Import Upstream version 0.6.5~20141030

1 files changed, 33 insertions, 33 deletions

diff --git a/src/de/lmu/ifi/dbs/elki/algorithm/clustering/SNNClustering.java b/src/de/lmu/ifi/dbs/elki/algorithm/clustering/SNNClustering.java
index 86bb9a09..d785b83f 100644
--- a/src/de/lmu/ifi/dbs/elki/algorithm/clustering/SNNClustering.java
+++ b/src/de/lmu/ifi/dbs/elki/algorithm/clustering/SNNClustering.java
@@ -4,7 +4,7 @@ package de.lmu.ifi.dbs.elki.algorithm.clustering;
  This file is part of ELKI:
  Environment for Developing KDD-Applications Supported by Index-Structures
 
- Copyright (C) 2013
+ Copyright (C) 2014
  Ludwig-Maximilians-Universität München
  Lehr- und Forschungseinheit für Datenbanksysteme
  ELKI Development Team
@@ -38,11 +38,11 @@ import de.lmu.ifi.dbs.elki.database.Database;
 import de.lmu.ifi.dbs.elki.database.ids.ArrayModifiableDBIDs;
 import de.lmu.ifi.dbs.elki.database.ids.DBID;
 import de.lmu.ifi.dbs.elki.database.ids.DBIDIter;
+import de.lmu.ifi.dbs.elki.database.ids.DBIDRef;
 import de.lmu.ifi.dbs.elki.database.ids.DBIDUtil;
 import de.lmu.ifi.dbs.elki.database.ids.ModifiableDBIDs;
 import de.lmu.ifi.dbs.elki.database.query.similarity.SimilarityQuery;
 import de.lmu.ifi.dbs.elki.database.relation.Relation;
-import de.lmu.ifi.dbs.elki.distance.distancevalue.IntegerDistance;
 import de.lmu.ifi.dbs.elki.distance.similarityfunction.SharedNearestNeighborSimilarityFunction;
 import de.lmu.ifi.dbs.elki.logging.Logging;
 import de.lmu.ifi.dbs.elki.logging.progress.FiniteProgress;
@@ -55,7 +55,6 @@ import de.lmu.ifi.dbs.elki.utilities.optionhandling.AbstractParameterizer;
 import de.lmu.ifi.dbs.elki.utilities.optionhandling.OptionID;
 import de.lmu.ifi.dbs.elki.utilities.optionhandling.constraints.CommonConstraints;
 import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameterization.Parameterization;
-import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameters.DistanceParameter;
 import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameters.IntParameter;
 
 /**
@@ -76,7 +75,10 @@ import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameters.IntParameter;
  */
 @Title("SNN: Shared Nearest Neighbor Clustering")
 @Description("Algorithm to find shared-nearest-neighbors-density-connected sets in a database based on the " + "parameters 'minPts' and 'epsilon' (specifying a volume). " + "These two parameters determine a density threshold for clustering.")
-@Reference(authors = "L. Ertöz, M. Steinbach, V. Kumar", title = "Finding Clusters of Different Sizes, Shapes, and Densities in Noisy, High Dimensional Data", booktitle = "Proc. of SIAM Data Mining (SDM), 2003", url = "http://www.siam.org/meetings/sdm03/proceedings/sdm03_05.pdf")
+@Reference(authors = "L. Ertöz, M. Steinbach, V. Kumar", //
+title = "Finding Clusters of Different Sizes, Shapes, and Densities in Noisy, High Dimensional Data", //
+booktitle = "Proc. of SIAM Data Mining (SDM), 2003", //
+url = "http://www.siam.org/meetings/sdm03/proceedings/sdm03_05.pdf")
 public class SNNClustering<O> extends AbstractAlgorithm<Clustering<Model>> implements ClusteringAlgorithm<Clustering<Model>> {
   /**
    * The logger for this class.
@@ -84,24 +86,12 @@ public class SNNClustering<O> extends AbstractAlgorithm<Clustering<Model>> imple
   private static final Logging LOG = Logging.getLogger(SNNClustering.class);
 
   /**
-   * Parameter to specify the minimum SNN density, must be an integer greater
-   * than 0.
+   * Epsilon radius threshold.
    */
-  public static final OptionID EPSILON_ID = new OptionID("snn.epsilon", "The minimum SNN density.");
+  private int epsilon;
 
   /**
-   * Holds the value of {@link #EPSILON_ID}.
-   */
-  private IntegerDistance epsilon;
-
-  /**
-   * Parameter to specify the threshold for minimum number of points in the
-   * epsilon-SNN-neighborhood of a point, must be an integer greater than 0.
-   */
-  public static final OptionID MINPTS_ID = new OptionID("snn.minpts", "Threshold for minimum number of points in " + "the epsilon-SNN-neighborhood of a point.");
-
-  /**
-   * Holds the value of {@link #MINPTS_ID}.
+   * Minimum number of clusters for connectedness.
    */
   private int minpts;
 
@@ -132,7 +122,7 @@ public class SNNClustering<O> extends AbstractAlgorithm<Clustering<Model>> imple
    * @param epsilon Epsilon
    * @param minpts Minpts
    */
-  public SNNClustering(SharedNearestNeighborSimilarityFunction<O> similarityFunction, IntegerDistance epsilon, int minpts) {
+  public SNNClustering(SharedNearestNeighborSimilarityFunction<O> similarityFunction, int epsilon, int minpts) {
     super();
     this.similarityFunction = similarityFunction;
     this.epsilon = epsilon;
@@ -147,7 +137,7 @@ public class SNNClustering<O> extends AbstractAlgorithm<Clustering<Model>> imple
    * @return Result
    */
   public Clustering<Model> run(Database database, Relation<O> relation) {
-    SimilarityQuery<O, IntegerDistance> snnInstance = similarityFunction.instantiate(relation);
+    SimilarityQuery<O> snnInstance = similarityFunction.instantiate(relation);
 
