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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    *      http://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  package org.apache.commons.math4.legacy.ml.clustering;
19  
20  import java.util.ArrayList;
21  import java.util.List;
22  
23  /**
24   * Cluster holding a set of {@link Clusterable} points.
25   * @param <T> the type of points that can be clustered
26   * @since 3.2
27   */
28  public class Cluster<T extends Clusterable> {
29  
30      /** The points contained in this cluster. */
31      private final List<T> points;
32  
33      /**
34       * Build a cluster centered at a specified point.
35       */
36      public Cluster() {
37          points = new ArrayList<>();
38      }
39  
40      /**
41       * Add a point to this cluster.
42       * @param point point to add
43       */
44      public void addPoint(final T point) {
45          points.add(point);
46      }
47  
48      /**
49       * Get the points contained in the cluster.
50       * @return points contained in the cluster
51       */
52      public List<T> getPoints() {
53          return points;
54      }
55  
56      /**
57       * Computes the centroid of the cluster.
58       *
59       * @return the centroid for the cluster, or {@code null} if the
60       * cluster does not contain any points.
61       */
62      public Clusterable centroid() {
63          if (points.isEmpty()) {
64              return null;
65          } else {
66              final int dimension = points.get(0).getPoint().length;
67              final double[] centroid = new double[dimension];
68              for (final T p : points) {
69                  final double[] point = p.getPoint();
70                  for (int i = 0; i < centroid.length; i++) {
71                      centroid[i] += point[i];
72                  }
73              }
74              for (int i = 0; i < centroid.length; i++) {
75                  centroid[i] /= points.size();
76              }
77              return new DoublePoint(centroid);
78          }
79      }
80  }