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Diffstat (limited to 'test/de/lmu/ifi/dbs/elki/algorithm/TestKNNJoin.java')
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diff --git a/test/de/lmu/ifi/dbs/elki/algorithm/TestKNNJoin.java b/test/de/lmu/ifi/dbs/elki/algorithm/TestKNNJoin.java deleted file mode 100644 index 19509350..00000000 --- a/test/de/lmu/ifi/dbs/elki/algorithm/TestKNNJoin.java +++ /dev/null @@ -1,215 +0,0 @@ -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.DBIDIter; -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.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.minkowski.EuclideanDistanceFunction; -import de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski.ManhattanDistanceFunction; -import de.lmu.ifi.dbs.elki.distance.distancevalue.DoubleDistance; -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.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; - -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.Parameterizer.INPUT_ID, dataset); - List<Class<?>> filters = Arrays.asList(new Class<?>[] { FixedDBIDsFilter.class }); - inputparams.addParameter(FileBasedDatabaseConnection.Parameterizer.FILTERS_ID, filters); - inputparams.addParameter(FixedDBIDsFilter.Parameterizer.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(DBIDIter iditer = relation.iterDBIDs(); iditer.valid(); iditer.advance()) { - KNNList<DoubleDistance> knnlist = knnq.getKNNForDBID(iditer, 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(DBIDIter iditer = relation.iterDBIDs(); iditer.valid(); iditer.advance()) { - KNNList<DoubleDistance> knnlist = knnq.getKNNForDBID(iditer, 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.Parameterizer.INDEX_ID, RStarTreeFactory.class); - spatparams.addParameter(AbstractPageFileFactory.Parameterizer.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.Parameterizer.INDEX_ID, RStarTreeFactory.class); - spatparams.addParameter(AbstractPageFileFactory.Parameterizer.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.Parameterizer.INDEX_ID, DeLiCluTreeFactory.class); - spatparams.addParameter(AbstractPageFileFactory.Parameterizer.PAGE_SIZE_ID, 200); - - doKNNJoin(spatparams); - } - - /** - * Actual test routine. - * - * @param inputparams - * @throws ParameterException - */ - void doKNNJoin(ListParameterization inputparams) { - inputparams.addParameter(FileBasedDatabaseConnection.Parameterizer.INPUT_ID, dataset); - List<Class<?>> filters = Arrays.asList(new Class<?>[] { FixedDBIDsFilter.class }); - inputparams.addParameter(FileBasedDatabaseConnection.Parameterizer.FILTERS_ID, filters); - inputparams.addParameter(FixedDBIDsFilter.Parameterizer.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(DBIDIter id = relation.getDBIDs().iter(); id.valid(); id.advance()) { - KNNList<DoubleDistance> knnlist = result.get(id); - meansize.put(knnlist.size()); - } - org.junit.Assert.assertEquals("Euclidean mean 2NN set size", 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(DBIDIter id = relation.getDBIDs().iter(); id.valid(); id.advance()) { - 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); - } - } -}
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