@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); }
public GaussianNaiveBayesTrainer setPriorProbabilities(double[] priorProbabilities) { resetSettings(); this.priorProbabilities = priorProbabilities.clone(); return this; }
GaussianNaiveBayesTrainer extends SingleLabelDatasetTrainer<GaussianNaiveBayesModel> { public GaussianNaiveBayesTrainer setPriorProbabilities(double[] priorProbabilities) { resetSettings(); this.priorProbabilities = priorProbabilities.clone(); return this; } }
GaussianNaiveBayesTrainer extends SingleLabelDatasetTrainer<GaussianNaiveBayesModel> { public GaussianNaiveBayesTrainer setPriorProbabilities(double[] priorProbabilities) { resetSettings(); this.priorProbabilities = priorProbabilities.clone(); return this; }  }
GaussianNaiveBayesTrainer extends SingleLabelDatasetTrainer<GaussianNaiveBayesModel> { public GaussianNaiveBayesTrainer setPriorProbabilities(double[] priorProbabilities) { resetSettings(); this.priorProbabilities = priorProbabilities.clone(); return this; }  @Override GaussianNaiveBayesModel fitWithInitializedDeployingContext(DatasetBuilder<K, V> datasetBuilder,
                                                        Preprocessor<K, V> extractor); @Override boolean isUpdateable(GaussianNaiveBayesModel mdl); @Override GaussianNaiveBayesTrainer withEnvironmentBuilder(LearningEnvironmentBuilder envBuilder); GaussianNaiveBayesTrainer withEquiprobableClasses(); GaussianNaiveBayesTrainer setPriorProbabilities(double[] priorProbabilities); GaussianNaiveBayesTrainer resetSettings(); }
GaussianNaiveBayesTrainer extends SingleLabelDatasetTrainer<GaussianNaiveBayesModel> { public GaussianNaiveBayesTrainer setPriorProbabilities(double[] priorProbabilities) { resetSettings(); this.priorProbabilities = priorProbabilities.clone(); return this; }  @Override GaussianNaiveBayesModel fitWithInitializedDeployingContext(DatasetBuilder<K, V> datasetBuilder,
                                                        Preprocessor<K, V> extractor); @Override boolean isUpdateable(GaussianNaiveBayesModel mdl); @Override GaussianNaiveBayesTrainer withEnvironmentBuilder(LearningEnvironmentBuilder envBuilder); GaussianNaiveBayesTrainer withEquiprobableClasses(); GaussianNaiveBayesTrainer setPriorProbabilities(double[] priorProbabilities); GaussianNaiveBayesTrainer resetSettings();  }