package de.lmu.ifi.dbs.elki.algorithm;
/*
This file is part of ELKI:
Environment for Developing KDD-Applications Supported by Index-Structures
Copyright (C) 2012
Ludwig-Maximilians-Universität München
Lehr- und Forschungseinheit für Datenbanksysteme
ELKI Development Team
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU Affero General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU Affero General Public License for more details.
You should have received a copy of the GNU Affero General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
*/
import java.util.Arrays;
import java.util.List;
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.type.TypeUtil;
import de.lmu.ifi.dbs.elki.database.Database;
import de.lmu.ifi.dbs.elki.database.QueryUtil;
import de.lmu.ifi.dbs.elki.database.StaticArrayDatabase;
import de.lmu.ifi.dbs.elki.database.datastore.DataStore;
import de.lmu.ifi.dbs.elki.database.ids.DBID;
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.relation.Relation;
import de.lmu.ifi.dbs.elki.datasource.FileBasedDatabaseConnection;
import de.lmu.ifi.dbs.elki.datasource.filter.FixedDBIDsFilter;
import de.lmu.ifi.dbs.elki.distance.distancefunction.EuclideanDistanceFunction;
import de.lmu.ifi.dbs.elki.distance.distancefunction.ManhattanDistanceFunction;
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.spatial.SpatialEntry;
import de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.deliclu.DeLiCluTree;
import de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.deliclu.DeLiCluTreeFactory;
import de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.rstar.RStarTree;
import de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.rstar.RStarTreeFactory;
import de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.rstar.RStarTreeNode;
import de.lmu.ifi.dbs.elki.math.MeanVariance;
import de.lmu.ifi.dbs.elki.utilities.ClassGenericsUtil;
import de.lmu.ifi.dbs.elki.utilities.datastructures.heap.KNNList;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.ParameterException;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameterization.ListParameterization;
public class TestKNNJoin implements JUnit4Test {
// the following values depend on the data set used!
String dataset = "data/testdata/unittests/uebungsblatt-2d-mini.csv";
// size of the data set
int shoulds = 20;
// mean number of 2NN
double mean2nnEuclid = 2.85;
// variance
double var2nnEuclid = 0.87105;
// mean number of 2NN
double mean2nnManhattan = 2.9;
// variance
double var2nnManhattan = 0.83157894;
@Test
public void testLinearScan() {
ListParameterization inputparams = new ListParameterization();
inputparams.addParameter(FileBasedDatabaseConnection.INPUT_ID, dataset);
List<Class<?>> filters = Arrays.asList(new Class<?>[] { FixedDBIDsFilter.class });
inputparams.addParameter(FileBasedDatabaseConnection.FILTERS_ID, filters);
inputparams.addParameter(FixedDBIDsFilter.IDSTART_ID, 1);
// get database
Database db = ClassGenericsUtil.parameterizeOrAbort(StaticArrayDatabase.class, inputparams);
inputparams.failOnErrors();
db.initialize();
Relation<NumberVector<?, ?>> relation = db.getRelation(TypeUtil.NUMBER_VECTOR_FIELD);
// verify data set size.
org.junit.Assert.assertEquals("Database size does not match.", shoulds, relation.size());
// Euclidean
{
DistanceQuery<NumberVector<?, ?>, DoubleDistance> dq = db.getDistanceQuery(relation, EuclideanDistanceFunction.STATIC);
KNNQuery<NumberVector<?, ?>, DoubleDistance> knnq = QueryUtil.getLinearScanKNNQuery(dq);
MeanVariance meansize = new MeanVariance();
for(DBID id : relation.iterDBIDs()) {
KNNResult<DoubleDistance> knnlist = knnq.getKNNForDBID(id, 2);
meansize.put(knnlist.size());
}
org.junit.Assert.assertEquals("Euclidean mean 2NN", mean2nnEuclid, meansize.getMean(), 0.00001);
org.junit.Assert.assertEquals("Euclidean variance 2NN", var2nnEuclid, meansize.getSampleVariance(), 0.00001);
}
// Manhattan
{
DistanceQuery<NumberVector<?, ?>, DoubleDistance> dq = db.getDistanceQuery(relation, ManhattanDistanceFunction.STATIC);
KNNQuery<NumberVector<?, ?>, DoubleDistance> knnq = QueryUtil.getLinearScanKNNQuery(dq);
MeanVariance meansize = new MeanVariance();
for(DBID id : relation.iterDBIDs()) {
KNNResult<DoubleDistance> knnlist = knnq.getKNNForDBID(id, 2);
meansize.put(knnlist.size());
}
org.junit.Assert.assertEquals("Manhattan mean 2NN", mean2nnManhattan, meansize.getMean(), 0.00001);
org.junit.Assert.assertEquals("Manhattan variance 2NN", var2nnManhattan, meansize.getSampleVariance(), 0.00001);
}
}
/**
* Test {@link RStarTree} using a file based database connection.
