ml-kmeans/lib-esm/KMeansResult.js

K-Means clustering

import { updateClusterID } from './utils';
export class KMeansResult {
    constructor(clusters, centroids, converged, iterations, distance) {
        this.clusters = clusters;
        this.centroids = centroids;
        this.converged = converged;
        this.iterations = iterations;
        this.distance = distance;
    }
    /**
     * Allows to compute for a new array of points their cluster id
     * @param {Array<Array<number>>} data - the [x,y,z,...] points to cluster
     * @return {Array<number>} - cluster id for each point
     */
    nearest(data) {
        const clusterID = new Array(data.length);
        return updateClusterID(data, this.centroids, clusterID, this.distance);
    }
    /**
     * Returns the error and size of each cluster
     * @ignore
     * @param {Array<Array<number>>} data - the [x,y,z,...] points to cluster
     * @return {KMeansResult}
     */
    computeInformation(data) {
        let enrichedCentroids = this.centroids.map((centroid) => {
            return {
                centroid,
                error: 0,
                size: 0,
            };
        });
        for (let i = 0; i < data.length; i++) {
            enrichedCentroids[this.clusters[i]].error += this.distance(data[i], this.centroids[this.clusters[i]]);
            enrichedCentroids[this.clusters[i]].size++;
        }
        for (let j = 0; j < this.centroids.length; j++) {
            let error = enrichedCentroids[j].error;
            if (enrichedCentroids[j].size && error !== -1) {
                error /= enrichedCentroids[j].size;
            }
            else {
                enrichedCentroids[j].error = -1;
            }
        }
        return enrichedCentroids;
    }
}
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