### Question:
KnnClassifier implements Classifier<KnnModel> { @Override public CategoryEntries classify(FeatureVector featureVector, KnnModel model) { CategoryEntriesBuilder builder = new CategoryEntriesBuilder().set(model.getCategories(), 0); double[] numericVector = model.getNormalizedVectorForClassification(featureVector); FixedSizePriorityQueue<Pair<String, Double>> neighbors = new FixedSizePriorityQueue<>(k, new EntryValueComparator<Double>(Order.DESCENDING)); for (TrainingExample example : model.getTrainingExamples()) { double distance = example.distance(numericVector); neighbors.add(Pair.of(example.category, distance)); } for (Pair<String, Double> neighbor : neighbors.asList()) { double distance = neighbor.getValue(); double weight = 1.0 / (distance + 0.000000001); String targetClass = neighbor.getKey(); builder.add(targetClass, weight); } return builder.create(); } KnnClassifier(int k);  KnnClassifier(); @Override CategoryEntries classify(FeatureVector featureVector, KnnModel model); @Override String toString();  }### Answer:
@Test public void testKnnClassifierSerialization() throws Exception { KnnLearner knnLearner = new KnnLearner(); Dataset instances = CsvDatasetReaderConfig.filePath(getResourceFile("/classifier/wineData.csv")).create(); KnnModel model = knnLearner.train(instances); File tempDir = FileHelper.getTempDir(); String tempFile = new File(tempDir, "/testKNN.gz").getPath(); FileHelper.serialize(model, tempFile); KnnModel loadedModel = FileHelper.deserialize(tempFile); CategoryEntries result = new KnnClassifier(3).classify(createTestInstance(), loadedModel); assertEquals(1, result.getProbability(result.getMostLikelyCategory()), 0); assertEquals("1", result.getMostLikelyCategory()); }