Package de.lmu.ifi.dbs.elki.database

Examples of de.lmu.ifi.dbs.elki.database.UpdatableDatabase

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   * @throws UnableToComplyException
   */
  @SuppressWarnings("unchecked")
  @Test
  public void testOnlineLOF() throws UnableToComplyException {
    UpdatableDatabase db = getDatabase();

    // 1. Run LOF
    LOF<DoubleVector, DoubleDistance> lof = new LOF<DoubleVector, DoubleDistance>(k, neighborhoodDistanceFunction, reachabilityDistanceFunction);
    OutlierResult result1 = lof.run(db);

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   */
  private static UpdatableDatabase getDatabase() {
    ListParameterization params = new ListParameterization();
    params.addParameter(FileBasedDatabaseConnection.INPUT_ID, dataset);

    UpdatableDatabase db = ClassGenericsUtil.parameterizeOrAbort(HashmapDatabase.class, params);
    params.failOnErrors();
    if(params.hasUnusedParameters()) {
      fail("Unused parameters: " + params.getRemainingParameters());
    }

    // get database
    db.initialize();
    return db;
  }
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   */
  protected void handleDelete() {
    if (!(database instanceof UpdatableDatabase)) {
      throw new UnsupportedOperationException("Database not updatable.");
    }
    UpdatableDatabase upd = (UpdatableDatabase) database;
    ModifiableDBIDs todel = DBIDUtil.newHashSet();
    ModifiableDBIDs remain = DBIDUtil.newHashSet(dbids);
    for(int row : table.getSelectedRows()) {
      final DBID id = dbids.get(row);
      todel.add(id);
      remain.remove(id);
    }
    // Unselect first ...
    context.setSelection(new DBIDSelection(remain));
    // Now delete them.
    for(DBID id : todel) {
      upd.delete(id);
    }
  }
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   * @throws UnableToComplyException
   */
  @SuppressWarnings("unchecked")
  @Test
  public void testOnlineLOF() throws UnableToComplyException {
    UpdatableDatabase db = getDatabase();

    // 1. Run LOF
    LOF<DoubleVector, DoubleDistance> lof = new LOF<DoubleVector, DoubleDistance>(k, neighborhoodDistanceFunction, reachabilityDistanceFunction);
    OutlierResult result1 = lof.run(db);

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   */
  private static UpdatableDatabase getDatabase() {
    ListParameterization params = new ListParameterization();
    params.addParameter(FileBasedDatabaseConnection.INPUT_ID, dataset);

    UpdatableDatabase db = ClassGenericsUtil.parameterizeOrAbort(HashmapDatabase.class, params);
    params.failOnErrors();
    if(params.hasUnusedParameters()) {
      fail("Unused parameters: " + params.getRemainingParameters());
    }

    // get database
    db.initialize();
    return db;
  }
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   */
  private BitSet determinePreferenceVectorByApriori(Relation<V> relation, ModifiableDBIDs[] neighborIDs, StringBuffer msg) throws ParameterException, UnableToComplyException {
    int dimensionality = neighborIDs.length;

    // database for apriori
    UpdatableDatabase apriori_db = new HashmapDatabase();
    SimpleTypeInformation<?> bitmeta = VectorFieldTypeInformation.get(BitVector.class, dimensionality);
    for(Iterator<DBID> it = relation.iterDBIDs(); it.hasNext();) {
      DBID id = it.next();
      Bit[] bits = new Bit[dimensionality];
      boolean allFalse = true;
      for(int d = 0; d < dimensionality; d++) {
        if(neighborIDs[d].contains(id)) {
          bits[d] = new Bit(true);
          allFalse = false;
        }
        else {
          bits[d] = new Bit(false);
        }
      }
      if(!allFalse) {
        SingleObjectBundle oaa = new SingleObjectBundle();
        oaa.append(bitmeta, new BitVector(bits));
        apriori_db.insert(oaa);
      }
    }
    APRIORI apriori = new APRIORI(minpts);
    AprioriResult aprioriResult = apriori.run(apriori_db);

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   */
  protected void handleDelete() {
    if (!(database instanceof UpdatableDatabase)) {
      throw new UnsupportedOperationException("Database not updatable.");
    }
    UpdatableDatabase upd = (UpdatableDatabase) database;
    ModifiableDBIDs todel = DBIDUtil.newHashSet();
    ModifiableDBIDs remain = DBIDUtil.newHashSet(dbids);
    for(int row : table.getSelectedRows()) {
      final DBID id = dbids.get(row);
      todel.add(id);
      remain.remove(id);
    }
    // Unselect first ...
    context.setSelection(new DBIDSelection(remain));
    // Now delete them.
    for(DBID id : todel) {
      upd.delete(id);
    }
  }
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  public void testPreprocessor() throws ParameterException, UnableToComplyException {
    ListParameterization params = new ListParameterization();
    params.addParameter(FileBasedDatabaseConnection.INPUT_ID, dataset);

