Import Debian changes 0.6.0~beta2-1

elki (0.6.0~beta2-1) unstable; urgency=low

  * New upstream beta release.
  * 3DPC extension is not yet included.

1 files changed, 168 insertions, 0 deletions

diff --git a/test/de/lmu/ifi/dbs/elki/algorithm/outlier/lof/TestOnlineLOF.java b/test/de/lmu/ifi/dbs/elki/algorithm/outlier/lof/TestOnlineLOF.java
new file mode 100644
index 00000000..cd60a58f
--- /dev/null
+++ b/test/de/lmu/ifi/dbs/elki/algorithm/outlier/lof/TestOnlineLOF.java
@@ -0,0 +1,168 @@
+package de.lmu.ifi.dbs.elki.algorithm.outlier.lof;
+
+/*
+ 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 static org.junit.Assert.assertTrue;
+import static org.junit.Assert.fail;
+
+import java.util.ArrayList;
+import java.util.Random;
+
+import org.junit.Test;
+
+import de.lmu.ifi.dbs.elki.JUnit4Test;
+import de.lmu.ifi.dbs.elki.algorithm.outlier.lof.FlexibleLOF;
+import de.lmu.ifi.dbs.elki.algorithm.outlier.lof.LOF;
+import de.lmu.ifi.dbs.elki.algorithm.outlier.lof.OnlineLOF;
+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;
+import de.lmu.ifi.dbs.elki.database.UpdatableDatabase;
+import de.lmu.ifi.dbs.elki.database.ids.DBIDIter;
+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.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.CosineDistanceFunction;
+import de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction;
+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.result.outlier.OutlierResult;
+import de.lmu.ifi.dbs.elki.utilities.ClassGenericsUtil;
+import de.lmu.ifi.dbs.elki.utilities.exceptions.UnableToComplyException;
+import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameterization.ListParameterization;
+
+/**
+ * Tests the OnlineLOF algorithm. Compares the result of the static LOF
+ * algorithm to the result of the OnlineLOF algorithm, where some insertions and
+ * deletions (of the previously inserted objects) have been applied to the
+ * database.
+ * 
+ * @author Elke Achtert
+ * 
+ */
+public class TestOnlineLOF implements JUnit4Test {
+  // the following values depend on the data set used!
+  static String dataset = "data/testdata/unittests/3clusters-and-noise-2d.csv";
+
+  // parameter k for LOF and OnlineLOF
+  static int k = 5;
+
+  // neighborhood distance function for LOF and OnlineLOF
+  @SuppressWarnings("rawtypes")
+  static DistanceFunction neighborhoodDistanceFunction = EuclideanDistanceFunction.STATIC;
+
+  // reachability distance function for LOF and OnlineLOF
+  @SuppressWarnings("rawtypes")
+  static DistanceFunction reachabilityDistanceFunction = CosineDistanceFunction.STATIC;
+
+  // seed for the generator
+  static int seed = 5;
+
+  // size of the data set
+  static int size = 50;
+
+  /**
+   * First, run the {@link LOF} algorithm on the database. Second, run the
+   * {@link OnlineLOF} algorithm on the database, insert new objects and
+   * afterwards delete them. Then, compare the two results for equality.
+   * 
+   * @throws UnableToComplyException
+   */
+  @SuppressWarnings("unchecked")
+  @Test
+  public void testOnlineLOF() throws UnableToComplyException {
+    UpdatableDatabase db = getDatabase();
+
+    // 1. Run LOF
+    FlexibleLOF<DoubleVector, DoubleDistance> lof = new FlexibleLOF<>(k, k, neighborhoodDistanceFunction, reachabilityDistanceFunction);
+    OutlierResult result1 = lof.run(db);
+
+    // 2. Run OnlineLOF (with insertions and removals) on database
+    OutlierResult result2 = runOnlineLOF(db);
+
+    // 3. Compare results
+    Relation<Double> scores1 = result1.getScores();
+    Relation<Double> scores2 = result2.getScores();
+
+    for(DBIDIter id = scores1.getDBIDs().iter(); id.valid(); id.advance()) {
+      Double lof1 = scores1.get(id);
+      Double lof2 = scores2.get(id);
+      assertTrue("lof(" + DBIDUtil.toString(id) + ") != lof(" + DBIDUtil.toString(id) + "): " + lof1 + " != " + lof2, lof1.equals(lof2));
+    }
+  }
+
+  /**
+   * Run OnlineLOF (with insertions and removals) on database.
+   */
+  @SuppressWarnings("unchecked")
+  private static OutlierResult runOnlineLOF(UpdatableDatabase db) throws UnableToComplyException {
+    Relation<DoubleVector> rep = db.getRelation(TypeUtil.DOUBLE_VECTOR_FIELD);
+
+    // setup algorithm
+    OnlineLOF<DoubleVector, DoubleDistance> lof = new OnlineLOF<>(k, k, neighborhoodDistanceFunction, reachabilityDistanceFunction);
+
+    // run OnlineLOF on database
+    OutlierResult result = lof.run(db);
+
+    // insert new objects
+    ArrayList<DoubleVector> insertions = new ArrayList<>();
+    NumberVector.Factory<DoubleVector, ?> o = RelationUtil.getNumberVectorFactory(rep);
+    int dim = RelationUtil.dimensionality(rep);
+    Random random = new Random(seed);
+    for(int i = 0; i < size; i++) {
+      DoubleVector obj = VectorUtil.randomVector(o, dim, random);
+      insertions.add(obj);
+    }
+    DBIDs deletions = db.insert(MultipleObjectsBundle.makeSimple(rep.getDataTypeInformation(), insertions));
+
+    // delete objects
+    db.delete(deletions);
+
+    return result;
+  }
+
+  /**
+   * Returns the database.
+   */
+  private static UpdatableDatabase getDatabase() {
+    ListParameterization params = new ListParameterization();
+    params.addParameter(FileBasedDatabaseConnection.INPUT_ID, dataset);
+
+    UpdatableDatabase db = ClassGenericsUtil.parameterizeOrAbort(HashmapDatabase.class, params);
+    params.failOnErrors();
+    if(params.hasUnusedParameters()) {
+      fail("Unused parameters: " + params.getRemainingParameters());
+    }
+
+    // get database
+    db.initialize();
+    return db;
+  }
+
+}