test_class.declarator: data1 = new GmmPartitionData(
          Arrays.asList(
              vec(1, 0),
              vec(0, 1),
              vec(3, 7)
          ),
          new double[3][]
      )
test_class.original_string: GmmPartitionData data1 = new GmmPartitionData(
          Arrays.asList(
              vec(1, 0),
              vec(0, 1),
              vec(3, 7)
          ),
          new double[3][]
      );
test_class.type: GmmPartitionData
test_class.var_name: data1
test_class.declarator: data2 = new GmmPartitionData(
          Arrays.asList(
              vec(3, 1),
              vec(1, 4),
              vec(1, 3)
          ),
          new double[3][]
      )
test_class.original_string: GmmPartitionData data2 = new GmmPartitionData(
          Arrays.asList(
              vec(3, 1),
              vec(1, 4),
              vec(1, 3)
          ),
          new double[3][]
      );
test_class.type: GmmPartitionData
test_class.var_name: data2
test_class.declarator: model
test_class.original_string: GmmModel model;
test_class.type: GmmModel
test_class.var_name: model
test_class.file: modules/ml/src/test/java/org/apache/ignite/ml/clustering/gmm/NewComponentStatisticsAggregatorTest.java
test_class.identifier: NewComponentStatisticsAggregatorTest
test_case.body: @Test
      public void testPlus() {
          NewComponentStatisticsAggregator agg1 = new NewComponentStatisticsAggregator();
          NewComponentStatisticsAggregator agg2 = new NewComponentStatisticsAggregator();
          int rowCount = 10;
          for (int i = 0; i < rowCount; i++)
              agg1.add(VectorUtils.of(0, 1, 2), i % 2 == 0);
  
          for (int i = 0; i < rowCount; i++)
              agg2.add(VectorUtils.of(2, 1, 0), i % 2 == 1);
  
          NewComponentStatisticsAggregator sum = agg1.plus(agg2);
          assertEquals(rowCount, sum.rowCountForNewCluster());
          assertEquals(rowCount * 2, sum.totalRowCount());
          assertArrayEquals(new double[] {1, 1, 1}, sum.mean().asArray(), 1e-4);
      }
test_case.class_method_signature: NewComponentStatisticsAggregatorTest.testPlus()
test_case.full_signature: @Test public void testPlus()
test_case.identifier: testPlus
test_case.invocations: add of add of plus assertEquals rowCountForNewCluster assertEquals totalRowCount assertArrayEquals asArray mean
test_case.modifiers: @Test public
test_case.parameters: ()
test_case.return: void
test_case.signature: void testPlus()
focal_class.declarator: serialVersionUID = 6748270328889375005L
focal_class.modifier: private static final
focal_class.original_string: private static final long serialVersionUID = 6748270328889375005L;
focal_class.type: long
focal_class.var_name: serialVersionUID
focal_class.declarator: totalRowCount
focal_class.modifier: private
focal_class.original_string: private long totalRowCount;
focal_class.type: long
focal_class.var_name: totalRowCount
focal_class.declarator: rowCountForNewCluster
focal_class.modifier: private
focal_class.original_string: private long rowCountForNewCluster;
focal_class.type: long
focal_class.var_name: rowCountForNewCluster
focal_class.declarator: sumOfAnomalies
focal_class.modifier: private
focal_class.original_string: private Vector sumOfAnomalies;
focal_class.type: Vector
focal_class.var_name: sumOfAnomalies
focal_class.file: modules/ml/src/main/java/org/apache/ignite/ml/clustering/gmm/NewComponentStatisticsAggregator.java
focal_class.identifier: NewComponentStatisticsAggregator
focal_class.interfaces: implements Serializable
focal_class.class_method_signature: NewComponentStatisticsAggregator.NewComponentStatisticsAggregator(long totalRowCount, long rowCountForNewCluster, Vector sumOfAnomalies)
focal_class.full_signature: public  NewComponentStatisticsAggregator(long totalRowCount, long rowCountForNewCluster, Vector sumOfAnomalies)
focal_class.identifier: NewComponentStatisticsAggregator
focal_class.modifiers: public
focal_class.parameters: (long totalRowCount, long rowCountForNewCluster, Vector sumOfAnomalies)
focal_class.signature:  NewComponentStatisticsAggregator(long totalRowCount, long rowCountForNewCluster, Vector sumOfAnomalies)
focal_class.class_method_signature: NewComponentStatisticsAggregator.NewComponentStatisticsAggregator()
focal_class.full_signature: public  NewComponentStatisticsAggregator()
focal_class.identifier: NewComponentStatisticsAggregator
focal_class.modifiers: public
focal_class.parameters: ()
focal_class.signature:  NewComponentStatisticsAggregator()
focal_class.class_method_signature: NewComponentStatisticsAggregator.mean()
focal_class.full_signature: public Vector mean()
focal_class.identifier: mean
focal_class.modifiers: public
focal_class.parameters: ()
focal_class.return: Vector
focal_class.signature: Vector mean()
