1 files changed, 35 insertions, 20 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 0ef07a69..3d6e1ba7 100644
--- a/test/de/lmu/ifi/dbs/elki/index/preprocessed/TestMaterializedKNNAndRKNNPreprocessor.java
+++ b/test/de/lmu/ifi/dbs/elki/index/preprocessed/TestMaterializedKNNAndRKNNPreprocessor.java
@@ -34,6 +34,7 @@ import org.junit.Test;
 
 import de.lmu.ifi.dbs.elki.JUnit4Test;
 import de.lmu.ifi.dbs.elki.data.DoubleVector;
+import de.lmu.ifi.dbs.elki.data.NumberVector;
 import de.lmu.ifi.dbs.elki.data.VectorUtil;
 import de.lmu.ifi.dbs.elki.data.type.TypeUtil;
 import de.lmu.ifi.dbs.elki.database.HashmapDatabase;
@@ -41,22 +42,23 @@ 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.query.DistanceResultPair;
 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.KNNResult;
 import de.lmu.ifi.dbs.elki.database.query.knn.LinearScanKNNQuery;
 import de.lmu.ifi.dbs.elki.database.query.rknn.LinearScanRKNNQuery;
 import de.lmu.ifi.dbs.elki.database.query.rknn.RKNNQuery;
 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.distancevalue.DoubleDistance;
 import de.lmu.ifi.dbs.elki.index.preprocessed.knn.MaterializeKNNAndRKNNPreprocessor;
 import de.lmu.ifi.dbs.elki.index.preprocessed.knn.MaterializeKNNPreprocessor;
 import de.lmu.ifi.dbs.elki.utilities.ClassGenericsUtil;
-import de.lmu.ifi.dbs.elki.utilities.DatabaseUtil;
 import de.lmu.ifi.dbs.elki.utilities.exceptions.UnableToComplyException;
 import de.lmu.ifi.dbs.elki.utilities.optionhandling.ParameterException;
 import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameterization.ListParameterization;
@@ -131,10 +133,11 @@ public class TestMaterializedKNNAndRKNNPreprocessor implements JUnit4Test {
 
     // insert new objects
     List<DoubleVector> insertions = new ArrayList<DoubleVector>();
-    DoubleVector o = DatabaseUtil.assumeVectorField(rep).getFactory();
+    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, random);
+      DoubleVector obj = VectorUtil.randomVector(o, dim, random);
       insertions.add(obj);
     }
     System.out.println("Insert " + insertions);
@@ -156,18 +159,24 @@ 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<KNNResult<DoubleDistance>> lin_knn_ids = lin_knn_query.getKNNForBulkDBIDs(sample, k);
-    List<KNNResult<DoubleDistance>> preproc_knn_ids = preproc_knn_query.getKNNForBulkDBIDs(sample, k);
+    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);
     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);
-      if(!lin_knn.equals(pre_knn)) {
-        System.out.println("LIN kNN " + lin_knn);
-        System.out.println("PRE kNN " + pre_knn);
+      DistanceDBIDResultIter<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());
+          System.out.print(" <=> ");
+          System.out.print("PRE kNN #" + i + " " + pre.getDistancePair());
+          System.out.println();
+          break;
+        }
       }
       assertEquals("kNN sizes do not agree.", lin_knn.size(), pre_knn.size());
       for(int j = 0; j < lin_knn.size(); j++) {
-        assertTrue("kNNs of linear scan and preprocessor do not match!", lin_knn.get(j).sameDBID(pre_knn.get(j)));
+        assertTrue("kNNs of linear scan and preprocessor do not match!", DBIDUtil.equal(lin_knn.get(j), pre_knn.get(j)));
         assertTrue("kNNs of linear scan and preprocessor do not match!", lin_knn.get(j).getDistance().equals(pre_knn.get(j).getDistance()));
       }
     }
@@ -176,19 +185,25 @@ 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<List<DistanceResultPair<DoubleDistance>>> lin_rknn_ids = lin_rknn_query.getRKNNForBulkDBIDs(sample, k);
-    List<List<DistanceResultPair<DoubleDistance>>> preproc_rknn_ids = preproc_rknn_query.getRKNNForBulkDBIDs(sample, k);
+    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);
     for(int i = 0; i < rep.size(); i++) {
-      List<DistanceResultPair<DoubleDistance>> lin_rknn = lin_rknn_ids.get(i);
-      List<DistanceResultPair<DoubleDistance>> pre_rknn = preproc_rknn_ids.get(i);
-      if(!lin_rknn.equals(pre_rknn)) {
-        System.out.println("LIN RkNN " + lin_rknn);
-        System.out.println("PRE RkNN " + pre_rknn);
-        System.out.println();
+      DistanceDBIDResult<DoubleDistance> lin_rknn = lin_rknn_ids.get(i);
+      DistanceDBIDResult<DoubleDistance> pre_rknn = preproc_rknn_ids.get(i);
+
+      DistanceDBIDResultIter<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);
+          System.out.print(" <=> ");
+          System.out.print("PRE RkNN #" + i + " " + pre);
+          System.out.println();
+          break;
+        }
       }
       assertEquals("rkNN sizes do not agree for k=" + k, lin_rknn.size(), pre_rknn.size());
       for(int j = 0; j < lin_rknn.size(); j++) {
-        assertTrue("rkNNs of linear scan and preprocessor do not match!", lin_rknn.get(j).sameDBID(pre_rknn.get(j)));
+        assertTrue("rkNNs of linear scan and preprocessor do not match!", DBIDUtil.equal(lin_rknn.get(j), pre_rknn.get(j)));
         assertTrue("rkNNs of linear scan and preprocessor do not match!", lin_rknn.get(j).getDistance().equals(pre_rknn.get(j).getDistance()));
       }
     }