1 files changed, 30 insertions, 40 deletions

diff --git a/src/de/lmu/ifi/dbs/elki/algorithm/clustering/kmeans/BestOfMultipleKMeans.java b/src/de/lmu/ifi/dbs/elki/algorithm/clustering/kmeans/BestOfMultipleKMeans.java
index 51e7ace9..1c9c6a71 100644
--- a/src/de/lmu/ifi/dbs/elki/algorithm/clustering/kmeans/BestOfMultipleKMeans.java
+++ b/src/de/lmu/ifi/dbs/elki/algorithm/clustering/kmeans/BestOfMultipleKMeans.java
@@ -3,7 +3,7 @@ package de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans;
 /*
  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
@@ -32,7 +32,6 @@ import de.lmu.ifi.dbs.elki.database.Database;
 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.distancefunction.PrimitiveDistanceFunction;
-import de.lmu.ifi.dbs.elki.distance.distancevalue.Distance;
 import de.lmu.ifi.dbs.elki.logging.Logging;
 import de.lmu.ifi.dbs.elki.logging.progress.FiniteProgress;
 import de.lmu.ifi.dbs.elki.utilities.exceptions.AbortException;
@@ -50,10 +49,9 @@ import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameters.ObjectParameter;
  * @author Erich Schubert
  * 
  * @param <V> Vector type
- * @param <D> Distance type
  * @param <M> Model type
  */
-public class BestOfMultipleKMeans<V extends NumberVector<?>, D extends Distance<?>, M extends MeanModel<V>> extends AbstractAlgorithm<Clustering<M>> implements KMeans<V, D, M> {
+public class BestOfMultipleKMeans<V extends NumberVector, M extends MeanModel> extends AbstractAlgorithm<Clustering<M>> implements KMeans<V, M> {
   /**
    * The logger for this class.
    */
@@ -67,12 +65,12 @@ public class BestOfMultipleKMeans<V extends NumberVector<?>, D extends Distance<
   /**
    * Variant of kMeans for the bisecting step.
    */
-  private KMeans<V, D, M> innerkMeans;
+  private KMeans<V, M> innerkMeans;
 
   /**
    * Quality measure which should be used.
    */
-  private KMeansQualityMeasure<? super V, ? super D> qualityMeasure;
+  private KMeansQualityMeasure<? super V> qualityMeasure;
 
   /**
    * Constructor.
@@ -81,7 +79,7 @@ public class BestOfMultipleKMeans<V extends NumberVector<?>, D extends Distance<
    * @param innerkMeans K-Means variant to actually use.
    * @param qualityMeasure Quality measure
    */
-  public BestOfMultipleKMeans(int trials, KMeans<V, D, M> innerkMeans, KMeansQualityMeasure<? super V, ? super D> qualityMeasure) {
+  public BestOfMultipleKMeans(int trials, KMeans<V, M> innerkMeans, KMeansQualityMeasure<? super V> qualityMeasure) {
     super();
     this.trials = trials;
     this.innerkMeans = innerkMeans;
@@ -93,34 +91,27 @@ public class BestOfMultipleKMeans<V extends NumberVector<?>, D extends Distance<
     if(!(innerkMeans.getDistanceFunction() instanceof PrimitiveDistanceFunction)) {
       throw new AbortException("K-Means results can only be evaluated for primitive distance functions, got: " + innerkMeans.getDistanceFunction().getClass());
     }
-    final PrimitiveDistanceFunction<? super V, D> df = (PrimitiveDistanceFunction<? super V, D>) innerkMeans.getDistanceFunction();
+    @SuppressWarnings("unchecked")
+    final PrimitiveDistanceFunction<? super NumberVector> df = (PrimitiveDistanceFunction<? super NumberVector>) innerkMeans.getDistanceFunction();
+
     Clustering<M> bestResult = null;
-    if(trials > 1) {
-      double bestCost = Double.POSITIVE_INFINITY;
-      FiniteProgress prog = LOG.isVerbose() ? new FiniteProgress("K-means iterations", trials, LOG) : null;
-      for(int i = 0; i < trials; i++) {
-        Clustering<M> currentCandidate = innerkMeans.run(database, relation);
-        double currentCost = qualityMeasure.calculateCost(currentCandidate, df, relation);
-
-        if(LOG.isVerbose()) {
-          LOG.verbose("Cost of candidate " + i + ": " + currentCost);
-        }
-
-        if(currentCost < bestCost) {
-          bestResult = currentCandidate;
-          bestCost = currentCost;
-        }
-        if(prog != null) {
-          prog.incrementProcessed(LOG);
-        }
+    double bestCost = Double.NaN;
+    FiniteProgress prog = LOG.isVerbose() ? new FiniteProgress("K-means iterations", trials, LOG) : null;
+    for(int i = 0; i < trials; i++) {
+      Clustering<M> currentCandidate = innerkMeans.run(database, relation);
+      double currentCost = qualityMeasure.quality(currentCandidate, df, relation);
+
+      if(LOG.isVerbose()) {
+        LOG.verbose("Cost of candidate " + i + ": " + currentCost);
       }
-      if(prog != null) {
-        prog.ensureCompleted(LOG);
+
+      if(qualityMeasure.isBetter(currentCost, bestCost)) {
+        bestResult = currentCandidate;
+        bestCost = currentCost;
       }
+      LOG.incrementProcessed(prog);
     }
-    else {
-      bestResult = innerkMeans.run(database);
-    }
+    LOG.ensureCompleted(prog);
 
