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The Apache Commons Math project is a library of lightweight, self-contained mathematics and statistics components addressing the most common practical problems not immediately available in the Java programming language or commons-lang.
/*
* 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.ml.clustering.evaluation.ClusterEvaluator;
import org.apache.commons.math3.ml.clustering.evaluation.SumOfClusterVariances;
/**
* A wrapper around a k-means++ clustering algorithm which performs multiple trials
* and returns the best solution.
* @param type of the points to cluster
* @since 3.2
*/
public class MultiKMeansPlusPlusClusterer extends Clusterer {
/** The underlying k-means clusterer. */
private final KMeansPlusPlusClusterer clusterer;
/** The number of trial runs. */
private final int numTrials;
/** The cluster evaluator to use. */
private final ClusterEvaluator evaluator;
/** Build a clusterer.
* @param clusterer the k-means clusterer to use
* @param numTrials number of trial runs
*/
public MultiKMeansPlusPlusClusterer(final KMeansPlusPlusClusterer clusterer,
final int numTrials) {
this(clusterer, numTrials, 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 clusterer,
final int numTrials,
final ClusterEvaluator evaluator) {
super(clusterer.getDistanceMeasure());
this.clusterer = clusterer;
this.numTrials = numTrials;
this.evaluator = evaluator;
}
/**
* Returns the embedded k-means clusterer used by this instance.
* @return the embedded clusterer
*/
public KMeansPlusPlusClusterer getClusterer() {
return clusterer;
}
/**
* Returns the number of trials this instance will do.
* @return the number of trials
*/
public int getNumTrials() {
return numTrials;
}
/**
* Returns the {@link ClusterEvaluator} used to determine the "best" clustering.
* @return the used {@link ClusterEvaluator}
* @since 3.3
*/
public ClusterEvaluator getClusterEvaluator() {
return evaluator;
}
/**
* 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}.
*/
@Override
public List> cluster(final Collection points)
throws MathIllegalArgumentException, ConvergenceException {
// at first, we have not found any clusters list yet
List> best = null;
double bestVarianceSum = Double.POSITIVE_INFINITY;
// do several clustering trials
for (int i = 0; i < numTrials; ++i) {
// compute a clusters list
List> clusters = clusterer.cluster(points);
// compute the variance of the current list
final double varianceSum = evaluator.score(clusters);
if (evaluator.isBetterScore(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;
}
}