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-rw-r--r--test/de/lmu/ifi/dbs/elki/index/TestIndexStructures.java29
-rw-r--r--test/de/lmu/ifi/dbs/elki/index/preprocessed/TestMaterializedKNNAndRKNNPreprocessor.java36
2 files changed, 32 insertions, 33 deletions
diff --git a/test/de/lmu/ifi/dbs/elki/index/TestIndexStructures.java b/test/de/lmu/ifi/dbs/elki/index/TestIndexStructures.java
index 0b0b12d5..1623bcfa 100644
--- a/test/de/lmu/ifi/dbs/elki/index/TestIndexStructures.java
+++ b/test/de/lmu/ifi/dbs/elki/index/TestIndexStructures.java
@@ -33,6 +33,9 @@ import de.lmu.ifi.dbs.elki.data.DoubleVector;
import de.lmu.ifi.dbs.elki.data.type.TypeUtil;
import de.lmu.ifi.dbs.elki.database.Database;
import de.lmu.ifi.dbs.elki.database.StaticArrayDatabase;
+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;
@@ -40,12 +43,8 @@ import de.lmu.ifi.dbs.elki.database.query.range.LinearScanRangeQuery;
import de.lmu.ifi.dbs.elki.database.query.range.RangeQuery;
import de.lmu.ifi.dbs.elki.database.relation.Relation;
import de.lmu.ifi.dbs.elki.datasource.FileBasedDatabaseConnection;
-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.tree.TreeIndexFactory;
import de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mtree.MTree;
import de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mtree.MTreeFactory;
import de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.query.DoubleDistanceMetricalIndexKNNQuery;
@@ -58,8 +57,8 @@ import de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.rstar.RStarTreeFacto
import de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.strategies.insert.ApproximativeLeastOverlapInsertionStrategy;
import de.lmu.ifi.dbs.elki.index.vafile.PartialVAFile;
import de.lmu.ifi.dbs.elki.index.vafile.VAFile;
+import de.lmu.ifi.dbs.elki.persistent.AbstractPageFileFactory;
import de.lmu.ifi.dbs.elki.utilities.ClassGenericsUtil;
-import de.lmu.ifi.dbs.elki.utilities.optionhandling.ParameterException;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameterization.ListParameterization;
/**
@@ -109,7 +108,7 @@ public class TestIndexStructures implements JUnit4Test {
public void testMetrical() {
ListParameterization metparams = new ListParameterization();
metparams.addParameter(StaticArrayDatabase.INDEX_ID, MTreeFactory.class);
- metparams.addParameter(TreeIndexFactory.PAGE_SIZE_ID, 100);
+ metparams.addParameter(AbstractPageFileFactory.Parameterizer.PAGE_SIZE_ID, 300);
testFileBasedDatabaseConnection(metparams, DoubleDistanceMetricalIndexKNNQuery.class, DoubleDistanceMetricalIndexRangeQuery.class);
}
@@ -120,7 +119,7 @@ public class TestIndexStructures implements JUnit4Test {
public void testRStarTree() {
ListParameterization spatparams = new ListParameterization();
spatparams.addParameter(StaticArrayDatabase.INDEX_ID, RStarTreeFactory.class);
- spatparams.addParameter(TreeIndexFactory.PAGE_SIZE_ID, 300);
+ spatparams.addParameter(AbstractPageFileFactory.Parameterizer.PAGE_SIZE_ID, 300);
testFileBasedDatabaseConnection(spatparams, DoubleDistanceRStarTreeKNNQuery.class, DoubleDistanceRStarTreeRangeQuery.class);
}
@@ -155,9 +154,9 @@ public class TestIndexStructures implements JUnit4Test {
public void testRStarTreeFast() {
ListParameterization spatparams = new ListParameterization();
spatparams.addParameter(StaticArrayDatabase.INDEX_ID, RStarTreeFactory.class);
- spatparams.addParameter(AbstractRStarTreeFactory.INSERTION_STRATEGY_ID, ApproximativeLeastOverlapInsertionStrategy.class);
+ spatparams.addParameter(AbstractRStarTreeFactory.Parameterizer.INSERTION_STRATEGY_ID, ApproximativeLeastOverlapInsertionStrategy.class);
spatparams.addParameter(ApproximativeLeastOverlapInsertionStrategy.Parameterizer.INSERTION_CANDIDATES_ID, 1);
- spatparams.addParameter(TreeIndexFactory.PAGE_SIZE_ID, 300);
+ spatparams.addParameter(AbstractPageFileFactory.Parameterizer.PAGE_SIZE_ID, 300);
testFileBasedDatabaseConnection(spatparams, DoubleDistanceRStarTreeKNNQuery.class, DoubleDistanceRStarTreeRangeQuery.class);
}
@@ -197,12 +196,12 @@ public class TestIndexStructures implements JUnit4Test {
DoubleVector dv = new DoubleVector(querypoint);
KNNQuery<DoubleVector, DoubleDistance> knnq = db.getKNNQuery(dist, k);
assertTrue("Returned knn query is not of expected class.", expectKNNQuery.isAssignableFrom(knnq.getClass()));
- KNNResult<DoubleDistance> ids = knnq.getKNNForObject(dv, k);
+ KNNList<DoubleDistance> ids = knnq.getKNNForObject(dv, k);
assertEquals("Result size does not match expectation!", shouldd.length, ids.size());
// verify that the neighbors match.
int i = 0;
- for(DistanceDBIDResultIter<DoubleDistance> res = ids.iter(); res.valid(); res.advance(), i++) {
+ for (DistanceDBIDListIter<DoubleDistance> res = ids.iter(); res.valid(); res.advance(), i++) {
// Verify distance
assertEquals("Expected distance doesn't match.", shouldd[i], res.getDistance().doubleValue());
// verify vector
@@ -216,12 +215,12 @@ public class TestIndexStructures implements JUnit4Test {
DoubleVector dv = new DoubleVector(querypoint);
RangeQuery<DoubleVector, DoubleDistance> rangeq = db.getRangeQuery(dist, eps);
assertTrue("Returned range query is not of expected class.", expectRangeQuery.isAssignableFrom(rangeq.getClass()));
- DistanceDBIDResult<DoubleDistance> ids = rangeq.getRangeForObject(dv, eps);
+ DistanceDBIDList<DoubleDistance> ids = rangeq.getRangeForObject(dv, eps);
assertEquals("Result size does not match expectation!", shouldd.length, ids.size());
// verify that the neighbors match.
int i = 0;
- for(DistanceDBIDResultIter<DoubleDistance> res = ids.iter(); res.valid(); res.advance(), i++) {
+ for (DistanceDBIDListIter<DoubleDistance> res = ids.iter(); res.valid(); res.advance(), i++) {
// Verify distance
assertEquals("Expected distance doesn't match.", shouldd[i], res.getDistance().doubleValue());
// verify vector
@@ -231,4 +230,4 @@ public class TestIndexStructures implements JUnit4Test {
}
}
}
-} \ No newline at end of file
+}
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);
generated by cgit v1.2.3 (git 2.25.1) at 2024-06-01 17:44:57 +0000