import { KMeansResult } from './KMeansResult.ts';
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 of the algorithm that yields the result of each iteration.
 * @param data - Points [x,y,z,...] to cluster
 * @param K - Number of clusters
 * @param [options] - Option object
 * @yields {KMeansResult} The result of each iteration.
 */
export declare function kmeansGenerator(data: number[][], K: number, options: Options): Generator<KMeansResult, void, unknown>;
/**
 * K-means algorithm
 * @param data - Points in the format to cluster [x,y,z,...]
 * @param K - Number of clusters
 * @param [options] - Option object
 * @param [options.maxIterations = 100] - Maximum of iterations allowed
 * @param [options.tolerance = 1e-6] - Error tolerance
 * @param [options.distanceFunction = squaredDistance] - Distance function to use between the points
 * @param [options.seed] - Seed for random initialization.
 * @param [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
 * @returns - 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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