Examples with MaterializeKNNAndRKNNPreprocessor de.lmu.ifi.dbs.elki.index.preprocessed.knn.MaterializeKNNAndRKNNPreprocessor used on opensource projects

Example 1 with MaterializeKNNAndRKNNPreprocessor

use of de.lmu.ifi.dbs.elki.index.preprocessed.knn.MaterializeKNNAndRKNNPreprocessor in project elki by elki-project.

the class OnlineLOF method getKNNAndRkNNQueries.

/**
 * Get the kNN and rkNN queries for the algorithm.
 *
 * @param relation Data
 * @param stepprog Progress logger
 * @return the kNN and rkNN queries
 */
private Pair<Pair<KNNQuery<O>, KNNQuery<O>>, Pair<RKNNQuery<O>, RKNNQuery<O>>> getKNNAndRkNNQueries(Database database, Relation<O> relation, StepProgress stepprog) {
    DistanceQuery<O> drefQ = database.getDistanceQuery(relation, referenceDistanceFunction);
    // Use "HEAVY" flag, since this is an online algorithm
    KNNQuery<O> kNNRefer = database.getKNNQuery(drefQ, krefer, DatabaseQuery.HINT_HEAVY_USE, DatabaseQuery.HINT_OPTIMIZED_ONLY, DatabaseQuery.HINT_NO_CACHE);
    RKNNQuery<O> rkNNRefer = database.getRKNNQuery(drefQ, DatabaseQuery.HINT_HEAVY_USE, DatabaseQuery.HINT_OPTIMIZED_ONLY, DatabaseQuery.HINT_NO_CACHE);
    // No optimized kNN query or RkNN query - use a preprocessor!
    if (kNNRefer == null || rkNNRefer == null) {
        if (stepprog != null) {
            stepprog.beginStep(1, "Materializing neighborhood w.r.t. reference neighborhood distance function.", LOG);
        }
        MaterializeKNNAndRKNNPreprocessor<O> preproc = new MaterializeKNNAndRKNNPreprocessor<>(relation, referenceDistanceFunction, krefer);
        kNNRefer = preproc.getKNNQuery(drefQ, krefer, DatabaseQuery.HINT_HEAVY_USE);
        rkNNRefer = preproc.getRKNNQuery(drefQ, krefer, DatabaseQuery.HINT_HEAVY_USE);
        // add as index
        database.getHierarchy().add(relation, preproc);
    } else {
        if (stepprog != null) {
            stepprog.beginStep(1, "Optimized neighborhood w.r.t. reference neighborhood distance function provided by database.", LOG);
        }
    }
    DistanceQuery<O> dreachQ = database.getDistanceQuery(relation, reachabilityDistanceFunction);
    KNNQuery<O> kNNReach = database.getKNNQuery(dreachQ, kreach, DatabaseQuery.HINT_HEAVY_USE, DatabaseQuery.HINT_OPTIMIZED_ONLY, DatabaseQuery.HINT_NO_CACHE);
    RKNNQuery<O> rkNNReach = database.getRKNNQuery(dreachQ, DatabaseQuery.HINT_HEAVY_USE, DatabaseQuery.HINT_OPTIMIZED_ONLY, DatabaseQuery.HINT_NO_CACHE);
    if (kNNReach == null || rkNNReach == null) {
        if (stepprog != null) {
            stepprog.beginStep(2, "Materializing neighborhood w.r.t. reachability distance function.", LOG);
        }
        ListParameterization config = new ListParameterization();
        config.addParameter(AbstractMaterializeKNNPreprocessor.Factory.DISTANCE_FUNCTION_ID, reachabilityDistanceFunction);
        config.addParameter(AbstractMaterializeKNNPreprocessor.Factory.K_ID, kreach);
        MaterializeKNNAndRKNNPreprocessor<O> preproc = new MaterializeKNNAndRKNNPreprocessor<>(relation, reachabilityDistanceFunction, kreach);
        kNNReach = preproc.getKNNQuery(dreachQ, kreach, DatabaseQuery.HINT_HEAVY_USE);
        rkNNReach = preproc.getRKNNQuery(dreachQ, kreach, DatabaseQuery.HINT_HEAVY_USE);
        // add as index
        database.getHierarchy().add(relation, preproc);
    }
    Pair<KNNQuery<O>, KNNQuery<O>> kNNPair = new Pair<>(kNNRefer, kNNReach);
    Pair<RKNNQuery<O>, RKNNQuery<O>> rkNNPair = new Pair<>(rkNNRefer, rkNNReach);
    return new Pair<>(kNNPair, rkNNPair);
}

Example 2 with MaterializeKNNAndRKNNPreprocessor

use of de.lmu.ifi.dbs.elki.index.preprocessed.knn.MaterializeKNNAndRKNNPreprocessor in project elki by elki-project.

the class MaterializedKNNAndRKNNPreprocessorTest method testPreprocessor.

