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Example 1 with LatLngDistanceFunction

use of de.lmu.ifi.dbs.elki.distance.distancefunction.geo.LatLngDistanceFunction in project elki by elki-project.

the class GeoIndexing method main.

public static void main(String[] args) {
    // Set the logging level to statistics:
    LoggingConfiguration.setStatistics();
    // Generate a random data set.
    Random rand = new Random(0L);
    // Note: ELKI has a nice data generator class, use that instead.
    double[][] data = new double[100000][];
    for (int i = 0; i < data.length; i++) {
        data[i] = randomLatitudeLongitude(rand);
    }
    // Adapter to load data from an existing array.
    DatabaseConnection dbc = new ArrayAdapterDatabaseConnection(data);
    // Since the R-tree has so many options, it is a bit easier to configure it
    // using the parameterization API, which handles defaults, instantiation,
    // and additional constraint checks.
    RStarTreeFactory<?> indexfactory = // 
    new ELKIBuilder<>(RStarTreeFactory.class).with(AbstractPageFileFactory.Parameterizer.PAGE_SIZE_ID, // 
    512).with(RStarTreeFactory.Parameterizer.BULK_SPLIT_ID, // 
    SortTileRecursiveBulkSplit.class).build();
    // Create the database, and initialize it.
    Database db = new StaticArrayDatabase(dbc, Arrays.asList(indexfactory));
    // This will build the index of the database.
    db.initialize();
    // Relation containing the number vectors we put in above:
    Relation<NumberVector> rel = db.getRelation(TypeUtil.NUMBER_VECTOR_FIELD);
    // We can use this to identify rows of the input data below.
    DBIDRange ids = (DBIDRange) rel.getDBIDs();
    // For all indexes, dump their statistics.
    for (It<Index> it = db.getHierarchy().iterDescendants(db).filter(Index.class); it.valid(); it.advance()) {
        it.get().logStatistics();
    }
    // We use the WGS84 earth model, and "latitude, longitude" coordinates:
    // This distance function returns meters.
    LatLngDistanceFunction df = new LatLngDistanceFunction(WGS84SpheroidEarthModel.STATIC);
    // k nearest neighbor query:
    KNNQuery<NumberVector> knnq = QueryUtil.getKNNQuery(rel, df);
    // Let's find the closest points to New York:
    DoubleVector newYork = DoubleVector.wrap(new double[] { 40.730610, -73.935242 });
    KNNList knns = knnq.getKNNForObject(newYork, 10);
    // Iterate over all results.
    System.out.println("Close to New York:");
    for (DoubleDBIDListIter it = knns.iter(); it.valid(); it.advance()) {
        // To kilometers
        double km = it.doubleValue() / 1000;
        System.out.println(rel.get(it) + " distance: " + km + " km row: " + ids.getOffset(it));
    }
    // Many other indexes will fail if we search close to the date line.
    DoubleVector tuvalu = DoubleVector.wrap(new double[] { -7.4784205, 178.679924 });
    knns = knnq.getKNNForObject(tuvalu, 10);
    // Iterate over all results.
    System.out.println("Close to Tuvalu:");
    for (DoubleDBIDListIter it = knns.iter(); it.valid(); it.advance()) {
        // To kilometers
        double km = it.doubleValue() / 1000;
        System.out.println(rel.get(it) + " distance: " + km + " km row: " + ids.getOffset(it));
    }
    // the distances to a few points in the data set.
    for (It<Index> it = db.getHierarchy().iterDescendants(db).filter(Index.class); it.valid(); it.advance()) {
        it.get().logStatistics();
    }
}
Also used : DoubleDBIDListIter(de.lmu.ifi.dbs.elki.database.ids.DoubleDBIDListIter) ArrayAdapterDatabaseConnection(de.lmu.ifi.dbs.elki.datasource.ArrayAdapterDatabaseConnection) SortTileRecursiveBulkSplit(de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.strategies.bulk.SortTileRecursiveBulkSplit) Index(de.lmu.ifi.dbs.elki.index.Index) LatLngDistanceFunction(de.lmu.ifi.dbs.elki.distance.distancefunction.geo.LatLngDistanceFunction) Random(java.util.Random) NumberVector(de.lmu.ifi.dbs.elki.data.NumberVector) KNNList(de.lmu.ifi.dbs.elki.database.ids.KNNList) Database(de.lmu.ifi.dbs.elki.database.Database) StaticArrayDatabase(de.lmu.ifi.dbs.elki.database.StaticArrayDatabase) DBIDRange(de.lmu.ifi.dbs.elki.database.ids.DBIDRange) ArrayAdapterDatabaseConnection(de.lmu.ifi.dbs.elki.datasource.ArrayAdapterDatabaseConnection) DatabaseConnection(de.lmu.ifi.dbs.elki.datasource.DatabaseConnection) DoubleVector(de.lmu.ifi.dbs.elki.data.DoubleVector) RStarTreeFactory(de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.rstar.RStarTreeFactory) StaticArrayDatabase(de.lmu.ifi.dbs.elki.database.StaticArrayDatabase)

Aggregations

DoubleVector (de.lmu.ifi.dbs.elki.data.DoubleVector)1 NumberVector (de.lmu.ifi.dbs.elki.data.NumberVector)1 Database (de.lmu.ifi.dbs.elki.database.Database)1 StaticArrayDatabase (de.lmu.ifi.dbs.elki.database.StaticArrayDatabase)1 DBIDRange (de.lmu.ifi.dbs.elki.database.ids.DBIDRange)1 DoubleDBIDListIter (de.lmu.ifi.dbs.elki.database.ids.DoubleDBIDListIter)1 KNNList (de.lmu.ifi.dbs.elki.database.ids.KNNList)1 ArrayAdapterDatabaseConnection (de.lmu.ifi.dbs.elki.datasource.ArrayAdapterDatabaseConnection)1 DatabaseConnection (de.lmu.ifi.dbs.elki.datasource.DatabaseConnection)1 LatLngDistanceFunction (de.lmu.ifi.dbs.elki.distance.distancefunction.geo.LatLngDistanceFunction)1 Index (de.lmu.ifi.dbs.elki.index.Index)1 RStarTreeFactory (de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.rstar.RStarTreeFactory)1 SortTileRecursiveBulkSplit (de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.strategies.bulk.SortTileRecursiveBulkSplit)1 Random (java.util.Random)1