ml-kmeans/lib/kmeans.d.ts

K-Means clustering

import { KMeansResult } from './KMeansResult';
/**
 * Each step operation for kmeans
 * @ignore
 * @param {Array<Array<number>>} centers - K centers in format [x,y,z,...]
 * @param {Array<Array<number>>} data - Points [x,y,z,...] to cluster
 * @param {Array<number>} clusterID - Cluster identifier for each data dot
 * @param {number} K - Number of clusters
 * @param {object} [options] - Option object
 * @param {number} iterations - Current number of iterations
 * @return {KMeansResult}
 */
export type InitializationMethod = 'kmeans++' | 'random' | 'mostDistant';
export interface OptionsWithDefault {
    distanceFunction?: (p: number[], q: number[]) => number;
    tolerance?: number;
    initialization?: InitializationMethod | number[][];
    maxIterations?: number;
}
export interface OptionsWithoutDefault {
    seed?: number;
}
export type Options = OptionsWithDefault & OptionsWithoutDefault;
/**
 * Generator version for the algorithm
 * @ignore
 * @param {Array<Array<number>>} centers - K centers in format [x,y,z,...]
 * @param {Array<Array<number>>} data - Points [x,y,z,...] to cluster
 * @param {Array<number>} clusterID - Cluster identifier for each data dot
 * @param {number} K - Number of clusters
 * @param {object} [options] - Option object
 */
export declare function kmeansGenerator(data: number[][], K: number, options: Options): Generator<KMeansResult, void, unknown>;
/**
 * K-means algorithm
 * @param {Array<Array<number>>} data - Points in the format to cluster [x,y,z,...]
 * @param {number} K - Number of clusters
 * @param {object} [options] - Option object
 * @param {number} [options.maxIterations = 100] - Maximum of iterations allowed
 * @param {number} [options.tolerance = 1e-6] - Error tolerance
 * @param {function} [options.distanceFunction = squaredDistance] - Distance function to use between the points
 * @param {number} [options.seed] - Seed for random initialization.
 * @param {string|Array<Array<number>>} [options.initialization = 'kmeans++'] - K centers in format [x,y,z,...] or a method for initialize the data:
 *  * You can either specify your custom start centroids, or select one of the following initialization method:
 *  * `'kmeans++'` will use the kmeans++ method as described by http://ilpubs.stanford.edu:8090/778/1/2006-13.pdf
 *  * `'random'` will choose K random different values.
 *  * `'mostDistant'` will choose the more distant points to a first random pick
 * @return {KMeansResult} - Cluster identifier for each data dot and centroids with the following fields:
 *  * `'clusters'`: Array of indexes for the clusters.
 *  * `'centroids'`: Array with the resulting centroids.
 *  * `'iterations'`: Number of iterations that took to converge
 */
export declare function kmeans(data: number[][], K: number, options: Options): KMeansResult;
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