diff options
Diffstat (limited to 'test/de/lmu/ifi/dbs/elki/index/preprocessed/TestMaterializedKNNAndRKNNPreprocessor.java')
| -rw-r--r-- | test/de/lmu/ifi/dbs/elki/index/preprocessed/TestMaterializedKNNAndRKNNPreprocessor.java | 36 |
1 files changed, 18 insertions, 18 deletions
diff --git a/test/de/lmu/ifi/dbs/elki/index/preprocessed/TestMaterializedKNNAndRKNNPreprocessor.java b/test/de/lmu/ifi/dbs/elki/index/preprocessed/TestMaterializedKNNAndRKNNPreprocessor.java index 3d6e1ba7..68202213 100644 --- a/test/de/lmu/ifi/dbs/elki/index/preprocessed/TestMaterializedKNNAndRKNNPreprocessor.java +++ b/test/de/lmu/ifi/dbs/elki/index/preprocessed/TestMaterializedKNNAndRKNNPreprocessor.java @@ -42,6 +42,9 @@ import de.lmu.ifi.dbs.elki.database.UpdatableDatabase; import de.lmu.ifi.dbs.elki.database.ids.ArrayDBIDs; import de.lmu.ifi.dbs.elki.database.ids.DBIDUtil; import de.lmu.ifi.dbs.elki.database.ids.DBIDs; +import de.lmu.ifi.dbs.elki.database.ids.distance.DistanceDBIDList; +import de.lmu.ifi.dbs.elki.database.ids.distance.DistanceDBIDListIter; +import de.lmu.ifi.dbs.elki.database.ids.distance.KNNList; import de.lmu.ifi.dbs.elki.database.query.distance.DistanceQuery; import de.lmu.ifi.dbs.elki.database.query.knn.KNNQuery; import de.lmu.ifi.dbs.elki.database.query.knn.LinearScanKNNQuery; @@ -51,10 +54,7 @@ import de.lmu.ifi.dbs.elki.database.relation.Relation; import de.lmu.ifi.dbs.elki.database.relation.RelationUtil; import de.lmu.ifi.dbs.elki.datasource.FileBasedDatabaseConnection; import de.lmu.ifi.dbs.elki.datasource.bundle.MultipleObjectsBundle; -import de.lmu.ifi.dbs.elki.distance.distancefunction.EuclideanDistanceFunction; -import de.lmu.ifi.dbs.elki.distance.distanceresultlist.DistanceDBIDResult; -import de.lmu.ifi.dbs.elki.distance.distanceresultlist.DistanceDBIDResultIter; -import de.lmu.ifi.dbs.elki.distance.distanceresultlist.KNNResult; +import de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski.EuclideanDistanceFunction; import de.lmu.ifi.dbs.elki.distance.distancevalue.DoubleDistance; import de.lmu.ifi.dbs.elki.index.preprocessed.knn.MaterializeKNNAndRKNNPreprocessor; import de.lmu.ifi.dbs.elki.index.preprocessed.knn.MaterializeKNNPreprocessor; @@ -110,14 +110,14 @@ public class TestMaterializedKNNAndRKNNPreprocessor implements JUnit4Test { 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); + LinearScanKNNQuery<DoubleVector, DoubleDistance> lin_knn_query = new LinearScanKNNQuery<>(distanceQuery); + LinearScanRKNNQuery<DoubleVector, DoubleDistance> 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, DoubleDistance> preproc = new MaterializeKNNAndRKNNPreprocessor<DoubleVector, DoubleDistance>(rep, distanceQuery.getDistanceFunction(), k); + MaterializeKNNAndRKNNPreprocessor<DoubleVector, DoubleDistance> preproc = new MaterializeKNNAndRKNNPreprocessor<>(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 @@ -132,7 +132,7 @@ public class TestMaterializedKNNAndRKNNPreprocessor implements JUnit4Test { testKNNQueries(rep, lin_knn_query, preproc_knn_query, k / 2); // insert new objects - List<DoubleVector> insertions = new ArrayList<DoubleVector>(); + List<DoubleVector> insertions = new ArrayList<>(); NumberVector.Factory<DoubleVector, ?