https://github.com/seracio/kohonen
A basic implementation of a Kohonen map in JavaScript
https://github.com/seracio/kohonen
kohonen open-source som
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A basic implementation of a Kohonen map in JavaScript
- Host: GitHub
- URL: https://github.com/seracio/kohonen
- Owner: seracio
- License: mit
- Created: 2016-02-11T21:08:30.000Z (over 9 years ago)
- Default Branch: master
- Last Pushed: 2022-12-09T05:22:52.000Z (over 2 years ago)
- Last Synced: 2025-04-14T12:58:09.753Z (about 2 months ago)
- Topics: kohonen, open-source, som
- Language: TypeScript
- Size: 4.06 MB
- Stars: 13
- Watchers: 4
- Forks: 5
- Open Issues: 18
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# kohonen [](https://travis-ci.org/seracio/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 :
```javascript
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 |
```javascript
// 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)=>{} |
```javascript
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.
```javascript
const myPositions = k.mapping();
```
##### umatrix method
`umatrix` method returns the U-Matrix of the grid (currently only with standardized vectors).
```javascript
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.
```javascript
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](https://github.com/seracio/kohonen-stars)
## (Re)sources
- [The Self-Organizing Map (SOM)]
- [d3]
- [lodash/fp]
- [ml-pca]
- [Loadings vs eigenvector in PCA]
- [SOM tutorial]
- [Shyam M. Guthikonda]
[d3]: https://d3js.org
[lodash/fp]: https://github.com/lodash/lodash/wiki/FP-Guide
[ml-pca]: https://github.com/mljs/pca
[the self-organizing map (som)]: http://www.cis.hut.fi/projects/somtoolbox/theory/somalgorithm.shtml
[som tutorial]: http://www.ai-junkie.com/ann/som/som1.html
[loadings vs eigenvector in pca]: http://stats.stackexchange.com/questions/143905/loadings-vs-eigenvectors-in-pca-when-to-use-one-or-another
[shyam m. guthikonda]: http://www.shy.am/wp-content/uploads/2009/01/kohonen-self-organizing-maps-shyam-guthikonda.pdf