test_class.declarator: aggregator1 = new MeanWithClusterProbAggregator()
test_class.modifier: private
test_class.original_string: private MeanWithClusterProbAggregator aggregator1 = new MeanWithClusterProbAggregator();
test_class.type: MeanWithClusterProbAggregator
test_class.var_name: aggregator1
test_class.declarator: aggregator2 = new MeanWithClusterProbAggregator()
test_class.modifier: private
test_class.original_string: private MeanWithClusterProbAggregator aggregator2 = new MeanWithClusterProbAggregator();
test_class.type: MeanWithClusterProbAggregator
test_class.var_name: aggregator2
test_class.file: modules/ml/src/test/java/org/apache/ignite/ml/clustering/gmm/MeanWithClusterProbAggregatorTest.java
test_class.identifier: MeanWithClusterProbAggregatorTest
test_case.body: @Test
      public void testAdd() {
          assertArrayEquals(new double[] {0.781, 0.468}, aggregator1.mean().asArray(), 1e-2);
          assertArrayEquals(new double[] {0.5, 0.5}, aggregator2.mean().asArray(), 1e-2);
  
          assertEquals(0.4, aggregator1.clusterProb(), 1e-4);
          assertEquals(1.0, aggregator2.clusterProb(), 1e-4);
      }
test_case.class_method_signature: MeanWithClusterProbAggregatorTest.testAdd()
test_case.full_signature: @Test public void testAdd()
test_case.identifier: testAdd
test_case.invocations: assertArrayEquals asArray mean assertArrayEquals asArray mean assertEquals clusterProb assertEquals clusterProb
test_case.modifiers: @Test public
test_case.parameters: ()
test_case.return: void
test_case.signature: void testAdd()
focal_class.declarator: serialVersionUID = 2700985110021774629L
focal_class.modifier: private static final
focal_class.original_string: private static final long serialVersionUID = 2700985110021774629L;
focal_class.type: long
focal_class.var_name: serialVersionUID
focal_class.declarator: weightedXsSum
focal_class.modifier: private
focal_class.original_string: private Vector weightedXsSum;
focal_class.type: Vector
focal_class.var_name: weightedXsSum
focal_class.declarator: pcxiSum
focal_class.modifier: private
focal_class.original_string: private double pcxiSum;
focal_class.type: double
focal_class.var_name: pcxiSum
focal_class.declarator: rowCount
focal_class.modifier: private
focal_class.original_string: private int rowCount;
focal_class.type: int
focal_class.var_name: rowCount
focal_class.file: modules/ml/src/main/java/org/apache/ignite/ml/clustering/gmm/MeanWithClusterProbAggregator.java
focal_class.identifier: MeanWithClusterProbAggregator
focal_class.interfaces: implements Serializable
focal_class.class_method_signature: MeanWithClusterProbAggregator.MeanWithClusterProbAggregator()
focal_class.full_signature:   MeanWithClusterProbAggregator()
focal_class.identifier: MeanWithClusterProbAggregator
focal_class.parameters: ()
focal_class.signature:  MeanWithClusterProbAggregator()
focal_class.class_method_signature: MeanWithClusterProbAggregator.MeanWithClusterProbAggregator(Vector weightedXsSum, double pcxiSum, int rowCount)
focal_class.full_signature:   MeanWithClusterProbAggregator(Vector weightedXsSum, double pcxiSum, int rowCount)
focal_class.identifier: MeanWithClusterProbAggregator
focal_class.parameters: (Vector weightedXsSum, double pcxiSum, int rowCount)
focal_class.signature:  MeanWithClusterProbAggregator(Vector weightedXsSum, double pcxiSum, int rowCount)
focal_class.class_method_signature: MeanWithClusterProbAggregator.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: MeanWithClusterProbAggregator.clusterProb()
focal_class.full_signature: public double clusterProb()
focal_class.identifier: clusterProb
focal_class.modifiers: public
focal_class.parameters: ()
focal_class.return: double
focal_class.signature: double clusterProb()
focal_class.class_method_signature: MeanWithClusterProbAggregator.aggreateStats(Dataset<EmptyContext, GmmPartitionData> dataset, int countOfComponents)
focal_class.full_signature: public static AggregatedStats aggreateStats(Dataset<EmptyContext, GmmPartitionData> dataset, int countOfComponents)
focal_class.identifier: aggreateStats
focal_class.modifiers: public static
focal_class.parameters: (Dataset<EmptyContext, GmmPartitionData> dataset, int countOfComponents)
focal_class.return: AggregatedStats
focal_class.signature: AggregatedStats aggreateStats(Dataset<EmptyContext, GmmPartitionData> dataset, int countOfComponents)
focal_class.class_method_signature: MeanWithClusterProbAggregator.add(Vector x, double pcxi)
focal_class.full_signature:  void add(Vector x, double pcxi)
focal_class.identifier: add
focal_class.parameters: (Vector x, double pcxi)
focal_class.return: void
focal_class.signature: void add(Vector x, double pcxi)
focal_class.class_method_signature: MeanWithClusterProbAggregator.plus(MeanWithClusterProbAggregator other)
focal_class.full_signature:  MeanWithClusterProbAggregator plus(MeanWithClusterProbAggregator other)
focal_class.identifier: plus
focal_class.parameters: (MeanWithClusterProbAggregator other)
focal_class.return: MeanWithClusterProbAggregator
focal_class.signature: MeanWithClusterProbAggregator plus(MeanWithClusterProbAggregator other)
focal_class.class_method_signature: MeanWithClusterProbAggregator.map(GmmPartitionData data, int countOfComponents)
focal_class.full_signature: static List<MeanWithClusterProbAggregator> map(GmmPartitionData data, int countOfComponents)
focal_class.identifier: map
focal_class.modifiers: static
focal_class.parameters: (GmmPartitionData data, int countOfComponents)
focal_class.return: List<MeanWithClusterProbAggregator>
focal_class.signature: List<MeanWithClusterProbAggregator> map(GmmPartitionData data, int countOfComponents)
focal_class.class_method_signature: MeanWithClusterProbAggregator.reduce(List<MeanWithClusterProbAggregator> l,
          List<MeanWithClusterProbAggregator> r)
focal_class.full_signature: static List<MeanWithClusterProbAggregator> reduce(List<MeanWithClusterProbAggregator> l,
          List<MeanWithClusterProbAggregator> r)
focal_class.identifier: reduce
focal_class.modifiers: static
focal_class.parameters: (List<MeanWithClusterProbAggregator> l,
          List<MeanWithClusterProbAggregator> r)
focal_class.return: List<MeanWithClusterProbAggregator>
focal_class.signature: List<MeanWithClusterProbAggregator> reduce(List<MeanWithClusterProbAggregator> l,
          List<MeanWithClusterProbAggregator> r)
focal_method.body: void add(Vector x, double pcxi) {
          A.ensure(pcxi >= 0 && pcxi <= 1., "pcxi >= 0 && pcxi <= 1.");
  
          Vector weightedVector = x.times(pcxi);
          if (weightedXsSum == null)
              weightedXsSum = weightedVector;
          else
              weightedXsSum = weightedXsSum.plus(weightedVector);
  
          pcxiSum += pcxi;
          rowCount += 1;
      }
focal_method.class_method_signature: MeanWithClusterProbAggregator.add(Vector x, double pcxi)
focal_method.full_signature:  void add(Vector x, double pcxi)
focal_method.identifier: add
focal_method.invocations: ensure times plus
focal_method.parameters: (Vector x, double pcxi)
focal_method.return: void
focal_method.signature: void add(Vector x, double pcxi)