"use strict";
var __importDefault = (this && this.__importDefault) || function (mod) {
    return (mod && mod.__esModule) ? mod : { "default": mod };
};
Object.defineProperty(exports, "__esModule", { value: true });
var clone_1 = __importDefault(require("@turf/clone"));
var meta_1 = require("@turf/meta");
var skmeans_1 = __importDefault(require("skmeans"));
/**
 * 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)
 *
 * @name clustersKmeans
 * @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];
 */
function clustersKmeans(points, options) {
    if (options === void 0) { options = {}; }
    // Default Params
    var count = points.features.length;
    options.numberOfClusters =
        options.numberOfClusters || Math.round(Math.sqrt(count / 2));
    // numberOfClusters can't be greater than the number of points
    // fallbacks to count
    if (options.numberOfClusters > count)
        options.numberOfClusters = count;
    // Clone points to prevent any mutations (enabled by default)
    if (options.mutate !== true)
        points = clone_1.default(points);
    // collect points coordinates
    var data = meta_1.coordAll(points);
    // create seed to avoid skmeans to drift
    var initialCentroids = data.slice(0, options.numberOfClusters);
    // create skmeans clusters
    var skmeansResult = skmeans_1.default(data, options.numberOfClusters, initialCentroids);
    // store centroids {clusterId: [number, number]}
    var centroids = {};
    skmeansResult.centroids.forEach(function (coord, idx) {
        centroids[idx] = coord;
    });
    // add associated cluster number
    meta_1.featureEach(points, function (point, index) {
        var clusterId = skmeansResult.idxs[index];
        point.properties.cluster = clusterId;
        point.properties.centroid = centroids[clusterId];
    });
    return points;
}
exports.default = clustersKmeans;