1 files changed, 9 insertions, 9 deletions
diff --git a/src/de/lmu/ifi/dbs/elki/distance/similarityfunction/kernel/LinearKernelFunction.java b/src/de/lmu/ifi/dbs/elki/distance/similarityfunction/kernel/LinearKernelFunction.java
index a86ad55d..6a5b0a79 100644
--- a/src/de/lmu/ifi/dbs/elki/distance/similarityfunction/kernel/LinearKernelFunction.java
+++ b/src/de/lmu/ifi/dbs/elki/distance/similarityfunction/kernel/LinearKernelFunction.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
@@ -24,12 +24,12 @@ 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.distancefunction.AbstractNumberVectorDistanceFunction;
 import de.lmu.ifi.dbs.elki.utilities.optionhandling.AbstractParameterizer;
 
 /**
- * Provides a linear Kernel function that computes a similarity between the two
- * feature vectors V1 and V2 defined by V1^T*V2.
+ * Linear Kernel function that computes a similarity between the two feature
+ * vectors V1 and V2 defined by V1^T*V2.
  * 
  * Note: this is effectively equivalent to using
  * {@link de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski.EuclideanDistanceFunction}
@@ -51,18 +51,18 @@ public class LinearKernelFunction extends PolynomialKernelFunction {
   }
 
   @Override
-  public double doubleSimilarity(final NumberVector<?> o1, final NumberVector<?> o2) {
-    final int dim = AbstractVectorDoubleDistanceFunction.dimensionality(o1, o2);
+  public double similarity(final NumberVector o1, final NumberVector o2) {
+    final int dim = AbstractNumberVectorDistanceFunction.dimensionality(o1, o2);
     double sim = 0.;
-    for (int i = 0; i < dim; i++) {
+    for(int i = 0; i < dim; i++) {
       sim += o1.doubleValue(i) * o2.doubleValue(i);
     }
     return sim;
   }
 
   @Override
-  public double doubleDistance(final NumberVector<?> fv1, final NumberVector<?> fv2) {
-    return Math.sqrt(doubleSimilarity(fv1, fv1) + doubleSimilarity(fv2, fv2) - 2 * doubleSimilarity(fv1, fv2));
+  public double distance(final NumberVector fv1, final NumberVector fv2) {
+    return Math.sqrt(similarity(fv1, fv1) + similarity(fv2, fv2) - 2 * similarity(fv1, fv2));
   }
 
   /**