/*
* 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.math3.ml.clustering;
import java.util.Collection;
import java.util.List;
import org.apache.commons.math3.exception.ConvergenceException;
import org.apache.commons.math3.exception.MathIllegalArgumentException;
import org.apache.commons.math3.stat.descriptive.moment.Variance;
/**
* 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
* @version $Id: MultiKMeansPlusPlusClusterer.java 1462375 2013-03-29 01:42:42Z psteitz $
* @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;
/** 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) {
super(clusterer.getDistanceMeasure());
this.clusterer = clusterer;
this.numTrials = numTrials;
}
/**
* Returns the embedded k-means clusterer used by this instance.
* @return the embedded clusterer
*/
public KMeansPlusPlusClusterer<T> getClusterer() {
return clusterer;
}
/**
* Returns the number of trials this instance will do.
* @return the number of trials
*/
public int getNumTrials() {
return numTrials;
}
/**
* Runs the K-means++ clustering algorithm.
*
* @param points the points to cluster
* @return a list of clusters containing the points
* @throws MathIllegalArgumentException if the data points are null or the number
* of clusters is larger than the number of data points
* @throws ConvergenceException if an empty cluster is encountered and the
* underlying {@link KMeansPlusPlusClusterer} has its
* {@link KMeansPlusPlusClusterer.EmptyClusterStrategy} is set to {@code ERROR}.
*/
public List<CentroidCluster<T>> cluster(final Collection<T> points)
throws MathIllegalArgumentException, ConvergenceException {
// at first, we have not found any clusters list yet
List<CentroidCluster<T>> best = null;
double bestVarianceSum = Double.POSITIVE_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 variance of the current list
double varianceSum = 0.0;
for (final CentroidCluster<T> cluster : clusters) {
if (!cluster.getPoints().isEmpty()) {
// compute the distance variance of the current cluster
final Clusterable center = cluster.getCenter();
final Variance stat = new Variance();
for (final T point : cluster.getPoints()) {
stat.increment(distance(point, center));
}
varianceSum += stat.getResult();
}
}
if (varianceSum <= bestVarianceSum) {
// this one is the best we have found so far, remember it
best = clusters;
bestVarianceSum = varianceSum;
}
}
// return the best clusters list found
return best;
}
}