test_class.file: modules/ml/src/test/java/org/apache/ignite/ml/clustering/gmm/GmmModelTest.java
test_class.identifier: GmmModelTest
test_case.body: @Test
      public void testTwoComponents() {
          Vector mean1 = VectorUtils.of(1., 2.);
          DenseMatrix covariance1 = MatrixUtil.fromList(Arrays.asList(
              VectorUtils.of(1, -0.25),
              VectorUtils.of(-0.25, 1)),
              true
          );
  
          Vector mean2 = VectorUtils.of(2., 1.);
          DenseMatrix covariance2 = MatrixUtil.fromList(Arrays.asList(
              VectorUtils.of(1, 0.5),
              VectorUtils.of(0.5, 1)),
              true
          );
  
          GmmModel gmm = new GmmModel(
              VectorUtils.of(0.5, 0.5),
              Arrays.asList(
                  new MultivariateGaussianDistribution(mean1, covariance1),
                  new MultivariateGaussianDistribution(mean2, covariance2)
              )
          );
  
          Assert.assertEquals(0., gmm.predict(mean1), 0.01);
          Assert.assertEquals(1., gmm.predict(mean2), 0.01);
          Assert.assertEquals(0., gmm.predict(VectorUtils.of(1.5, 1.5)), 0.01);
          Assert.assertEquals(1., gmm.predict(VectorUtils.of(3., 0.)), 0.01);
      }
test_case.class_method_signature: GmmModelTest.testTwoComponents()
test_case.full_signature: @Test public void testTwoComponents()
test_case.identifier: testTwoComponents
test_case.invocations: of fromList asList of of of fromList asList of of of asList assertEquals predict assertEquals predict assertEquals predict of assertEquals predict of
test_case.modifiers: @Test public
test_case.parameters: ()
test_case.return: void
test_case.signature: void testTwoComponents()
focal_class.declarator: serialVersionUID = -4484174539118240037L
focal_class.modifier: private static final
focal_class.original_string: private static final long serialVersionUID = -4484174539118240037L;
focal_class.type: long
focal_class.var_name: serialVersionUID
focal_class.file: modules/ml/src/main/java/org/apache/ignite/ml/clustering/gmm/GmmModel.java
focal_class.identifier: GmmModel
focal_class.interfaces: implements IgniteModel<Vector, Double>,
      DeployableObject
focal_class.class_method_signature: GmmModel.GmmModel(Vector componentProbs, List<MultivariateGaussianDistribution> distributions)
focal_class.full_signature: public  GmmModel(Vector componentProbs, List<MultivariateGaussianDistribution> distributions)
focal_class.identifier: GmmModel
focal_class.modifiers: public
focal_class.parameters: (Vector componentProbs, List<MultivariateGaussianDistribution> distributions)
focal_class.signature:  GmmModel(Vector componentProbs, List<MultivariateGaussianDistribution> distributions)
focal_class.class_method_signature: GmmModel.predict(Vector input)
focal_class.full_signature: @Override public Double predict(Vector input)
focal_class.identifier: predict
focal_class.modifiers: @Override public
focal_class.parameters: (Vector input)
focal_class.return: Double
focal_class.signature: Double predict(Vector input)
focal_class.class_method_signature: GmmModel.getDependencies()
focal_class.full_signature: @Override public List<Object> getDependencies()
focal_class.identifier: getDependencies
focal_class.modifiers: @Override public
focal_class.parameters: ()
focal_class.return: List<Object>
focal_class.signature: List<Object> getDependencies()
focal_class.superclass: extends DistributionMixture<MultivariateGaussianDistribution>
focal_method.body: @Override public Double predict(Vector input) {
          return (double)likelihood(input).maxElement().index();
      }
focal_method.class_method_signature: GmmModel.predict(Vector input)
focal_method.full_signature: @Override public Double predict(Vector input)
focal_method.identifier: predict
focal_method.invocations: index maxElement likelihood
focal_method.modifiers: @Override public
focal_method.parameters: (Vector input)
focal_method.return: Double
focal_method.signature: Double predict(Vector input)