MultiKMeansPlusPlusClusterer.java
- /*
- * 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,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
- package org.apache.commons.math4.legacy.ml.clustering;
- import java.util.Collection;
- import java.util.List;
- import org.apache.commons.math4.legacy.ml.clustering.evaluation.SumOfClusterVariances;
- /**
- * A wrapper around a k-means++ clustering algorithm which performs multiple trials
- * and returns the best solution.
- * @param <T> type of the points to cluster
- * @since 3.2
- */
- public class MultiKMeansPlusPlusClusterer<T extends Clusterable> extends Clusterer<T> {
- /** The underlying k-means clusterer. */
- private final KMeansPlusPlusClusterer<T> clusterer;
- /** The number of trial runs. */
- private final int numTrials;
- /** The cluster evaluator to use. */
- private final ClusterRanking evaluator;
- /** Build a clusterer.
- * @param clusterer the k-means clusterer to use
- * @param numTrials number of trial runs
- */
- public MultiKMeansPlusPlusClusterer(final KMeansPlusPlusClusterer<T> clusterer,
- final int numTrials) {
- this(clusterer,
- numTrials,
- ClusterEvaluator.ranking(new SumOfClusterVariances(clusterer.getDistanceMeasure())));
- }
- /** Build a clusterer.
- * @param clusterer the k-means clusterer to use
- * @param numTrials number of trial runs
- * @param evaluator the cluster evaluator to use
- * @since 3.3
- */
- public MultiKMeansPlusPlusClusterer(final KMeansPlusPlusClusterer<T> clusterer,
- final int numTrials,
- final ClusterRanking evaluator) {
- super(clusterer.getDistanceMeasure());
- this.clusterer = clusterer;
- this.numTrials = numTrials;
- this.evaluator = evaluator;
- }
- /**
- * Runs the K-means++ clustering algorithm.
- *
- * @param points the points to cluster
- * @return a list of clusters containing the points
- * @throws org.apache.commons.math4.legacy.exception.MathIllegalArgumentException if
- * the data points are null or the number of clusters is larger than the
- * number of data points
- * @throws org.apache.commons.math4.legacy.exception.ConvergenceException if
- * an empty cluster is encountered and the underlying {@link KMeansPlusPlusClusterer}
- * has its {@link KMeansPlusPlusClusterer.EmptyClusterStrategy} is set to {@code ERROR}.
- */
- @Override
- public List<CentroidCluster<T>> cluster(final Collection<T> points) {
- // at first, we have not found any clusters list yet
- List<CentroidCluster<T>> best = null;
- double bestRank = Double.NEGATIVE_INFINITY;
- // do several clustering trials
- for (int i = 0; i < numTrials; ++i) {
- // compute a clusters list
- List<CentroidCluster<T>> clusters = clusterer.cluster(points);
- // compute the rank of the current list
- final double rank = evaluator.compute(clusters);
- if (rank > bestRank) {
- // this one is the best we have found so far, remember it
- best = clusters;
- bestRank = rank;
- }
- }
- // return the best clusters list found
- return best;
- }
- }