@Test
public void testPreprocessor() {
    UpdatableDatabase db;
    // get database
    try (InputStream is = AbstractSimpleAlgorithmTest.open(dataset)) {
        ListParameterization params = new ListParameterization();
        // Setup parser and data loading
        NumberVectorLabelParser<DoubleVector> parser = new NumberVectorLabelParser<>(DoubleVector.FACTORY);
        InputStreamDatabaseConnection dbc = new InputStreamDatabaseConnection(is, new ArrayList<>(), parser);
        // We want to allow the use of indexes via "params"
        params.addParameter(AbstractDatabase.Parameterizer.DATABASE_CONNECTION_ID, dbc);
        db = ClassGenericsUtil.parameterizeOrAbort(HashmapDatabase.class, params);
        db.initialize();
    } catch (IOException e) {
        fail("Test data " + dataset + " not found.");
        return;
    }
    Relation<DoubleVector> rep = db.getRelation(TypeUtil.DOUBLE_VECTOR_FIELD);
    DistanceQuery<DoubleVector> distanceQuery = db.getDistanceQuery(rep, EuclideanDistanceFunction.STATIC);
    // verify data set size.
    assertEquals("Data set size doesn't match parameters.", shoulds, rep.size());
    // get linear queries
    LinearScanDistanceKNNQuery<DoubleVector> lin_knn_query = new LinearScanDistanceKNNQuery<>(distanceQuery);
    LinearScanRKNNQuery<DoubleVector> lin_rknn_query = new LinearScanRKNNQuery<>(distanceQuery, lin_knn_query, k);
    // get preprocessed queries
    ListParameterization config = new ListParameterization();
    config.addParameter(MaterializeKNNPreprocessor.Factory.DISTANCE_FUNCTION_ID, distanceQuery.getDistanceFunction());
    config.addParameter(MaterializeKNNPreprocessor.Factory.K_ID, k);
    MaterializeKNNAndRKNNPreprocessor<DoubleVector> preproc = new MaterializeKNNAndRKNNPreprocessor<>(rep, distanceQuery.getDistanceFunction(), k);
    KNNQuery<DoubleVector> preproc_knn_query = preproc.getKNNQuery(distanceQuery, k);
    RKNNQuery<DoubleVector> preproc_rknn_query = preproc.getRKNNQuery(distanceQuery);
    // add as index
    db.getHierarchy().add(rep, preproc);
    assertFalse("Preprocessor knn query class incorrect.", preproc_knn_query instanceof LinearScanDistanceKNNQuery);
    assertFalse("Preprocessor rknn query class incorrect.", preproc_rknn_query instanceof LinearScanDistanceKNNQuery);
    // test queries
    testKNNQueries(rep, lin_knn_query, preproc_knn_query, k);
    testRKNNQueries(rep, lin_rknn_query, preproc_rknn_query, k);
    // also test partial queries, forward only
    testKNNQueries(rep, lin_knn_query, preproc_knn_query, k / 2);
    // insert new objects
    List<DoubleVector> insertions = new ArrayList<>();
    NumberVector.Factory<DoubleVector> o = RelationUtil.getNumberVectorFactory(rep);
    int dim = RelationUtil.dimensionality(rep);
    Random random = new Random(seed);
    for (int i = 0; i < updatesize; i++) {
        DoubleVector obj = VectorUtil.randomVector(o, dim, random);
        insertions.add(obj);
    }
    // System.out.println("Insert " + insertions);
    DBIDs deletions = db.insert(MultipleObjectsBundle.makeSimple(rep.getDataTypeInformation(), insertions));
    // test queries
    testKNNQueries(rep, lin_knn_query, preproc_knn_query, k);
    testRKNNQueries(rep, lin_rknn_query, preproc_rknn_query, k);
    // delete objects
    // System.out.println("Delete " + deletions);
    db.delete(deletions);
    // test queries
    testKNNQueries(rep, lin_knn_query, preproc_knn_query, k);
    testRKNNQueries(rep, lin_rknn_query, preproc_rknn_query, k);
}