Library for usage different neuron networks and combine them.
May be used for build Kohonen's neuron layer. Accepts any numeric values. Public methods:
- setData
const kohonen = new Kohonen();
kohonen.setData([
[ new BigNumber(2), new BigNumber(5), ... ],
[ new BigNumber(8), new BigNumber(3), ... ],
...
]);
- learn
const kohonen = new Kohonen();
kohonen.setData([
[ new BigNumber(2), new BigNumber(5), ... ],
[ new BigNumber(8), new BigNumber(3), ... ],
...
]);
kohonen.iterations = new BigNumber(1);
kohonen.range = new BigNumber(.5);
// here you can get clusters which was created by learning
// clusters === kohonen.clusters;
const clusters = kohonen.learn();
- clusterify
const kohonen = new Kohonen();
kohonen.setData([
[ new BigNumber(2), new BigNumber(5), ... ],
[ new BigNumber(8), new BigNumber(3), ... ],
...
]);
kohonen.iterations = new BigNumber(1);
kohonen.range = new BigNumber(.5);
kohonen.learn();
const dataToClusterify: BigNumber[] = [ new BigNumber(5), new BigNumber(6), ... ];
const cluster = kohonen.clusterify(dataToClusterify);
- setClusterStructure
const kohonen = new Kohonen();
kohonen.setData(bigNumberData);
const clusters: BigNumber[][] = [
[ new BigNumber(2), new BigNumber(5), ... ],
[ new BigNumber(8), new BigNumber(3), ... ],
...
];
kohonen.setClusterStructure(clusters);
const dataToClusterify: BigNumber[] = [ new BigNumber(2), new BigNumber(5), ... ];
const cluster = kohonen.clusterify(dataToClusterify);
- getDenormalizedClusters
const kohonen = new Kohonen();
kohonen.setData(bigNumberData);
const inputClusters = [
[ new BigNumber(32), new BigNumber(5200), ... ],
[ new BigNumber(20), new BigNumber(1000), ... ],
...
];
kohonen.setClusterStructure(clusters);
let denormalizedClusters = kohonen.getDenormalizedClusters();
// NOTICE: denormalizedClusters !== kohonen.clusters;
expect(denormalizedClusters).toEqual(inputClusters);
May be used for analysis binary data by ART-1 method (with long/short memory). Allows only binary data: 1 || 0
. Public methods:
- learn
const bar = new BinaryAdaptiveResonance();
bar.speed = new BigNumber(.5);
bar.range = new BigNumber(.8);
const data: BigNumber[][] = [
[new BigNumber(0), new BigNumber(1)],
[new BigNumber(1), new BigNumber(0)],
...
];
// clusters which was prepared on last `learn` call
const clusters: BigNumber[][] = bar.learn(data);
...
// clusters which were prepared on all `learn` calls
bar.clusters;
- clusterify
const bar = new AnalogAdaptiveResonance();
bar.speed = new BigNumber(.5);
bar.range = new BigNumber(.8);
const data: BigNumber[][] = [
[new BigNumber(1), new BigNumber(0), ...],
[new BigNumber(1), new BigNumber(1), ...]
];
bar.learn(data);
// cluster === bar.clusters[1]
const cluster = bar.clusterify(
[new BigNumber(0), new BigNumber(1), ...]
);
- getClosestCluster
const bar = new AnalogAdaptiveResonance();
bar.speed = new BigNumber(.5);
bar.range = new BigNumber(.8);
const data: BigNumber[][]= [
[new BigNumber(1), new BigNumber(0), ...],
[new BigNumber(0), new BigNumber(0), ...],
...
];
bar.learn(data);
const cluster = bar.getClosestCluster(
[new BigNumber(0), new BigNumber(1), ...]
);
May be used for analysis data by ART-2 method. Accepts any numeric values. Public methods:
- learn
const aar = new AnalogAdaptiveResonance();
aar.speed = new BigNumber(.5);
aar.range = new BigNumber(.8);
const data: BigNumber[][] = [
[ new BigNumber(200), new BigNumber(500), ... ],
[ new BigNumber(800), new BigNumber(300), ... ],
...
];
// clusters which was prepared on last `learn` call
const clusters: BigNumber[][] = aar.learn(data);
...
// clusters which were prepared on all `learn` calls
aar.clusters;
- clusterify
const aar = new AnalogAdaptiveResonance();
aar.speed = new BigNumber(.5);
aar.range = new BigNumber(.8);
const data: BigNumber[][] = [
[ new BigNumber(2), new BigNumber(5), ... ],
[ new BigNumber(8), new BigNumber(3), ... ],
...
];
aar.learn(data);
// cluster === aar.clusters[1]
const cluster = aar.clusterify(
[ new BigNumber(9), new BigNumber(3), ... ]
);
- getClosestCluster
const aar = new AnalogAdaptiveResonance();
aar.speed = new BigNumber(.5);
aar.range = new BigNumber(.8);
const data: BigNumber[][] = [
[ new BigNumber(2), new BigNumber(5), ... ],
[ new BigNumber(8), new BigNumber(3), ... ],
...
];
aar.learn(data);
const cluster = aar.getClosestCluster(
[ new BigNumber(9), new BigNumber(3), ... ]
);
// probably `anotherOne` will be equal `null`
const anotherOne = aar.getClosestCluster(
[ new BigNumber(999), new BigNumber(321), ... ]
);
For more info you may look into tests.
npm test