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/*
 * Copyright (c) 2010-2021 Haifeng Li. All rights reserved.
 *
 * Smile is free software: you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation, either version 3 of the License, or
 * (at your option) any later version.
 *
 * Smile is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU General Public License for more details.
 *
 * You should have received a copy of the GNU General Public License
 * along with Smile.  If not, see .
 */

package smile.clustering.linkage;

import smile.math.distance.Distance;

/**
 * Unweighted Pair Group Method using Centroids (also known as centroid linkage).
 * The distance between two clusters is the Euclidean distance between their
 * centroids, as calculated by arithmetic mean. Only valid for Euclidean
 * distance based proximity matrix.
 * 
 * @author Haifeng Li
 */
public class UPGMCLinkage extends Linkage {
    /**
     * The number of samples in each cluster.
     */
    private int[] n;

    /**
     * Constructor.
     * @param proximity the proximity matrix. Only the lower half will
     *                  be referred.
     */
    public UPGMCLinkage(double[][] proximity) {
        super(proximity);
        init();
    }

    /**
     * Constructor. Initialize the linkage with the lower triangular proximity matrix.
     * @param size the data size.
     * @param proximity the column-wise linearized proximity matrix that stores
     *                  only the lower half. The length of proximity should be
     *                  size * (size+1) / 2.
     *                  To save space, Linkage will use this argument directly
     *                  without copy. The elements may be modified.
     */
    public UPGMCLinkage(int size, float[] proximity) {
        super(size, proximity);
        init();
    }

    /** Initialize sample size. */
    private void init() {
        n = new int[size];
        for (int i = 0; i < size; i++) {
            n[i] = 1;
        }

        for (int i = 0; i < proximity.length; i++) {
            proximity[i] *= proximity[i];
        }
    }

    /**
     * Computes the proximity and the linkage.
     *
     * @param data the data points.
     * @return the linkage.
     */
    public static UPGMCLinkage of(double[][] data) {
        return new UPGMCLinkage(data.length, proximity(data));
    }

    /**
     * Computes the proximity and the linkage.
     *
     * @param data the data points.
     * @param distance the distance function.
     * @param  the data type of points.
     * @return the linkage.
     */
    public static  UPGMCLinkage of(T[] data, Distance distance) {
        return new UPGMCLinkage(data.length, proximity(data, distance));
    }

    @Override
    public String toString() {
        return "UPGMC linkage";
    }

    @Override
    public void merge(int i, int j) {
        float nij = n[i] + n[j];

        for (int k = 0; k < i; k++) {
            proximity[index(i, k)] = (d(i, k) * n[i] + d(j, k) * n[j] - d(j, i) * n[i] * n[j] / nij) / nij;
        }

        for (int k = i+1; k < j; k++) {
            proximity[index(k, i)] = (d(k, i) * n[i] + d(j, k) * n[j] - d(j, i) * n[i] * n[j] / nij) / nij;
        }

        for (int k = j+1; k < size; k++) {
            proximity[index(k, i)] = (d(k, i) * n[i] + d(k, j) * n[j] - d(j, i) * n[i] * n[j] / nij) / nij;
        }

        n[i] += n[j];
    }
}