     return bestResult;
   }
@@ -131,7 +122,7 @@ public class BestOfMultipleKMeans<V extends NumberVector<?>, D extends Distance<
   }
 
   @Override
-  public DistanceFunction<? super V, D> getDistanceFunction() {
+  public DistanceFunction<? super V> getDistanceFunction() {
     return innerkMeans.getDistanceFunction();
   }
 
@@ -141,7 +132,7 @@ public class BestOfMultipleKMeans<V extends NumberVector<?>, D extends Distance<
   }
 
   @Override
-  public void setDistanceFunction(PrimitiveDistanceFunction<? super NumberVector<?>, D> distanceFunction) {
+  public void setDistanceFunction(PrimitiveDistanceFunction<? super NumberVector> distanceFunction) {
     innerkMeans.setDistanceFunction(distanceFunction);
   }
 
@@ -159,10 +150,9 @@ public class BestOfMultipleKMeans<V extends NumberVector<?>, D extends Distance<
    * @apiviz.exclude
    * 
    * @param <V> Vector type
-   * @param <D> Distance type
    * @param <M> Model type
    */
-  public static class Parameterizer<V extends NumberVector<?>, D extends Distance<D>, M extends MeanModel<V>> extends AbstractParameterizer {
+  public static class Parameterizer<V extends NumberVector, M extends MeanModel> extends AbstractParameterizer {
     /**
      * Parameter to specify the iterations of the bisecting step.
      */
@@ -186,12 +176,12 @@ public class BestOfMultipleKMeans<V extends NumberVector<?>, D extends Distance<
     /**
      * Variant of kMeans to use.
      */
-    protected KMeans<V, D, M> kMeansVariant;
+    protected KMeans<V, M> kMeansVariant;
 
     /**
      * Quality measure.
      */
-    protected KMeansQualityMeasure<? super V, ? super D> qualityMeasure;
+    protected KMeansQualityMeasure<? super V> qualityMeasure;
 
     @Override
     protected void makeOptions(Parameterization config) {
@@ -201,19 +191,19 @@ public class BestOfMultipleKMeans<V extends NumberVector<?>, D extends Distance<
         trials = trialsP.intValue();
       }
 
-      ObjectParameter<KMeans<V, D, M>> kMeansVariantP = new ObjectParameter<>(KMEANS_ID, KMeans.class);
+      ObjectParameter<KMeans<V, M>> kMeansVariantP = new ObjectParameter<>(KMEANS_ID, KMeans.class);
       if(config.grab(kMeansVariantP)) {
         kMeansVariant = kMeansVariantP.instantiateClass(config);
       }
 
-      ObjectParameter<KMeansQualityMeasure<V, ? super D>> qualityMeasureP = new ObjectParameter<>(QUALITYMEASURE_ID, KMeansQualityMeasure.class);
+      ObjectParameter<KMeansQualityMeasure<V>> qualityMeasureP = new ObjectParameter<>(QUALITYMEASURE_ID, KMeansQualityMeasure.class);
       if(config.grab(qualityMeasureP)) {
         qualityMeasure = qualityMeasureP.instantiateClass(config);
       }
     }
 
     @Override
-    protected BestOfMultipleKMeans<V, D, M> makeInstance() {
+    protected BestOfMultipleKMeans<V, M> makeInstance() {
       return new BestOfMultipleKMeans<>(trials, kMeansVariant, qualityMeasure);
     }
   }