A Kohonen Network api for NodeJS

Installation

npm install node-som

Usage

// Inject the module
var som = require('node-som');

// Create the instance
var somInstance = new som({
    inputLength: 2,
    maxClusters: 5,
    loggingEnabled: true,
    scale: {
        min : 0,
        max : 1000
    }
});

// Train (all automatic). Optionally, you can include custom input vectors, but 
// it is recommended that developers allow the automated training (based on the 
// `scale` configuration) since this results in better networks
somInstance.train();

// Create input array. 
// All items features should be normalized to domain [0,1]
var sample = [0.24, 0.34];

// Call classify to receive your classification group/neuron
var group = somInstance.classify(sample);

// For the sake of intelligence, you can then extract your 
// domain boundaries from a stored group by component.
var groupDomainBoundaries = somInstance.extract(group);

// The output is enough to do some pretty hardcore analysis on
// the cluster domain (wisker plot, etc)
//
// groupDomainBoundaries = [ 
//  { range: [ 0.002023763954639435, 999.9967189505696 ],
//    amean: 498.7489069010919,
//    gmean: 366.71135268678785,
//    median: 498.73221735469997,
//    stddev: 287.81665907708333,
//    gstddev: 2.7296960810401862,
//    moe: 1.7839061347985679 },
//  { range: [ 0.0025222543627023697, 999.9986472539604 ],
//    amean: 501.4137114080192,
//    gmean: 370.21158812427205,
//    median: 500.92090992257,
//    stddev: 288.3074606679444,
//    gstddev: 2.6996348342872674,
//    moe: 1.7869481545750234 } ]

Options

loggingEnabled (bool)

Enables/Disables logging on the console

maxClusters (Integer)

Max number of Classification groups to use. This is an upper-bound and an optimized network usually contains less than this number.

created (Date)

When the network was initially created

classificationCount (Integer)

Represents the number of classifications run through the network. Used as a metric of maturity.

inputLength (Integer)

Component length of input vectors

inputPatterns (Integer)

Represents the number of random samples to self-train on. Higher numbers cost more, but can result in more granular groups

scale (Complex)

Tells the SOM how to train to insure high accuracy on the target domain. ex. {min: -1000, max: 1000}

API

train

Accepts an array of unscaled input vectors of ordinal length inputLength. The implementation scales itself, based on the scale constructor configuration.

If no training vectors are provided, Recommended, a random set is generated and trained based on the __inputPatterns__ configuration.

Note: this results in the most optimized network

classify

Given a single unscaled input vector, will return the classification/neuron that best describes the input based on the network state

extract

Given a classication/neuron, will return component statistics for unscaled input vector extraction. Statistics are in the form of component ranges.

Its up to the developer to use this information to infer about their own domain, since the network doesn't hold on to the entire input domain upon training/classification (for momento purposes).

serialize

Serialize the network for future use.

Reloading the network can be done by passing the serialized json into the constructor on instantiation.

scaleInput

Scales a vector of inputLength based on the SOM's scale boundaries.

_Note: scaling is done internally. No need to do this unless extending the network implementation _

descaleInput

Descales a scalled vector of inputLength based on the SOM's scale boundaries.

_Note: descaling is done internally. No need to do this unless extending the network implementation. _

Samples

Colors Clustering

This example shows how the SOM can adaptively cluster a random RGB input space based on the network's training state.

run node examples/colors/colors-cluster.js from the root of the application.

After the batch script has completed running, open the resulting html file with open examples/colors/color.html

Node-som