focal_class.class_method_signature: NewComponentStatisticsAggregator.rowCountForNewCluster()
focal_class.full_signature: public long rowCountForNewCluster()
focal_class.identifier: rowCountForNewCluster
focal_class.modifiers: public
focal_class.parameters: ()
focal_class.return: long
focal_class.signature: long rowCountForNewCluster()
focal_class.class_method_signature: NewComponentStatisticsAggregator.totalRowCount()
focal_class.full_signature: public long totalRowCount()
focal_class.identifier: totalRowCount
focal_class.modifiers: public
focal_class.parameters: ()
focal_class.return: long
focal_class.signature: long totalRowCount()
focal_class.class_method_signature: NewComponentStatisticsAggregator.computeNewMean(Dataset<EmptyContext, GmmPartitionData> dataset,
          double maxXsProb, double maxProbDivergence, GmmModel currentModel)
focal_class.full_signature: static NewComponentStatisticsAggregator computeNewMean(Dataset<EmptyContext, GmmPartitionData> dataset,
          double maxXsProb, double maxProbDivergence, GmmModel currentModel)
focal_class.identifier: computeNewMean
focal_class.modifiers: static
focal_class.parameters: (Dataset<EmptyContext, GmmPartitionData> dataset,
          double maxXsProb, double maxProbDivergence, GmmModel currentModel)
focal_class.return: NewComponentStatisticsAggregator
focal_class.signature: NewComponentStatisticsAggregator computeNewMean(Dataset<EmptyContext, GmmPartitionData> dataset,
          double maxXsProb, double maxProbDivergence, GmmModel currentModel)
focal_class.class_method_signature: NewComponentStatisticsAggregator.computeNewMeanMap(GmmPartitionData data, double maxXsProb,
          double maxProbDivergence, GmmModel currentModel)
focal_class.full_signature: static NewComponentStatisticsAggregator computeNewMeanMap(GmmPartitionData data, double maxXsProb,
          double maxProbDivergence, GmmModel currentModel)
focal_class.identifier: computeNewMeanMap
focal_class.modifiers: static
focal_class.parameters: (GmmPartitionData data, double maxXsProb,
          double maxProbDivergence, GmmModel currentModel)
focal_class.return: NewComponentStatisticsAggregator
focal_class.signature: NewComponentStatisticsAggregator computeNewMeanMap(GmmPartitionData data, double maxXsProb,
          double maxProbDivergence, GmmModel currentModel)
focal_class.class_method_signature: NewComponentStatisticsAggregator.add(Vector x, boolean isAnomaly)
focal_class.full_signature:  void add(Vector x, boolean isAnomaly)
focal_class.identifier: add
focal_class.parameters: (Vector x, boolean isAnomaly)
focal_class.return: void
focal_class.signature: void add(Vector x, boolean isAnomaly)
focal_class.class_method_signature: NewComponentStatisticsAggregator.computeNewMeanReduce(NewComponentStatisticsAggregator left,
          NewComponentStatisticsAggregator right)
focal_class.full_signature: static NewComponentStatisticsAggregator computeNewMeanReduce(NewComponentStatisticsAggregator left,
          NewComponentStatisticsAggregator right)
focal_class.identifier: computeNewMeanReduce
focal_class.modifiers: static
focal_class.parameters: (NewComponentStatisticsAggregator left,
          NewComponentStatisticsAggregator right)
focal_class.return: NewComponentStatisticsAggregator
focal_class.signature: NewComponentStatisticsAggregator computeNewMeanReduce(NewComponentStatisticsAggregator left,
          NewComponentStatisticsAggregator right)
focal_class.class_method_signature: NewComponentStatisticsAggregator.plus(NewComponentStatisticsAggregator other)
focal_class.full_signature:  NewComponentStatisticsAggregator plus(NewComponentStatisticsAggregator other)
focal_class.identifier: plus
focal_class.parameters: (NewComponentStatisticsAggregator other)
focal_class.return: NewComponentStatisticsAggregator
focal_class.signature: NewComponentStatisticsAggregator plus(NewComponentStatisticsAggregator other)
focal_method.body: NewComponentStatisticsAggregator plus(NewComponentStatisticsAggregator other) {
          return new NewComponentStatisticsAggregator(
              totalRowCount + other.totalRowCount,
              rowCountForNewCluster + other.rowCountForNewCluster,
              sumOfAnomalies.plus(other.sumOfAnomalies)
          );
      }
focal_method.class_method_signature: NewComponentStatisticsAggregator.plus(NewComponentStatisticsAggregator other)
focal_method.full_signature:  NewComponentStatisticsAggregator plus(NewComponentStatisticsAggregator other)
focal_method.identifier: plus
focal_method.invocations: plus
focal_method.parameters: (NewComponentStatisticsAggregator other)
focal_method.return: NewComponentStatisticsAggregator
focal_method.signature: NewComponentStatisticsAggregator plus(NewComponentStatisticsAggregator other)