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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.evaluation;
import java.util.List;
import org.apache.commons.math3.ml.clustering.Cluster;
import org.apache.commons.math3.ml.clustering.Clusterable;
import org.apache.commons.math3.ml.distance.DistanceMeasure;
import org.apache.commons.math3.stat.descriptive.moment.Variance;
/**
* Computes the sum of intra-cluster distance variances according to the formula:
*
* \( score = \sum\limits_{i=1}^n \sigma_i^2 \)
*
* where n is the number of clusters and \( \sigma_i^2 \) is the variance of
* intra-cluster distances of cluster \( c_i \).
*
* @param the type of the clustered points
* @since 3.3
*/
public class SumOfClusterVariances extends ClusterEvaluator {
/**
*
* @param measure the distance measure to use
*/
public SumOfClusterVariances(final DistanceMeasure measure) {
super(measure);
}
/** {@inheritDoc} */
@Override
public double score(final List> clusters) {
double varianceSum = 0.0;
for (final Cluster cluster : clusters) {
if (!cluster.getPoints().isEmpty()) {
final Clusterable center = centroidOf(cluster);
// compute the distance variance of the current cluster
final Variance stat = new Variance();
for (final T point : cluster.getPoints()) {
stat.increment(distance(point, center));
}
varianceSum += stat.getResult();
}
}
return varianceSum;
}
}