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+package tutorial.outlier;
+
+/*
+ This file is part of ELKI:
+ Environment for Developing KDD-Applications Supported by Index-Structures
+
+ Copyright (C) 2013
+ 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 de.lmu.ifi.dbs.elki.algorithm.AbstractDistanceBasedAlgorithm;
+import de.lmu.ifi.dbs.elki.algorithm.outlier.OutlierAlgorithm;
+import de.lmu.ifi.dbs.elki.data.type.TypeInformation;
+import de.lmu.ifi.dbs.elki.data.type.TypeUtil;
+import de.lmu.ifi.dbs.elki.database.Database;
+import de.lmu.ifi.dbs.elki.database.datastore.DataStoreFactory;
+import de.lmu.ifi.dbs.elki.database.datastore.DataStoreUtil;
+import de.lmu.ifi.dbs.elki.database.datastore.WritableDoubleDataStore;
+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.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.MaterializedRelation;
+import de.lmu.ifi.dbs.elki.database.relation.Relation;
+import de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction;
+import de.lmu.ifi.dbs.elki.distance.distancevalue.Distance;
+import de.lmu.ifi.dbs.elki.logging.Logging;
+import de.lmu.ifi.dbs.elki.result.outlier.InvertedOutlierScoreMeta;
+import de.lmu.ifi.dbs.elki.result.outlier.OutlierResult;
+import de.lmu.ifi.dbs.elki.result.outlier.OutlierScoreMeta;
+import de.lmu.ifi.dbs.elki.utilities.documentation.Reference;
+import de.lmu.ifi.dbs.elki.utilities.optionhandling.OptionID;
+import de.lmu.ifi.dbs.elki.utilities.optionhandling.constraints.GreaterConstraint;
+import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameterization.Parameterization;
+import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameters.IntParameter;
+
+/**
+ * Outlier detection based on the in-degree of the kNN graph.
+ * 
+ * This is a curried version: instead of using a threshold T to obtain a binary
+ * decision, we use the computed value as outlier score.
+ * 
+ * Reference:
+ * <p>
+ * V. Hautamäki and I. Kärkkäinen and P Fränti<br />
+ * Outlier detection using k-nearest neighbour graph<br />
+ * Proc. 17th Int. Conf. Pattern Recognition, ICPR 2004 <br />
+ * </p>
+ * 
+ * @author Erich Schubert
+ * 
+ * @param <O> Object type
+ * @param <D> Distance type
+ */
+@Reference(authors = "V. Hautamäki and I. Kärkkäinen and P Fränti", title = "Outlier detection using k-nearest neighbour graph", booktitle = "Proc. 17th Int. Conf. Pattern Recognition, ICPR 2004", url = "http://dx.doi.org/10.1109/ICPR.2004.1334558")
+public class ODIN<O, D extends Distance<D>> extends AbstractDistanceBasedAlgorithm<O, D, OutlierResult> implements OutlierAlgorithm {
+  /**
+   * Class logger.
+   */
+  private static final Logging LOG = Logging.getLogger(ODIN.class);
+
+  /**
+   * Number of neighbors for kNN graph.
+   */
+  int k;
+
+  /**
+   * Constructor.
+   * 
+   * @param distanceFunction Distance function
+   * @param k k parameter
+   */
+  public ODIN(DistanceFunction<? super O, D> distanceFunction, int k) {
+    super(distanceFunction);
+    this.k = k;
+  }
+
+  /**
+   * Run the ODIN algorithm
+   * 
+   * Tutorial note: the <em>signature</em> of this method depends on the types
+   * that we requested in the {@link #getInputTypeRestriction} method. Here we
+   * requested a single relation of type {@code O} , the data type of our
+   * distance function.
+   * 
+   * @param database Database to run on.
+   * @param relation Relation to process.
+   * @return ODIN outlier result.
+   */
+  public OutlierResult run(Database database, Relation<O> relation) {
+    // Get the query functions:
+    DistanceQuery<O, D> dq = database.getDistanceQuery(relation, getDistanceFunction());
+    KNNQuery<O, D> knnq = database.getKNNQuery(dq, k);
+
+    // Get the objects to process, and a data storage for counting and output:
+    DBIDs ids = relation.getDBIDs();
+    WritableDoubleDataStore scores = DataStoreUtil.makeDoubleStorage(ids, DataStoreFactory.HINT_DB, 0.);
+
+    // Process all objects
+    for (DBIDIter iter = ids.iter(); iter.valid(); iter.advance()) {
+      // Find the nearest neighbors (using an index, if available!)
+      KNNList<D> neighbors = knnq.getKNNForDBID(iter, k);
+      // For each neighbor, except ourselves, increase the in-degree:
+      for (DBIDIter nei = neighbors.iter(); nei.valid(); nei.advance()) {
+        if (DBIDUtil.equal(iter, nei)) {
+          continue;
+        }
+        scores.put(nei, scores.doubleValue(nei) + 1);
+      }
+    }
+
+    // Compute maximum
+    double min = Double.POSITIVE_INFINITY, max = 0.0;
+    for (DBIDIter iter = ids.iter(); iter.valid(); iter.advance()) {
+      min = Math.min(min, scores.doubleValue(iter));
+      max = Math.max(max, scores.doubleValue(iter));
+    }
+    // Wrap the result and add metadata.
+    // By actually specifying theoretical min, max and baseline, we get a better
+    // visualization (try it out - or see the screenshots in the tutorial)!
+    OutlierScoreMeta meta = new InvertedOutlierScoreMeta(min, max, 0., ids.size() - 1, k);
+    Relation<Double> rel = new MaterializedRelation<>("ODIN In-Degree", "odin", TypeUtil.DOUBLE, scores, ids);
+    return new OutlierResult(meta, rel);
+  }
+
+  @Override
+  public TypeInformation[] getInputTypeRestriction() {
+    return TypeUtil.array(getDistanceFunction().getInputTypeRestriction());
+  }
+
+  @Override
+  protected Logging getLogger() {
+    return LOG;
+  }
+
+  /**
+   * Parameterization class.
+   * 
+   * @author Erich Schubert
+   * 
+   * @apiviz.exclude
+   * 
+   * @param <O> Object type
+   * @param <D> Distance type
+   */
+  public static class Parameterizer<O, D extends Distance<D>> extends AbstractDistanceBasedAlgorithm.Parameterizer<O, D> {
+    /**
+     * Parameter for the number of nearest neighbors:
+     * 
+     * <pre>
+     * -odin.k &lt;int&gt;
+     * </pre>
+     */
+    public static final OptionID K_ID = new OptionID("odin.k", "Number of neighbors to use for kNN graph.");
+
+    /**
+     * Number of nearest neighbors to use.
+     */
+    int k;
+
+    @Override
+    protected void makeOptions(Parameterization config) {
+      super.makeOptions(config);
+
+      IntParameter param = new IntParameter(K_ID);
+      // Since in a database context, the 1 nearest neighbor
+      // will usually be the query object itself, we require
+      // this value to be at least 2.
+      param.addConstraint(new GreaterConstraint(1));
+      if (config.grab(param)) {
+        k = param.intValue();
+      }
+    }
+
+    @Override
+    protected ODIN<O, D> makeInstance() {
+      return new ODIN<>(distanceFunction, k);
+    }
+  }
+}