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import euclideanDistance from "./euclidean_distance.js";import makeMatrix from "./make_matrix.js";import sample from "./sample.js";
/** * @typedef {Object} kMeansReturn * @property {Array<number>} labels The labels. * @property {Array<Array<number>>} centroids The cluster centroids. */
/** * Perform k-means clustering. * * @param {Array<Array<number>>} points N-dimensional coordinates of points to be clustered. * @param {number} numCluster How many clusters to create. * @param {Function} randomSource An optional entropy source that generates uniform values in [0, 1). * @return {kMeansReturn} Labels (same length as data) and centroids (same length as numCluster). * @throws {Error} If any centroids wind up friendless (i.e., without associated points). * * @example * kMeansCluster([[0.0, 0.5], [1.0, 0.5]], 2); // => {labels: [0, 1], centroids: [[0.0, 0.5], [1.0 0.5]]} */function kMeansCluster(points, numCluster, randomSource = Math.random) {    let oldCentroids = null;    let newCentroids = sample(points, numCluster, randomSource);    let labels = null;    let change = Number.MAX_VALUE;    while (change !== 0) {        labels = labelPoints(points, newCentroids);        oldCentroids = newCentroids;        newCentroids = calculateCentroids(points, labels, numCluster);        change = calculateChange(newCentroids, oldCentroids);    }    return {        labels: labels,        centroids: newCentroids    };}
/** * Label each point according to which centroid it is closest to. * * @private * @param {Array<Array<number>>} points Array of XY coordinates. * @param {Array<Array<number>>} centroids Current centroids. * @return {Array<number>} Group labels. */function labelPoints(points, centroids) {    return points.map((p) => {        let minDist = Number.MAX_VALUE;        let label = -1;        for (let i = 0; i < centroids.length; i++) {            const dist = euclideanDistance(p, centroids[i]);            if (dist < minDist) {                minDist = dist;                label = i;            }        }        return label;    });}
/** * Calculate centroids for points given labels. * * @private * @param {Array<Array<number>>} points Array of XY coordinates. * @param {Array<number>} labels Which groups points belong to. * @param {number} numCluster Number of clusters being created. * @return {Array<Array<number>>} Centroid for each group. * @throws {Error} If any centroids wind up friendless (i.e., without associated points). */function calculateCentroids(points, labels, numCluster) {    // Initialize accumulators.    const dimension = points[0].length;    const centroids = makeMatrix(numCluster, dimension);    const counts = Array(numCluster).fill(0);
    // Add points to centroids' accumulators and count points per centroid.    const numPoints = points.length;    for (let i = 0; i < numPoints; i++) {        const point = points[i];        const label = labels[i];        const current = centroids[label];        for (let j = 0; j < dimension; j++) {            current[j] += point[j];        }        counts[label] += 1;    }
    // Rescale centroids, checking for any that have no points.    for (let i = 0; i < numCluster; i++) {        if (counts[i] === 0) {            throw new Error(`Centroid ${i} has no friends`);        }        const centroid = centroids[i];        for (let j = 0; j < dimension; j++) {            centroid[j] /= counts[i];        }    }
    return centroids;}
/** * Calculate the difference between old centroids and new centroids. * * @private * @param {Array<Array<number>>} left One list of centroids. * @param {Array<Array<number>>} right Another list of centroids. * @return {number} Distance between centroids. */function calculateChange(left, right) {    let total = 0;    for (let i = 0; i < left.length; i++) {        total += euclideanDistance(left[i], right[i]);    }    return total;}
export default kMeansCluster;