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    * 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.math4.legacy.ml.clustering;
  
  import java.util.ArrayList;
  import java.util.Arrays;
  import java.util.List;
  
  import org.apache.commons.math4.legacy.exception.MathIllegalArgumentException;
  import org.apache.commons.math4.legacy.exception.NullArgumentException;
  import org.apache.commons.math4.legacy.ml.distance.CanberraDistance;
  import org.apache.commons.math4.legacy.ml.distance.DistanceMeasure;
  import org.apache.commons.rng.simple.RandomSource;
  import org.apache.commons.rng.UniformRandomProvider;
  import org.junit.Assert;
  import org.junit.Test;
  
  /**
   * Test cases for FuzzyKMeansClusterer.
   *
   * @since 3.3
   */
  public class FuzzyKMeansClustererTest {
  
      @Test
      public void testCluster() {
          final List<DoublePoint> points = new ArrayList<>();
  
          // create 10 data points: [1], ... [10]
          for (int i = 1; i <= 10; i++) {
              final DoublePoint p = new DoublePoint(new double[] { i } );
              points.add(p);
          }
  
          final FuzzyKMeansClusterer<DoublePoint> transformer =
                  new FuzzyKMeansClusterer<>(3, 2.0);
          final List<CentroidCluster<DoublePoint>> clusters = transformer.cluster(points);
  
          // we expect 3 clusters:
          //   [1], [2], [3]
          //   [4], [5], [6], [7]
          //   [8], [9], [10]
          final List<DoublePoint> clusterOne = Arrays.asList(points.get(0), points.get(1), points.get(2));
          final List<DoublePoint> clusterTwo = Arrays.asList(points.get(3), points.get(4), points.get(5), points.get(6));
          final List<DoublePoint> clusterThree = Arrays.asList(points.get(7), points.get(8), points.get(9));
  
          boolean cluster1Found = false;
          boolean cluster2Found = false;
          boolean cluster3Found = false;
          Assert.assertEquals(3, clusters.size());
          for (final Cluster<DoublePoint> cluster : clusters) {
              if (cluster.getPoints().containsAll(clusterOne)) {
                  cluster1Found = true;
              }
              if (cluster.getPoints().containsAll(clusterTwo)) {
                  cluster2Found = true;
              }
              if (cluster.getPoints().containsAll(clusterThree)) {
                  cluster3Found = true;
              }
          }
          Assert.assertTrue(cluster1Found);
          Assert.assertTrue(cluster2Found);
          Assert.assertTrue(cluster3Found);
      }
  
      @Test(expected = MathIllegalArgumentException.class)
      public void testTooSmallFuzzynessFactor() {
          new FuzzyKMeansClusterer<>(3, 1.0);
      }
  
      @Test(expected = NullArgumentException.class)
      public void testNullDataset() {
          final FuzzyKMeansClusterer<DoublePoint> clusterer = new FuzzyKMeansClusterer<>(3, 2.0);
          clusterer.cluster(null);
      }
  
      @Test
      public void testGetters() {
          final DistanceMeasure measure = new CanberraDistance();
          final UniformRandomProvider random = RandomSource.MT_64.create();
          final FuzzyKMeansClusterer<DoublePoint> clusterer =
                  new FuzzyKMeansClusterer<>(3, 2.0, 100, measure, 1e-6, random);
  
          Assert.assertEquals(3, clusterer.getK());
         Assert.assertEquals(2.0, clusterer.getFuzziness(), 1e-6);
         Assert.assertEquals(100, clusterer.getMaxIterations());
         Assert.assertEquals(1e-6, clusterer.getEpsilon(), 1e-12);
         Assert.assertEquals(measure, clusterer.getDistanceMeasure());
         Assert.assertEquals(random, clusterer.getRandomGenerator());
     }
 
     @Test
     public void testSingleCluster() {
         final List<DoublePoint> points = new ArrayList<>();
         points.add(new DoublePoint(new double[] { 1, 1 }));
 
         final FuzzyKMeansClusterer<DoublePoint> transformer =
                 new FuzzyKMeansClusterer<>(1, 2.0);
         final List<CentroidCluster<DoublePoint>> clusters = transformer.cluster(points);
 
         Assert.assertEquals(1, clusters.size());
     }
 
     @Test
     public void testClusterCenterEqualsPoints() {
         final List<DoublePoint> points = new ArrayList<>();
         points.add(new DoublePoint(new double[] { 1, 1 }));
         points.add(new DoublePoint(new double[] { 1.00001, 1.00001 }));
         points.add(new DoublePoint(new double[] { 2, 2 }));
         points.add(new DoublePoint(new double[] { 3, 3 }));
 
         final FuzzyKMeansClusterer<DoublePoint> transformer =
                 new FuzzyKMeansClusterer<>(3, 2.0);
         final List<CentroidCluster<DoublePoint>> clusters = transformer.cluster(points);
 
         Assert.assertEquals(3, clusters.size());
     }
 }