test_class.file: modules/ml/src/test/java/org/apache/ignite/ml/clustering/gmm/CovarianceMatricesAggregatorTest.java
test_class.identifier: CovarianceMatricesAggregatorTest
test_case.body: @Test
      public void testPlus() {
          Vector mean = VectorUtils.of(1, 0);
  
          CovarianceMatricesAggregator agg1 = new CovarianceMatricesAggregator(mean, identity(2), 1);
          CovarianceMatricesAggregator agg2 = new CovarianceMatricesAggregator(mean, identity(2).times(2), 3);
          CovarianceMatricesAggregator res = agg1.plus(agg2);
  
          assertArrayEquals(mean.asArray(), res.mean().asArray(), 1e-4);
          assertArrayEquals(identity(2).times(3).getStorage().data(), res.weightedSum().getStorage().data(), 1e-4);
          assertEquals(4, res.rowCount());
      }
test_case.class_method_signature: CovarianceMatricesAggregatorTest.testPlus()
test_case.full_signature: @Test public void testPlus()
test_case.identifier: testPlus
test_case.invocations: of identity times identity plus assertArrayEquals asArray asArray mean assertArrayEquals data getStorage times identity data getStorage weightedSum assertEquals rowCount
test_case.modifiers: @Test public
test_case.parameters: ()
test_case.return: void
test_case.signature: void testPlus()
focal_class.declarator: serialVersionUID = 4163253784526780812L
focal_class.modifier: private static final
focal_class.original_string: private static final long serialVersionUID = 4163253784526780812L;
focal_class.type: long
focal_class.var_name: serialVersionUID
focal_class.declarator: mean
focal_class.modifier: private final
focal_class.original_string: private final Vector mean;
focal_class.type: Vector
focal_class.var_name: mean
focal_class.declarator: weightedSum
focal_class.modifier: private
focal_class.original_string: private Matrix weightedSum;
focal_class.type: Matrix
focal_class.var_name: weightedSum
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/CovarianceMatricesAggregator.java
focal_class.identifier: CovarianceMatricesAggregator
focal_class.interfaces: implements Serializable
focal_class.class_method_signature: CovarianceMatricesAggregator.CovarianceMatricesAggregator(Vector mean)
focal_class.full_signature:   CovarianceMatricesAggregator(Vector mean)
focal_class.identifier: CovarianceMatricesAggregator
focal_class.parameters: (Vector mean)
focal_class.signature:  CovarianceMatricesAggregator(Vector mean)
focal_class.class_method_signature: CovarianceMatricesAggregator.CovarianceMatricesAggregator(Vector mean, Matrix weightedSum, int rowCount)
focal_class.full_signature:   CovarianceMatricesAggregator(Vector mean, Matrix weightedSum, int rowCount)
focal_class.identifier: CovarianceMatricesAggregator
focal_class.parameters: (Vector mean, Matrix weightedSum, int rowCount)
focal_class.signature:  CovarianceMatricesAggregator(Vector mean, Matrix weightedSum, int rowCount)
focal_class.class_method_signature: CovarianceMatricesAggregator.computeCovariances(Dataset<EmptyContext, GmmPartitionData> dataset,
          Vector clusterProbs, Vector[] means)
focal_class.full_signature: static List<Matrix> computeCovariances(Dataset<EmptyContext, GmmPartitionData> dataset,
          Vector clusterProbs, Vector[] means)
focal_class.identifier: computeCovariances
focal_class.modifiers: static
focal_class.parameters: (Dataset<EmptyContext, GmmPartitionData> dataset,
          Vector clusterProbs, Vector[] means)
focal_class.return: List<Matrix>
focal_class.signature: List<Matrix> computeCovariances(Dataset<EmptyContext, GmmPartitionData> dataset,
          Vector clusterProbs, Vector[] means)
focal_class.class_method_signature: CovarianceMatricesAggregator.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: CovarianceMatricesAggregator.plus(CovarianceMatricesAggregator other)
focal_class.full_signature:  CovarianceMatricesAggregator plus(CovarianceMatricesAggregator other)
focal_class.identifier: plus
focal_class.parameters: (CovarianceMatricesAggregator other)
focal_class.return: CovarianceMatricesAggregator
focal_class.signature: CovarianceMatricesAggregator plus(CovarianceMatricesAggregator other)
focal_class.class_method_signature: CovarianceMatricesAggregator.map(GmmPartitionData data, Vector[] means)
focal_class.full_signature: static List<CovarianceMatricesAggregator> map(GmmPartitionData data, Vector[] means)
focal_class.identifier: map
focal_class.modifiers: static
focal_class.parameters: (GmmPartitionData data, Vector[] means)
focal_class.return: List<CovarianceMatricesAggregator>
focal_class.signature: List<CovarianceMatricesAggregator> map(GmmPartitionData data, Vector[] means)
focal_class.class_method_signature: CovarianceMatricesAggregator.covariance(double clusterProb)
focal_class.full_signature: private Matrix covariance(double clusterProb)
focal_class.identifier: covariance
focal_class.modifiers: private
focal_class.parameters: (double clusterProb)
focal_class.return: Matrix
focal_class.signature: Matrix covariance(double clusterProb)
focal_class.class_method_signature: CovarianceMatricesAggregator.reduce(List<CovarianceMatricesAggregator> l,
          List<CovarianceMatricesAggregator> r)
focal_class.full_signature: static List<CovarianceMatricesAggregator> reduce(List<CovarianceMatricesAggregator> l,
          List<CovarianceMatricesAggregator> r)
focal_class.identifier: reduce
focal_class.modifiers: static
focal_class.parameters: (List<CovarianceMatricesAggregator> l,
          List<CovarianceMatricesAggregator> r)
focal_class.return: List<CovarianceMatricesAggregator>
focal_class.signature: List<CovarianceMatricesAggregator> reduce(List<CovarianceMatricesAggregator> l,
          List<CovarianceMatricesAggregator> r)
focal_class.class_method_signature: CovarianceMatricesAggregator.mean()
focal_class.full_signature:  Vector mean()
focal_class.identifier: mean
focal_class.parameters: ()
focal_class.return: Vector
focal_class.signature: Vector mean()
focal_class.class_method_signature: CovarianceMatricesAggregator.weightedSum()
focal_class.full_signature:  Matrix weightedSum()
focal_class.identifier: weightedSum
focal_class.parameters: ()
focal_class.return: Matrix
focal_class.signature: Matrix weightedSum()
focal_class.class_method_signature: CovarianceMatricesAggregator.rowCount()
focal_class.full_signature: public int rowCount()
focal_class.identifier: rowCount
focal_class.modifiers: public
focal_class.parameters: ()
focal_class.return: int
focal_class.signature: int rowCount()
focal_method.body: CovarianceMatricesAggregator plus(CovarianceMatricesAggregator other) {
          A.ensure(this.mean.equals(other.mean), "this.mean == other.mean");
  
          return new CovarianceMatricesAggregator(
              mean,
              this.weightedSum.plus(other.weightedSum),
              this.rowCount + other.rowCount
          );
      }
focal_method.class_method_signature: CovarianceMatricesAggregator.plus(CovarianceMatricesAggregator other)
focal_method.full_signature:  CovarianceMatricesAggregator plus(CovarianceMatricesAggregator other)
focal_method.identifier: plus
focal_method.invocations: ensure equals plus
focal_method.parameters: (CovarianceMatricesAggregator other)
focal_method.return: CovarianceMatricesAggregator
focal_method.signature: CovarianceMatricesAggregator plus(CovarianceMatricesAggregator other)