kohonen
A basic implementation of a Kohonen map in JavaScript
`We are still on an early stage of dev. Do not use this package until v1.0.0 has been released.
Usage
Import lib
npm i kohonen --save
Then, in your JS script :
;
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 | 10000 |
maxLearningCoef | Number | no | 1 | |
minLearningCoef | Number | no | .3 | |
maxNeighborhood | Number | no | 1 | |
minNeighborhood | Number | no | .3 |
// instanciate your Kohonen mapconst k = data neurons; // you can use the grid helper to generate a grid with 10x10 hexagonsconst k = data neurons: hexagonHelper;
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 | ()=>{} |
k;
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;
umatrix method
umatrix
method returns the U-Matrix of the grid (currently only with standardized vectors).
const umatrix = k;
Example
We've developed a full example on a dedicated repository