/*
    * 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
    *
    *      https://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.
   */
  
  /*
   * This is not the original file distributed by the Apache Software Foundation
   * It has been modified by the Hipparchus project
   */
  
  package org.hipparchus.clustering;
  
  import org.hipparchus.clustering.distance.EuclideanDistance;
  import org.hipparchus.exception.MathIllegalArgumentException;
  import org.hipparchus.random.JDKRandomGenerator;
  import org.hipparchus.random.RandomGenerator;
  import org.junit.jupiter.api.BeforeEach;
  import org.junit.jupiter.api.Test;
  
  import java.util.ArrayList;
  import java.util.Arrays;
  import java.util.Collection;
  import java.util.List;
  
  import static org.junit.jupiter.api.Assertions.assertEquals;
  import static org.junit.jupiter.api.Assertions.assertThrows;
  import static org.junit.jupiter.api.Assertions.assertTrue;
  
  class KMeansPlusPlusClustererTest {
  
      private RandomGenerator random;
  
      @BeforeEach
      void setUp() {
          random = new JDKRandomGenerator();
          random.setSeed(1746432956321l);
      }
  
      /**
       * JIRA: MATH-305
       *
       * Two points, one cluster, one iteration
       */
      @Test
      void testPerformClusterAnalysisDegenerate() {
          KMeansPlusPlusClusterer<DoublePoint> transformer =
                  new KMeansPlusPlusClusterer<DoublePoint>(1, 1);
  
          DoublePoint[] points = new DoublePoint[] {
                  new DoublePoint(new int[] { 1959, 325100 }),
                  new DoublePoint(new int[] { 1960, 373200 }), };
          List<? extends Cluster<DoublePoint>> clusters = transformer.cluster(Arrays.asList(points));
          assertEquals(1, clusters.size());
          assertEquals(2, (clusters.get(0).getPoints().size()));
          DoublePoint pt1 = new DoublePoint(new int[] { 1959, 325100 });
          DoublePoint pt2 = new DoublePoint(new int[] { 1960, 373200 });
          assertTrue(clusters.get(0).getPoints().contains(pt1));
          assertTrue(clusters.get(0).getPoints().contains(pt2));
  
      }
  
      @Test
      void testCertainSpace() {
          KMeansPlusPlusClusterer.EmptyClusterStrategy[] strategies = {
              KMeansPlusPlusClusterer.EmptyClusterStrategy.LARGEST_VARIANCE,
              KMeansPlusPlusClusterer.EmptyClusterStrategy.LARGEST_POINTS_NUMBER,
              KMeansPlusPlusClusterer.EmptyClusterStrategy.FARTHEST_POINT
          };
          for (KMeansPlusPlusClusterer.EmptyClusterStrategy strategy : strategies) {
              int numberOfVariables = 27;
              // initialise testvalues
              int position1 = 1;
              int position2 = position1 + numberOfVariables;
              int position3 = position2 + numberOfVariables;
              int position4 = position3 + numberOfVariables;
              // testvalues will be multiplied
              int multiplier = 1000000;
  
              DoublePoint[] breakingPoints = new DoublePoint[numberOfVariables];
              // define the space which will break the cluster algorithm
              for (int i = 0; i < numberOfVariables; i++) {
                  int[] points = { position1, position2, position3, position4 };
                  // multiply the values
                  for (int j = 0; j < points.length; j++) {
                      points[j] *= multiplier;
                  }
                  DoublePoint DoublePoint = new DoublePoint(points);
                 breakingPoints[i] = DoublePoint;
                 position1 += numberOfVariables;
                 position2 += numberOfVariables;
                 position3 += numberOfVariables;
                 position4 += numberOfVariables;
             }
 
             for (int n = 2; n < 27; ++n) {
                 KMeansPlusPlusClusterer<DoublePoint> transformer =
                     new KMeansPlusPlusClusterer<DoublePoint>(n, 100, new EuclideanDistance(), random, strategy);
 
                 List<? extends Cluster<DoublePoint>> clusters =
                         transformer.cluster(Arrays.asList(breakingPoints));
 
                 assertEquals(n, clusters.size());
                 int sum = 0;
                 for (Cluster<DoublePoint> cluster : clusters) {
                     sum += cluster.getPoints().size();
                 }
                 assertEquals(numberOfVariables, sum);
             }
         }
 
     }
 
     /**
      * A helper class for testSmallDistances(). This class is similar to DoublePoint, but
      * it defines a different distanceFrom() method that tends to return distances less than 1.
      */
     private class CloseDistance extends EuclideanDistance {
         private static final long serialVersionUID = 1L;
 
         @Override
         public double compute(double[] a, double[] b) {
             return super.compute(a, b) * 0.001;
         }
     }
 
     /**
      * Test points that are very close together. See issue MATH-546.
      */
     @Test
     void testSmallDistances() {
         // Create a bunch of CloseDoublePoints. Most are identical, but one is different by a
         // small distance.
         int[] repeatedArray = { 0 };
         int[] uniqueArray = { 1 };
         DoublePoint repeatedPoint = new DoublePoint(repeatedArray);
         DoublePoint uniquePoint = new DoublePoint(uniqueArray);
 
         Collection<DoublePoint> points = new ArrayList<DoublePoint>();
         final int NUM_REPEATED_POINTS = 10 * 1000;
         for (int i = 0; i < NUM_REPEATED_POINTS; ++i) {
             points.add(repeatedPoint);
         }
         points.add(uniquePoint);
 
         // Ask a KMeansPlusPlusClusterer to run zero iterations (i.e., to simply choose initial
         // cluster centers).
         final long RANDOM_SEED = 0;
         final int NUM_CLUSTERS = 2;
         final int NUM_ITERATIONS = 0;
         random.setSeed(RANDOM_SEED);
 
         KMeansPlusPlusClusterer<DoublePoint> clusterer =
             new KMeansPlusPlusClusterer<DoublePoint>(NUM_CLUSTERS, NUM_ITERATIONS,
                     new CloseDistance(), random);
         List<CentroidCluster<DoublePoint>> clusters = clusterer.cluster(points);
 
         // Check that one of the chosen centers is the unique point.
         boolean uniquePointIsCenter = false;
         for (CentroidCluster<DoublePoint> cluster : clusters) {
             if (cluster.getCenter().equals(uniquePoint)) {
                 uniquePointIsCenter = true;
             }
         }
         assertTrue(uniquePointIsCenter);
     }
 
     /**
      * 2 variables cannot be clustered into 3 clusters. See issue MATH-436.
      */
     @Test
     void testPerformClusterAnalysisToManyClusters() {
         assertThrows(MathIllegalArgumentException.class, () -> {
             KMeansPlusPlusClusterer<DoublePoint> transformer =
                 new KMeansPlusPlusClusterer<DoublePoint>(3, 1, new EuclideanDistance(), random);
 
             DoublePoint[] points = new DoublePoint[]{
                 new DoublePoint(new int[]{
                     1959, 325100
                 }), new DoublePoint(new int[]{
                 1960, 373200
             })
             };
 
             transformer.cluster(Arrays.asList(points));
 
         });
 
     }
 
 }