1 files changed, 7 insertions, 13 deletions
diff --git a/src/de/lmu/ifi/dbs/elki/algorithm/clustering/onedimensional/KNNKernelDensityMinimaClustering.java b/src/de/lmu/ifi/dbs/elki/algorithm/clustering/onedimensional/KNNKernelDensityMinimaClustering.java
index 55114f7d..4e31f3a1 100644
--- a/src/de/lmu/ifi/dbs/elki/algorithm/clustering/onedimensional/KNNKernelDensityMinimaClustering.java
+++ b/src/de/lmu/ifi/dbs/elki/algorithm/clustering/onedimensional/KNNKernelDensityMinimaClustering.java
@@ -4,7 +4,7 @@ package de.lmu.ifi.dbs.elki.algorithm.clustering.onedimensional;
  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
@@ -59,7 +59,7 @@ import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameters.ObjectParameter;
  * 
  * @author Erich Schubert
  */
-public class KNNKernelDensityMinimaClustering<V extends NumberVector<?>> extends AbstractAlgorithm<Clustering<ClusterModel>> implements ClusteringAlgorithm<Clustering<ClusterModel>> {
+public class KNNKernelDensityMinimaClustering<V extends NumberVector> extends AbstractAlgorithm<Clustering<ClusterModel>> implements ClusteringAlgorithm<Clustering<ClusterModel>> {
   /**
    * Class logger.
    */
@@ -138,9 +138,7 @@ public class KNNKernelDensityMinimaClustering<V extends NumberVector<?>> extends
 
     StepProgress sprog = LOG.isVerbose() ? new StepProgress("Clustering steps", 2) : null;
 
-    if(sprog != null) {
-      sprog.beginStep(1, "Kernel density estimation.", LOG);
-    }
+    LOG.beginStep(sprog, 1, "Kernel density estimation.");
     {
       double[] scratch = new double[2 * k];
       iter.seek(0);
@@ -216,9 +214,7 @@ public class KNNKernelDensityMinimaClustering<V extends NumberVector<?>> extends
       }
     }
 
-    if(sprog != null) {
-      sprog.beginStep(2, "Local minima detection.", LOG);
-    }
+    LOG.beginStep(sprog, 2, "Local minima detection.");
     Clustering<ClusterModel> clustering = new Clustering<>("onedimensional-kde-clustering", "One-Dimensional clustering using kernel density estimation.");
     {
       double[] scratch = new double[2 * minwindow + 1];
@@ -270,15 +266,13 @@ public class KNNKernelDensityMinimaClustering<V extends NumberVector<?>> extends
       clustering.addToplevelCluster(new Cluster<>(cids, ClusterModel.CLUSTER));
     }
 
-    if(sprog != null) {
-      sprog.setCompleted(LOG);
-    }
+    LOG.ensureCompleted(sprog);
     return clustering;
   }
 
   @Override
   public TypeInformation[] getInputTypeRestriction() {
-    return TypeUtil.array(new VectorFieldTypeInformation<>(NumberVector.class, dim + 1, Integer.MAX_VALUE));
+    return TypeUtil.array(VectorFieldTypeInformation.typeRequest(NumberVector.class, dim + 1, Integer.MAX_VALUE));
   }
 
   @Override
@@ -293,7 +287,7 @@ public class KNNKernelDensityMinimaClustering<V extends NumberVector<?>> extends
    * 
    * @apiviz.exclude
    */
-  public static class Parameterizer<V extends NumberVector<?>> extends AbstractParameterizer {
+  public static class Parameterizer<V extends NumberVector> extends AbstractParameterizer {
     /**
      * Dimension to use for clustering.
      */