*
* @throws ParameterException on errors.
*/
@Test
public void testKNNJoinRtreeMini() {
ListParameterization spatparams = new ListParameterization();
spatparams.addParameter(StaticArrayDatabase.INDEX_ID, RStarTreeFactory.class);
spatparams.addParameter(TreeIndexFactory.PAGE_SIZE_ID, 200);
doKNNJoin(spatparams);
}
/**
* Test {@link RStarTree} using a file based database connection.
*
* @throws ParameterException on errors.
*/
@Test
public void testKNNJoinRtreeMaxi() {
ListParameterization spatparams = new ListParameterization();
spatparams.addParameter(StaticArrayDatabase.INDEX_ID, RStarTreeFactory.class);
spatparams.addParameter(TreeIndexFactory.PAGE_SIZE_ID, 2000);
doKNNJoin(spatparams);
}
/**
* Test {@link DeLiCluTree} using a file based database connection.
*
* @throws ParameterException on errors.
*/
@Test
public void testKNNJoinDeLiCluTreeMini() {
ListParameterization spatparams = new ListParameterization();
spatparams.addParameter(StaticArrayDatabase.INDEX_ID, DeLiCluTreeFactory.class);
spatparams.addParameter(TreeIndexFactory.PAGE_SIZE_ID, 200);
doKNNJoin(spatparams);
}
/**
* Actual test routine.
*
* @param inputparams
* @throws ParameterException
*/
void doKNNJoin(ListParameterization inputparams) {
inputparams.addParameter(FileBasedDatabaseConnection.INPUT_ID, dataset);
List<Class<?>> filters = Arrays.asList(new Class<?>[] { FixedDBIDsFilter.class });
inputparams.addParameter(FileBasedDatabaseConnection.FILTERS_ID, filters);
inputparams.addParameter(FixedDBIDsFilter.IDSTART_ID, 1);
// get database
Database db = ClassGenericsUtil.parameterizeOrAbort(StaticArrayDatabase.class, inputparams);
inputparams.failOnErrors();
db.initialize();
Relation<NumberVector<?, ?>> relation = db.getRelation(TypeUtil.NUMBER_VECTOR_FIELD);
// verify data set size.
org.junit.Assert.assertEquals("Database size does not match.", shoulds, relation.size());
// Euclidean
{
KNNJoin<DoubleVector, DoubleDistance, ?, ?> knnjoin = new KNNJoin<DoubleVector, DoubleDistance, RStarTreeNode, SpatialEntry>(EuclideanDistanceFunction.STATIC, 2);
DataStore<KNNList<DoubleDistance>> result = knnjoin.run(db);
MeanVariance meansize = new MeanVariance();
for(DBID id : relation.getDBIDs()) {
KNNList<DoubleDistance> knnlist = result.get(id);
meansize.put(knnlist.size());
}
org.junit.Assert.assertEquals("Euclidean mean 2NN", mean2nnEuclid, meansize.getMean(), 0.00001);
org.junit.Assert.assertEquals("Euclidean variance 2NN", var2nnEuclid, meansize.getSampleVariance(), 0.00001);
}
// Manhattan
{
KNNJoin<DoubleVector, DoubleDistance, ?, ?> knnjoin = new KNNJoin<DoubleVector, DoubleDistance, RStarTreeNode, SpatialEntry>(ManhattanDistanceFunction.STATIC, 2);
DataStore<KNNList<DoubleDistance>> result = knnjoin.run(db);
MeanVariance meansize = new MeanVariance();
for(DBID id : relation.getDBIDs()) {
KNNList<DoubleDistance> knnlist = result.get(id);
meansize.put(knnlist.size());
}
org.junit.Assert.assertEquals("Manhattan mean 2NN", mean2nnManhattan, meansize.getMean(), 0.00001);
org.junit.Assert.assertEquals("Manhattan variance 2NN", var2nnManhattan, meansize.getSampleVariance(), 0.00001);
}
}
}