test_class.declarator: PRECISION = 1e-2
test_class.modifier: private static final
test_class.original_string: private static final double PRECISION = 1e-2;
test_class.type: double
test_class.var_name: PRECISION
test_class.declarator: LABEL_1 = 1.
test_class.modifier: private static final
test_class.original_string: private static final double LABEL_1 = 1.;
test_class.type: double
test_class.var_name: LABEL_1
test_class.declarator: LABEL_2 = 2.
test_class.modifier: private static final
test_class.original_string: private static final double LABEL_2 = 2.;
test_class.type: double
test_class.var_name: LABEL_2
test_class.declarator: data = new HashMap<>()
test_class.modifier: private static final
test_class.original_string: private static final Map<Integer, double[]> data = new HashMap<>();
test_class.type: Map<Integer, double[]>
test_class.var_name: data
test_class.declarator: singleLabeldata1 = new HashMap<>()
test_class.modifier: private static final
test_class.original_string: private static final Map<Integer, double[]> singleLabeldata1 = new HashMap<>();
test_class.type: Map<Integer, double[]>
test_class.var_name: singleLabeldata1
test_class.declarator: singleLabeldata2 = new HashMap<>()
test_class.modifier: private static final
test_class.original_string: private static final Map<Integer, double[]> singleLabeldata2 = new HashMap<>();
test_class.type: Map<Integer, double[]>
test_class.var_name: singleLabeldata2
test_class.declarator: trainer
test_class.modifier: private
test_class.original_string: private GaussianNaiveBayesTrainer trainer;
test_class.type: GaussianNaiveBayesTrainer
test_class.var_name: trainer
test_class.file: modules/ml/src/test/java/org/apache/ignite/ml/naivebayes/gaussian/GaussianNaiveBayesTrainerTest.java
test_class.identifier: GaussianNaiveBayesTrainerTest
test_class.superclass: extends TrainerTest
test_case.body: @Test
      public void testReturnsPresetProbalitiesWhenSetPriorProbabilities() {
          double[] priorProbabilities = new double[] {.35, .65};
          GaussianNaiveBayesTrainer trainer = new GaussianNaiveBayesTrainer()
              .setPriorProbabilities(priorProbabilities);
  
          GaussianNaiveBayesModel model = trainer.fit(
              new LocalDatasetBuilder<>(data, parts),
              new DoubleArrayVectorizer<Integer>().labeled(Vectorizer.LabelCoordinate.LAST)
          );
  
          Assert.assertEquals(priorProbabilities[0], model.getClassProbabilities()[0], PRECISION);
          Assert.assertEquals(priorProbabilities[1], model.getClassProbabilities()[1], PRECISION);
      }
test_case.class_method_signature: GaussianNaiveBayesTrainerTest.testReturnsPresetProbalitiesWhenSetPriorProbabilities()
test_case.full_signature: @Test public void testReturnsPresetProbalitiesWhenSetPriorProbabilities()
test_case.identifier: testReturnsPresetProbalitiesWhenSetPriorProbabilities
test_case.invocations: setPriorProbabilities fit labeled assertEquals getClassProbabilities assertEquals getClassProbabilities
test_case.modifiers: @Test public
test_case.parameters: ()
test_case.return: void
test_case.signature: void testReturnsPresetProbalitiesWhenSetPriorProbabilities()
focal_class.declarator: priorProbabilities
focal_class.modifier: private
focal_class.original_string: private double[] priorProbabilities;
focal_class.type: double[]
focal_class.var_name: priorProbabilities
focal_class.declarator: equiprobableClasses
focal_class.modifier: private
focal_class.original_string: private boolean equiprobableClasses;
focal_class.type: boolean
focal_class.var_name: equiprobableClasses
focal_class.file: modules/ml/src/main/java/org/apache/ignite/ml/naivebayes/gaussian/GaussianNaiveBayesTrainer.java
focal_class.identifier: GaussianNaiveBayesTrainer
focal_class.class_method_signature: GaussianNaiveBayesTrainer.fitWithInitializedDeployingContext(DatasetBuilder<K, V> datasetBuilder,
                                                          Preprocessor<K, V> extractor)
focal_class.full_signature: @Override public GaussianNaiveBayesModel fitWithInitializedDeployingContext(DatasetBuilder<K, V> datasetBuilder,
                                                          Preprocessor<K, V> extractor)
focal_class.identifier: fitWithInitializedDeployingContext
focal_class.modifiers: @Override public
focal_class.parameters: (DatasetBuilder<K, V> datasetBuilder,
                                                          Preprocessor<K, V> extractor)
