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 * https://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 /*
19 * This is not the original file distributed by the Apache Software Foundation
20 * It has been modified by the Hipparchus project
21 */
22
23 package org.hipparchus.clustering.evaluation;
24
25 import java.util.List;
26
27 import org.hipparchus.clustering.Cluster;
28 import org.hipparchus.clustering.Clusterable;
29 import org.hipparchus.clustering.distance.DistanceMeasure;
30 import org.hipparchus.stat.descriptive.moment.Variance;
31
32 /**
33 * Computes the sum of intra-cluster distance variances according to the formula:
34 * \] score = \sum\limits_{i=1}^n \sigma_i^2 \]
35 * where n is the number of clusters and \( \sigma_i^2 \) is the variance of
36 * intra-cluster distances of cluster \( c_i \).
37 *
38 * @param <T> the type of the clustered points
39 */
40 public class SumOfClusterVariances<T extends Clusterable> extends ClusterEvaluator<T> {
41
42 /** Simple constructor.
43 * @param measure the distance measure to use
44 */
45 public SumOfClusterVariances(final DistanceMeasure measure) {
46 super(measure);
47 }
48
49 /** {@inheritDoc} */
50 @Override
51 public double score(final List<? extends Cluster<T>> clusters) {
52 double varianceSum = 0.0;
53 for (final Cluster<T> cluster : clusters) {
54 if (!cluster.getPoints().isEmpty()) {
55
56 final Clusterable center = centroidOf(cluster);
57
58 // compute the distance variance of the current cluster
59 final Variance stat = new Variance();
60 for (final T point : cluster.getPoints()) {
61 stat.increment(distance(point, center));
62 }
63 varianceSum += stat.getResult();
64
65 }
66 }
67 return varianceSum;
68 }
69
70 }