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-rw-r--r--test/de/lmu/ifi/dbs/elki/index/TestIndexStructures.java44
-rw-r--r--test/de/lmu/ifi/dbs/elki/index/preprocessed/TestMaterializedKNNAndRKNNPreprocessor.java55
2 files changed, 47 insertions, 52 deletions
diff --git a/test/de/lmu/ifi/dbs/elki/index/TestIndexStructures.java b/test/de/lmu/ifi/dbs/elki/index/TestIndexStructures.java
index e0b3a479..0b0b12d5 100644
--- a/test/de/lmu/ifi/dbs/elki/index/TestIndexStructures.java
+++ b/test/de/lmu/ifi/dbs/elki/index/TestIndexStructures.java
@@ -26,8 +26,6 @@ package de.lmu.ifi.dbs.elki.index;
import static junit.framework.Assert.assertEquals;
import static junit.framework.Assert.assertTrue;
-import java.util.List;
-
import org.junit.Test;
import de.lmu.ifi.dbs.elki.JUnit4Test;
@@ -35,22 +33,23 @@ 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.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.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.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.MetricalIndexKNNQuery;
-import de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.query.MetricalIndexRangeQuery;
+import de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.query.DoubleDistanceMetricalIndexKNNQuery;
+import de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.query.DoubleDistanceMetricalIndexRangeQuery;
import de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.AbstractRStarTreeFactory;
import de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.query.DoubleDistanceRStarTreeKNNQuery;
import de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.query.DoubleDistanceRStarTreeRangeQuery;
@@ -96,8 +95,6 @@ public class TestIndexStructures implements JUnit4Test {
/**
* Test exact query, also to validate the test is correct.
- *
- * @throws ParameterException on errors.
*/
@Test
public void testExact() {
@@ -107,21 +104,17 @@ public class TestIndexStructures implements JUnit4Test {
/**
* Test {@link MTree} using a file based database connection.
- *
- * @throws ParameterException on errors.
*/
@Test
public void testMetrical() {
ListParameterization metparams = new ListParameterization();
metparams.addParameter(StaticArrayDatabase.INDEX_ID, MTreeFactory.class);
metparams.addParameter(TreeIndexFactory.PAGE_SIZE_ID, 100);
- testFileBasedDatabaseConnection(metparams, MetricalIndexKNNQuery.class, MetricalIndexRangeQuery.class);
+ testFileBasedDatabaseConnection(metparams, DoubleDistanceMetricalIndexKNNQuery.class, DoubleDistanceMetricalIndexRangeQuery.class);
}
/**
* Test {@link RStarTree} using a file based database connection.
- *
- * @throws ParameterException on errors.
*/
@Test
public void testRStarTree() {
@@ -133,8 +126,6 @@ public class TestIndexStructures implements JUnit4Test {
/**
* Test {@link VAFile} using a file based database connection.
- *
- * @throws ParameterException on errors.
*/
@Test
public void testVAFile() {
@@ -143,11 +134,9 @@ public class TestIndexStructures implements JUnit4Test {
spatparams.addParameter(VAFile.Factory.PARTITIONS_ID, 4);
testFileBasedDatabaseConnection(spatparams, VAFile.VAFileKNNQuery.class, VAFile.VAFileRangeQuery.class);
}
-
+
/**
* Test {@link PartialVAFile} using a file based database connection.
- *
- * @throws ParameterException on errors.
*/
@Test
public void testPartialVAFile() {
@@ -156,13 +145,11 @@ public class TestIndexStructures implements JUnit4Test {
spatparams.addParameter(PartialVAFile.Factory.PARTITIONS_ID, 4);
testFileBasedDatabaseConnection(spatparams, PartialVAFile.PartialVAFileKNNQuery.class, PartialVAFile.PartialVAFileRangeQuery.class);
}
-
+
/**
* Test {@link RStarTree} using a file based database connection. With "fast"
* mode enabled on an extreme level (since this should only reduce
- * performance, not accuracy!)
- *
- * @throws ParameterException on errors.
+ * performance, not correctness!)
*/
@Test
public void testRStarTreeFast() {
@@ -176,8 +163,6 @@ public class TestIndexStructures implements JUnit4Test {
/**
* Test {@link XTree} using a file based database connection.
- *
- * @throws ParameterException
*/
// @Test
// public void testXTree() {
@@ -194,7 +179,6 @@ public class TestIndexStructures implements JUnit4Test {
* Actual test routine.
*
* @param inputparams
- * @throws ParameterException
*/
void testFileBasedDatabaseConnection(ListParameterization inputparams, Class<?> expectKNNQuery, Class<?> expectRangeQuery) {
inputparams.addParameter(FileBasedDatabaseConnection.INPUT_ID, dataset);
@@ -218,15 +202,13 @@ public class TestIndexStructures implements JUnit4Test {
// verify that the neighbors match.
int i = 0;
- for(DistanceResultPair<DoubleDistance> res : ids) {
+ for(DistanceDBIDResultIter<DoubleDistance> res = ids.iter(); res.valid(); res.advance(), i++) {
// Verify distance
assertEquals("Expected distance doesn't match.", shouldd[i], res.getDistance().doubleValue());
// verify vector
DoubleVector c = rep.get(res);
DoubleVector c2 = new DoubleVector(shouldc[i]);
assertEquals("Expected vector doesn't match: " + c.toString(), 0.0, dist.distance(c, c2).doubleValue(), 0.00001);
-
- i++;
}
}
{
@@ -234,20 +216,18 @@ 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()));
- List<DistanceResultPair<DoubleDistance>> ids = rangeq.getRangeForObject(dv, eps);
+ DistanceDBIDResult<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(DistanceResultPair<DoubleDistance> res : ids) {
+ for(DistanceDBIDResultIter<DoubleDistance> res = ids.iter(); res.valid(); res.advance(), i++) {
// Verify distance
assertEquals("Expected distance doesn't match.", shouldd[i], res.getDistance().doubleValue());
// verify vector
DoubleVector c = rep.get(res);
DoubleVector c2 = new DoubleVector(shouldc[i]);
assertEquals("Expected vector doesn't match: " + c.toString(), 0.0, dist.distance(c, c2).doubleValue(), 0.00001);
-
- i++;
}
}
}
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()));
}
}
generated by cgit v1.2.3 (git 2.25.1) at 2024-06-14 22:49:46 +0000