focal_class.return: GaussianNaiveBayesModel
focal_class.signature: GaussianNaiveBayesModel fitWithInitializedDeployingContext(DatasetBuilder<K, V> datasetBuilder,
                                                          Preprocessor<K, V> extractor)
focal_class.class_method_signature: GaussianNaiveBayesTrainer.isUpdateable(GaussianNaiveBayesModel mdl)
focal_class.full_signature: @Override public boolean isUpdateable(GaussianNaiveBayesModel mdl)
focal_class.identifier: isUpdateable
focal_class.modifiers: @Override public
focal_class.parameters: (GaussianNaiveBayesModel mdl)
focal_class.return: boolean
focal_class.signature: boolean isUpdateable(GaussianNaiveBayesModel mdl)
focal_class.class_method_signature: GaussianNaiveBayesTrainer.withEnvironmentBuilder(LearningEnvironmentBuilder envBuilder)
focal_class.full_signature: @Override public GaussianNaiveBayesTrainer withEnvironmentBuilder(LearningEnvironmentBuilder envBuilder)
focal_class.identifier: withEnvironmentBuilder
focal_class.modifiers: @Override public
focal_class.parameters: (LearningEnvironmentBuilder envBuilder)
focal_class.return: GaussianNaiveBayesTrainer
focal_class.signature: GaussianNaiveBayesTrainer withEnvironmentBuilder(LearningEnvironmentBuilder envBuilder)
focal_class.class_method_signature: GaussianNaiveBayesTrainer.updateModel(GaussianNaiveBayesModel mdl,
                                                                     DatasetBuilder<K, V> datasetBuilder, Preprocessor<K, V> extractor)
focal_class.full_signature: @Override protected GaussianNaiveBayesModel updateModel(GaussianNaiveBayesModel mdl,
                                                                     DatasetBuilder<K, V> datasetBuilder, Preprocessor<K, V> extractor)
focal_class.identifier: updateModel
focal_class.modifiers: @Override protected
focal_class.parameters: (GaussianNaiveBayesModel mdl,
                                                                     DatasetBuilder<K, V> datasetBuilder, Preprocessor<K, V> extractor)
focal_class.return: GaussianNaiveBayesModel
focal_class.signature: GaussianNaiveBayesModel updateModel(GaussianNaiveBayesModel mdl,
                                                                     DatasetBuilder<K, V> datasetBuilder, Preprocessor<K, V> extractor)
focal_class.class_method_signature: GaussianNaiveBayesTrainer.withEquiprobableClasses()
focal_class.full_signature: public GaussianNaiveBayesTrainer withEquiprobableClasses()
focal_class.identifier: withEquiprobableClasses
focal_class.modifiers: public
focal_class.parameters: ()
focal_class.return: GaussianNaiveBayesTrainer
focal_class.signature: GaussianNaiveBayesTrainer withEquiprobableClasses()
focal_class.class_method_signature: GaussianNaiveBayesTrainer.setPriorProbabilities(double[] priorProbabilities)
focal_class.full_signature: public GaussianNaiveBayesTrainer setPriorProbabilities(double[] priorProbabilities)
focal_class.identifier: setPriorProbabilities
focal_class.modifiers: public
focal_class.parameters: (double[] priorProbabilities)
focal_class.return: GaussianNaiveBayesTrainer
focal_class.signature: GaussianNaiveBayesTrainer setPriorProbabilities(double[] priorProbabilities)
focal_class.class_method_signature: GaussianNaiveBayesTrainer.resetSettings()
focal_class.full_signature: public GaussianNaiveBayesTrainer resetSettings()
focal_class.identifier: resetSettings
focal_class.modifiers: public
focal_class.parameters: ()
focal_class.return: GaussianNaiveBayesTrainer
focal_class.signature: GaussianNaiveBayesTrainer resetSettings()
focal_class.superclass: extends SingleLabelDatasetTrainer<GaussianNaiveBayesModel>
focal_method.body: public GaussianNaiveBayesTrainer setPriorProbabilities(double[] priorProbabilities) {
          resetSettings();
          this.priorProbabilities = priorProbabilities.clone();
          return this;
      }
focal_method.class_method_signature: GaussianNaiveBayesTrainer.setPriorProbabilities(double[] priorProbabilities)
focal_method.full_signature: public GaussianNaiveBayesTrainer setPriorProbabilities(double[] priorProbabilities)
focal_method.identifier: setPriorProbabilities
focal_method.invocations: resetSettings clone
focal_method.modifiers: public
focal_method.parameters: (double[] priorProbabilities)
focal_method.return: GaussianNaiveBayesTrainer
focal_method.signature: GaussianNaiveBayesTrainer setPriorProbabilities(double[] priorProbabilities)