scancode-miro/node_modules/@turf/clusters-kmeans/dist/cjs/index.d.cts

import { GeoJsonProperties, FeatureCollection, Point } from 'geojson';

type KmeansProps = GeoJsonProperties & {
    cluster?: number;
    centroid?: [number, number];
};
/**
 * Takes a set of {@link Point|points} and partition them into clusters using the k-mean .
 * It uses the [k-means algorithm](https://en.wikipedia.org/wiki/K-means_clustering)
 *
 * @function
 * @param {FeatureCollection<Point>} points to be clustered
 * @param {Object} [options={}] Optional parameters
 * @param {number} [options.numberOfClusters=Math.sqrt(numberOfPoints/2)] numberOfClusters that will be generated
 * @param {boolean} [options.mutate=false] allows GeoJSON input to be mutated (significant performance increase if true)
 * @returns {FeatureCollection<Point>} Clustered Points with an additional two properties associated to each Feature:
 * - {number} cluster - the associated clusterId
 * - {[number, number]} centroid - Centroid of the cluster [Longitude, Latitude]
 * @example
 * // create random points with random z-values in their properties
 * var points = turf.randomPoint(100, {bbox: [0, 30, 20, 50]});
 * var options = {numberOfClusters: 7};
 * var clustered = turf.clustersKmeans(points, options);
 *
 * //addToMap
 * var addToMap = [clustered];
 */
declare function clustersKmeans(points: FeatureCollection<Point>, options?: {
    numberOfClusters?: number;
    mutate?: boolean;
}): FeatureCollection<Point, KmeansProps>;

export { type KmeansProps, clustersKmeans, clustersKmeans as default };