1 /* 2 * Licensed to the Apache Software Foundation (ASF) under one or more 3 * contributor license agreements. See the NOTICE file distributed with 4 * this work for additional information regarding copyright ownership. 5 * The ASF licenses this file to You under the Apache License, Version 2.0 6 * (the "License"); you may not use this file except in compliance with 7 * the License. You may obtain a copy of the License at 8 * 9 * http://www.apache.org/licenses/LICENSE-2.0 10 * 11 * Unless required by applicable law or agreed to in writing, software 12 * distributed under the License is distributed on an "AS IS" BASIS, 13 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 14 * See the License for the specific language governing permissions and 15 * limitations under the License. 16 */ 17 18 package org.apache.commons.math4.legacy.ml.clustering; 19 20 import java.util.Collection; 21 import java.util.List; 22 23 import org.apache.commons.math4.legacy.ml.clustering.evaluation.SumOfClusterVariances; 24 25 /** 26 * A wrapper around a k-means++ clustering algorithm which performs multiple trials 27 * and returns the best solution. 28 * @param <T> type of the points to cluster 29 * @since 3.2 30 */ 31 public class MultiKMeansPlusPlusClusterer<T extends Clusterable> extends Clusterer<T> { 32 33 /** The underlying k-means clusterer. */ 34 private final KMeansPlusPlusClusterer<T> clusterer; 35 36 /** The number of trial runs. */ 37 private final int numTrials; 38 39 /** The cluster evaluator to use. */ 40 private final ClusterRanking evaluator; 41 42 /** Build a clusterer. 43 * @param clusterer the k-means clusterer to use 44 * @param numTrials number of trial runs 45 */ 46 public MultiKMeansPlusPlusClusterer(final KMeansPlusPlusClusterer<T> clusterer, 47 final int numTrials) { 48 this(clusterer, 49 numTrials, 50 ClusterEvaluator.ranking(new SumOfClusterVariances(clusterer.getDistanceMeasure()))); 51 } 52 53 /** Build a clusterer. 54 * @param clusterer the k-means clusterer to use 55 * @param numTrials number of trial runs 56 * @param evaluator the cluster evaluator to use 57 * @since 3.3 58 */ 59 public MultiKMeansPlusPlusClusterer(final KMeansPlusPlusClusterer<T> clusterer, 60 final int numTrials, 61 final ClusterRanking evaluator) { 62 super(clusterer.getDistanceMeasure()); 63 this.clusterer = clusterer; 64 this.numTrials = numTrials; 65 this.evaluator = evaluator; 66 } 67 68 /** 69 * Runs the K-means++ clustering algorithm. 70 * 71 * @param points the points to cluster 72 * @return a list of clusters containing the points 73 * @throws org.apache.commons.math4.legacy.exception.MathIllegalArgumentException if 74 * the data points are null or the number of clusters is larger than the 75 * number of data points 76 * @throws org.apache.commons.math4.legacy.exception.ConvergenceException if 77 * an empty cluster is encountered and the underlying {@link KMeansPlusPlusClusterer} 78 * has its {@link KMeansPlusPlusClusterer.EmptyClusterStrategy} is set to {@code ERROR}. 79 */ 80 @Override 81 public List<CentroidCluster<T>> cluster(final Collection<T> points) { 82 // at first, we have not found any clusters list yet 83 List<CentroidCluster<T>> best = null; 84 double bestRank = Double.NEGATIVE_INFINITY; 85 86 // do several clustering trials 87 for (int i = 0; i < numTrials; ++i) { 88 89 // compute a clusters list 90 List<CentroidCluster<T>> clusters = clusterer.cluster(points); 91 92 // compute the rank of the current list 93 final double rank = evaluator.compute(clusters); 94 95 if (rank > bestRank) { 96 // this one is the best we have found so far, remember it 97 best = clusters; 98 bestRank = rank; 99 } 100 } 101 102 // return the best clusters list found 103 return best; 104 } 105 }