1 files changed, 6 insertions, 6 deletions
diff --git a/src/de/lmu/ifi/dbs/elki/distance/similarityfunction/kernel/SigmoidKernelFunction.java b/src/de/lmu/ifi/dbs/elki/distance/similarityfunction/kernel/SigmoidKernelFunction.java
index a88225f9..11f33f16 100644
--- a/src/de/lmu/ifi/dbs/elki/distance/similarityfunction/kernel/SigmoidKernelFunction.java
+++ b/src/de/lmu/ifi/dbs/elki/distance/similarityfunction/kernel/SigmoidKernelFunction.java
@@ -4,7 +4,7 @@ package de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel;
  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
@@ -24,8 +24,8 @@ package de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel;
  */
 
 import de.lmu.ifi.dbs.elki.data.NumberVector;
-import de.lmu.ifi.dbs.elki.distance.distancefunction.AbstractVectorDoubleDistanceFunction;
-import de.lmu.ifi.dbs.elki.distance.similarityfunction.AbstractVectorDoubleSimilarityFunction;
+import de.lmu.ifi.dbs.elki.distance.distancefunction.AbstractNumberVectorDistanceFunction;
+import de.lmu.ifi.dbs.elki.distance.similarityfunction.AbstractVectorSimilarityFunction;
 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.parameterization.Parameterization;
@@ -37,7 +37,7 @@ import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameters.DoubleParameter;
  * 
  * @author Erich Schubert
  */
-public class SigmoidKernelFunction extends AbstractVectorDoubleSimilarityFunction {
+public class SigmoidKernelFunction extends AbstractVectorSimilarityFunction {
   /**
    * Scaling factor c, bias theta
    */
@@ -56,8 +56,8 @@ public class SigmoidKernelFunction extends AbstractVectorDoubleSimilarityFunctio
   }
 
   @Override
-  public double doubleSimilarity(NumberVector<?> o1, NumberVector<?> o2) {
-    final int dim = AbstractVectorDoubleDistanceFunction.dimensionality(o1, o2);
+  public double similarity(NumberVector o1, NumberVector o2) {
+    final int dim = AbstractNumberVectorDistanceFunction.dimensionality(o1, o2);
     double sim = 0.;
     for (int i = 0; i < dim; i++) {
       final double v = o1.doubleValue(i) * o2.doubleValue(i);