> o = RelationUtil.getNumberVectorFactory(rep); int dim = RelationUtil.dimensionality(rep); Random random = new Random(seed); @@ -159,12 +159,12 @@ public class TestMaterializedKNNAndRKNNPreprocessor implements JUnit4Test { private void testKNNQueries(Relation<DoubleVector> rep, KNNQuery<DoubleVector, DoubleDistance> lin_knn_query, KNNQuery<DoubleVector, DoubleDistance> preproc_knn_query, int k) { ArrayDBIDs sample = DBIDUtil.ensureArray(rep.getDBIDs()); - List<? extends KNNResult<DoubleDistance>> lin_knn_ids = lin_knn_query.getKNNForBulkDBIDs(sample, k); - List<? extends KNNResult<DoubleDistance>> preproc_knn_ids = preproc_knn_query.getKNNForBulkDBIDs(sample, k); + List<? extends KNNList<DoubleDistance>> lin_knn_ids = lin_knn_query.getKNNForBulkDBIDs(sample, k); + List<? extends KNNList<DoubleDistance>> preproc_knn_ids = preproc_knn_query.getKNNForBulkDBIDs(sample, k); for(int i = 0; i < rep.size(); i++) { - KNNResult<DoubleDistance> lin_knn = lin_knn_ids.get(i); - KNNResult<DoubleDistance> pre_knn = preproc_knn_ids.get(i); - DistanceDBIDResultIter<DoubleDistance> lin = lin_knn.iter(), pre = pre_knn.iter(); + KNNList<DoubleDistance> lin_knn = lin_knn_ids.get(i); + KNNList<DoubleDistance> pre_knn = preproc_knn_ids.get(i); + DistanceDBIDListIter<DoubleDistance> lin = lin_knn.iter(), pre = pre_knn.iter(); for(; lin.valid() && pre.valid(); lin.advance(), pre.advance(), i++) { if(!DBIDUtil.equal(lin, pre) && lin.getDistance().compareTo(pre.getDistance()) != 0) { System.out.print("LIN kNN #" + i + " " + lin.getDistancePair()); @@ -185,13 +185,13 @@ public class TestMaterializedKNNAndRKNNPreprocessor implements JUnit4Test { private void testRKNNQueries(Relation<DoubleVector> rep, RKNNQuery<DoubleVector, DoubleDistance> lin_rknn_query, RKNNQuery<DoubleVector, DoubleDistance> preproc_rknn_query, int k) { ArrayDBIDs sample = DBIDUtil.ensureArray(rep.getDBIDs()); - List<? extends DistanceDBIDResult<DoubleDistance>> lin_rknn_ids = lin_rknn_query.getRKNNForBulkDBIDs(sample, k); - List<? extends DistanceDBIDResult<DoubleDistance>> preproc_rknn_ids = preproc_rknn_query.getRKNNForBulkDBIDs(sample, k); + List<? extends DistanceDBIDList<DoubleDistance>> lin_rknn_ids = lin_rknn_query.getRKNNForBulkDBIDs(sample, k); + List<? extends DistanceDBIDList<DoubleDistance>> preproc_rknn_ids = preproc_rknn_query.getRKNNForBulkDBIDs(sample, k); for(int i = 0; i < rep.size(); i++) { - DistanceDBIDResult<DoubleDistance> lin_rknn = lin_rknn_ids.get(i); - DistanceDBIDResult<DoubleDistance> pre_rknn = preproc_rknn_ids.get(i); + DistanceDBIDList<DoubleDistance> lin_rknn = lin_rknn_ids.get(i); + DistanceDBIDList<DoubleDistance> pre_rknn = preproc_rknn_ids.get(i); - DistanceDBIDResultIter<DoubleDistance> lin = lin_rknn.iter(), pre = pre_rknn.iter(); + DistanceDBIDListIter<DoubleDistance> lin = lin_rknn.iter(), pre = pre_rknn.iter(); for(; lin.valid() && pre.valid(); lin.advance(), pre.advance(), i++) { if(!DBIDUtil.equal(lin, pre) || lin.getDistance().compareTo(pre.getDistance()) != 0) { System.out.print("LIN RkNN #" + i + " " + lin); |