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Java example source code file (ClusterEvaluator.java)

This example Java source code file (ClusterEvaluator.java) is included in the alvinalexander.com "Java Source Code Warehouse" project. The intent of this project is to help you "Learn Java by Example" TM.

Learn more about this Java project at its project page.

Java - Java tags/keywords

centroidcluster, cluster, clusterable, clusterevaluator, distancemeasure, doublepoint, euclideandistance, list, util

The ClusterEvaluator.java Java example source code

 * Licensed to the Apache Software Foundation (ASF) under one or more
 * contributor license agreements.  See the NOTICE file distributed with
 * this work for additional information regarding copyright ownership.
 * The ASF licenses this file to You under the Apache License, Version 2.0
 * (the "License"); you may not use this file except in compliance with
 * the License.  You may obtain a copy of the License at
 *      http://www.apache.org/licenses/LICENSE-2.0
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * See the License for the specific language governing permissions and
 * limitations under the License.

package org.apache.commons.math3.ml.clustering.evaluation;

import java.util.List;

import org.apache.commons.math3.ml.clustering.CentroidCluster;
import org.apache.commons.math3.ml.clustering.Cluster;
import org.apache.commons.math3.ml.clustering.Clusterable;
import org.apache.commons.math3.ml.clustering.DoublePoint;
import org.apache.commons.math3.ml.distance.DistanceMeasure;
import org.apache.commons.math3.ml.distance.EuclideanDistance;

 * Base class for cluster evaluation methods.
 * @param <T> type of the clustered points
 * @since 3.3
public abstract class ClusterEvaluator<T extends Clusterable> {

    /** The distance measure to use when evaluating the cluster. */
    private final DistanceMeasure measure;

     * Creates a new cluster evaluator with an {@link EuclideanDistance}
     * as distance measure.
    public ClusterEvaluator() {
        this(new EuclideanDistance());

     * Creates a new cluster evaluator with the given distance measure.
     * @param measure the distance measure to use
    public ClusterEvaluator(final DistanceMeasure measure) {
        this.measure = measure;

     * Computes the evaluation score for the given list of clusters.
     * @param clusters the clusters to evaluate
     * @return the computed score
    public abstract double score(List<? extends Cluster clusters);

     * Returns whether the first evaluation score is considered to be better
     * than the second one by this evaluator.
     * <p>
     * Specific implementations shall override this method if the returned scores
     * do not follow the same ordering, i.e. smaller score is better.
     * @param score1 the first score
     * @param score2 the second score
     * @return {@code true} if the first score is considered to be better, {@code false} otherwise
    public boolean isBetterScore(double score1, double score2) {
        return score1 < score2;

     * Calculates the distance between two {@link Clusterable} instances
     * with the configured {@link DistanceMeasure}.
     * @param p1 the first clusterable
     * @param p2 the second clusterable
     * @return the distance between the two clusterables
    protected double distance(final Clusterable p1, final Clusterable p2) {
        return measure.compute(p1.getPoint(), p2.getPoint());

     * Computes the centroid for a cluster.
     * @param cluster the cluster
     * @return the computed centroid for the cluster,
     * or {@code null} if the cluster does not contain any points
    protected Clusterable centroidOf(final Cluster<T> cluster) {
        final List<T> points = cluster.getPoints();
        if (points.isEmpty()) {
            return null;

        // in case the cluster is of type CentroidCluster, no need to compute the centroid
        if (cluster instanceof CentroidCluster) {
            return ((CentroidCluster<T>) cluster).getCenter();

        final int dimension = points.get(0).getPoint().length;
        final double[] centroid = new double[dimension];
        for (final T p : points) {
            final double[] point = p.getPoint();
            for (int i = 0; i < centroid.length; i++) {
                centroid[i] += point[i];
        for (int i = 0; i < centroid.length; i++) {
            centroid[i] /= points.size();
        return new DoublePoint(centroid);


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