     FiniteProgress objprog = LOG.isVerbose() ? new FiniteProgress("SNNClustering", relation.size(), LOG) : null;
     IndefiniteProgress clusprog = LOG.isVerbose() ? new IndefiniteProgress("Number of clusters", LOG) : null;
@@ -155,9 +145,9 @@ public class SNNClustering<O> extends AbstractAlgorithm<Clustering<Model>> imple
     noise = DBIDUtil.newHashSet();
     processedIDs = DBIDUtil.newHashSet(relation.size());
     if(relation.size() >= minpts) {
-      for(DBIDIter id = snnInstance.getRelation().iterDBIDs(); id.valid(); id.advance()) {
+      for(DBIDIter id = relation.iterDBIDs(); id.valid(); id.advance()) {
         if(!processedIDs.contains(id)) {
-          expandCluster(snnInstance, DBIDUtil.deref(id), objprog, clusprog);
+          expandCluster(snnInstance, id, objprog, clusprog);
           if(processedIDs.size() == relation.size() && noise.size() == 0) {
             break;
           }
@@ -169,7 +159,7 @@ public class SNNClustering<O> extends AbstractAlgorithm<Clustering<Model>> imple
       }
     }
     else {
-      for(DBIDIter id = snnInstance.getRelation().iterDBIDs(); id.valid(); id.advance()) {
+      for(DBIDIter id = relation.iterDBIDs(); id.valid(); id.advance()) {
         noise.add(id);
         if(objprog != null && clusprog != null) {
           objprog.setProcessed(noise.size(), LOG);
@@ -178,10 +168,8 @@ public class SNNClustering<O> extends AbstractAlgorithm<Clustering<Model>> imple
       }
     }
     // Finish progress logging
-    if(objprog != null && clusprog != null) {
-      objprog.ensureCompleted(LOG);
-      clusprog.setCompleted(LOG);
-    }
+    LOG.ensureCompleted(objprog);
+    LOG.setCompleted(clusprog);
 
     Clustering<Model> result = new Clustering<>("Shared-Nearest-Neighbor Clustering", "snn-clustering");
     for(Iterator<ModifiableDBIDs> resultListIter = resultList.iterator(); resultListIter.hasNext();) {
@@ -201,10 +189,10 @@ public class SNNClustering<O> extends AbstractAlgorithm<Clustering<Model>> imple
    * @return the shared nearest neighbors of the specified query object in the
    *         given database
    */
-  protected ArrayModifiableDBIDs findSNNNeighbors(SimilarityQuery<O, IntegerDistance> snnInstance, DBID queryObject) {
+  protected ArrayModifiableDBIDs findSNNNeighbors(SimilarityQuery<O> snnInstance, DBIDRef queryObject) {
     ArrayModifiableDBIDs neighbors = DBIDUtil.newArray();
     for(DBIDIter iditer = snnInstance.getRelation().iterDBIDs(); iditer.valid(); iditer.advance()) {
-      if(snnInstance.similarity(queryObject, iditer).compareTo(epsilon) >= 0) {
+      if(snnInstance.similarity(queryObject, iditer) >= epsilon) {
         neighbors.add(iditer);
       }
     }
@@ -222,7 +210,7 @@ public class SNNClustering<O> extends AbstractAlgorithm<Clustering<Model>> imple
    * @param objprog the progress object to report about the progress of
    *        clustering
    */
-  protected void expandCluster(SimilarityQuery<O, IntegerDistance> snnInstance, DBID startObjectID, FiniteProgress objprog, IndefiniteProgress clusprog) {
+  protected void expandCluster(SimilarityQuery<O> snnInstance, DBIDRef startObjectID, FiniteProgress objprog, IndefiniteProgress clusprog) {
     ArrayModifiableDBIDs seeds = findSNNNeighbors(snnInstance, startObjectID);
 
     // startObject is no core-object
@@ -310,7 +298,19 @@ public class SNNClustering<O> extends AbstractAlgorithm<Clustering<Model>> imple
    * @param <O> object type
    */
   public static class Parameterizer<O> extends AbstractParameterizer {
-    protected IntegerDistance epsilon;
+    /**
+     * Parameter to specify the minimum SNN density, must be an integer greater
+     * than 0.
+     */
+    public static final OptionID EPSILON_ID = new OptionID("snn.epsilon", "The minimum SNN density.");
+
+    /**
+     * Parameter to specify the threshold for minimum number of points in the
+     * epsilon-SNN-neighborhood of a point, must be an integer greater than 0.
+     */
+    public static final OptionID MINPTS_ID = new OptionID("snn.minpts", "Threshold for minimum number of points in " + "the epsilon-SNN-neighborhood of a point.");
+
+    protected int epsilon;
 
     protected int minpts;
 
@@ -322,7 +322,7 @@ public class SNNClustering<O> extends AbstractAlgorithm<Clustering<Model>> imple
       Class<SharedNearestNeighborSimilarityFunction<O>> cls = ClassGenericsUtil.uglyCastIntoSubclass(SharedNearestNeighborSimilarityFunction.class);
       similarityFunction = config.tryInstantiate(cls);
 
-      DistanceParameter<IntegerDistance> epsilonP = new DistanceParameter<>(EPSILON_ID, IntegerDistance.FACTORY);
+      IntParameter epsilonP = new IntParameter(EPSILON_ID);
       if(config.grab(epsilonP)) {
         epsilon = epsilonP.getValue();
       }