

    2

    Version

    3.0.2

    License

    MIT

    Unpacked Size

    242 kB

    Total Files

    18

    Last publish

    Collaborators

    • seracio