    // get database
    UpdatableDatabase db = ClassGenericsUtil.parameterizeOrAbort(HashmapDatabase.class, params);
    db.initialize();
    Relation<DoubleVector> rep = db.getRelation(TypeUtil.DOUBLE_VECTOR_FIELD);
    DistanceQuery<DoubleVector, DoubleDistance> distanceQuery = db.getDistanceQuery(rep, EuclideanDistanceFunction.STATIC);

    // verify data set size.
    assertEquals("Data set size doesn't match parameters.", shoulds, rep.size());

    // get linear queries
    LinearScanKNNQuery<DoubleVector, DoubleDistance> lin_knn_query = new LinearScanKNNQuery<DoubleVector, DoubleDistance>(distanceQuery);
    LinearScanRKNNQuery<DoubleVector, DoubleDistance> lin_rknn_query = new LinearScanRKNNQuery<DoubleVector, DoubleDistance>(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, DoubleDistance> preproc = new MaterializeKNNAndRKNNPreprocessor<DoubleVector, DoubleDistance>(rep, distanceQuery.getDistanceFunction(), k);
    KNNQuery<DoubleVector, DoubleDistance> preproc_knn_query = preproc.getKNNQuery(distanceQuery, k);
    RKNNQuery<DoubleVector, DoubleDistance> preproc_rknn_query = preproc.getRKNNQuery(distanceQuery);
    // add as index
    db.addIndex(preproc);
    assertTrue("Preprocessor knn query class incorrect.", !(preproc_knn_query instanceof LinearScanKNNQuery));
    assertTrue("Preprocessor rknn query class incorrect.", !(preproc_rknn_query instanceof LinearScanKNNQuery));

    // 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<DoubleVector>();
    DoubleVector o = DatabaseUtil.assumeVectorField(rep).getFactory();
    Random random = new Random(seed);
    for(int i = 0; i < updatesize; i++) {
      DoubleVector obj = VectorUtil.randomVector(o, random);
      insertions.add(obj);
    }
    System.out.println("Insert " + insertions);
    System.out.println();
    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);
  }
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  public void testPreprocessor() throws ParameterException, UnableToComplyException {
    ListParameterization params = new ListParameterization();
    params.addParameter(FileBasedDatabaseConnection.INPUT_ID, dataset);

    // get database
    UpdatableDatabase db = ClassGenericsUtil.parameterizeOrAbort(HashmapDatabase.class, params);
    db.initialize();
    Relation<DoubleVector> rep = db.getRelation(TypeUtil.DOUBLE_VECTOR_FIELD);
    DistanceQuery<DoubleVector, DoubleDistance> distanceQuery = db.getDistanceQuery(rep, EuclideanDistanceFunction.STATIC);

    // verify data set size.
    assertEquals("Data set size doesn't match parameters.", shoulds, rep.size());

    // get linear queries
    LinearScanKNNQuery<DoubleVector, DoubleDistance> lin_knn_query = new LinearScanKNNQuery<DoubleVector, DoubleDistance>(distanceQuery);
    LinearScanRKNNQuery<DoubleVector, DoubleDistance> lin_rknn_query = new LinearScanRKNNQuery<DoubleVector, DoubleDistance>(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, DoubleDistance> preproc = new MaterializeKNNAndRKNNPreprocessor<DoubleVector, DoubleDistance>(rep, distanceQuery.getDistanceFunction(), k);
    KNNQuery<DoubleVector, DoubleDistance> preproc_knn_query = preproc.getKNNQuery(distanceQuery, k);
    RKNNQuery<DoubleVector, DoubleDistance> preproc_rknn_query = preproc.getRKNNQuery(distanceQuery);
    // add as index
    db.addIndex(preproc);
    assertTrue("Preprocessor knn query class incorrect.", !(preproc_knn_query instanceof LinearScanKNNQuery));
    assertTrue("Preprocessor rknn query class incorrect.", !(preproc_rknn_query instanceof LinearScanKNNQuery));

    // 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<DoubleVector>();
    DoubleVector o = DatabaseUtil.assumeVectorField(rep).getFactory();
    Random random = new Random(seed);
    for(int i = 0; i < updatesize; i++) {
      DoubleVector obj = VectorUtil.randomVector(o, random);
      insertions.add(obj);
    }
    System.out.println("Insert " + insertions);
    System.out.println();
    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);
  }
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Related Classes of de.lmu.ifi.dbs.elki.database.UpdatableDatabase

  • de.lmu.ifi.dbs.elki.algorithm.outlier.TestOnlineLOF
  • de.lmu.ifi.dbs.elki.index.preprocessed.preference.DiSHPreferenceVectorIndex
  • de.lmu.ifi.dbs.elki.index.preprocessed.TestMaterializedKNNAndRKNNPreprocessor
  • de.lmu.ifi.dbs.elki.visualization.gui.SelectionTableWindow
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