1 files changed, 9 insertions, 10 deletions
diff --git a/src/de/lmu/ifi/dbs/elki/algorithm/outlier/GaussianModel.java b/src/de/lmu/ifi/dbs/elki/algorithm/outlier/GaussianModel.java
index 13f047e7..ae47c100 100644
--- a/src/de/lmu/ifi/dbs/elki/algorithm/outlier/GaussianModel.java
+++ b/src/de/lmu/ifi/dbs/elki/algorithm/outlier/GaussianModel.java
@@ -3,7 +3,7 @@ package de.lmu.ifi.dbs.elki.algorithm.outlier;
 This file is part of ELKI:
 Environment for Developing KDD-Applications Supported by Index-Structures
 
-Copyright (C) 2011
+Copyright (C) 2012
 Ludwig-Maximilians-Universität München
 Lehr- und Forschungseinheit für Datenbanksysteme
 ELKI Development Team
@@ -28,8 +28,9 @@ import de.lmu.ifi.dbs.elki.data.type.TypeInformation;
 import de.lmu.ifi.dbs.elki.data.type.TypeUtil;
 import de.lmu.ifi.dbs.elki.database.datastore.DataStoreFactory;
 import de.lmu.ifi.dbs.elki.database.datastore.DataStoreUtil;
-import de.lmu.ifi.dbs.elki.database.datastore.WritableDataStore;
+import de.lmu.ifi.dbs.elki.database.datastore.WritableDoubleDataStore;
 import de.lmu.ifi.dbs.elki.database.ids.DBID;
+import de.lmu.ifi.dbs.elki.database.relation.MaterializedRelation;
 import de.lmu.ifi.dbs.elki.database.relation.Relation;
 import de.lmu.ifi.dbs.elki.logging.Logging;
 import de.lmu.ifi.dbs.elki.math.DoubleMinMax;
@@ -37,7 +38,6 @@ import de.lmu.ifi.dbs.elki.math.MathUtil;
 import de.lmu.ifi.dbs.elki.math.linearalgebra.CovarianceMatrix;
 import de.lmu.ifi.dbs.elki.math.linearalgebra.Matrix;
 import de.lmu.ifi.dbs.elki.math.linearalgebra.Vector;
-import de.lmu.ifi.dbs.elki.database.relation.MaterializedRelation;
 import de.lmu.ifi.dbs.elki.result.outlier.BasicOutlierScoreMeta;
 import de.lmu.ifi.dbs.elki.result.outlier.InvertedOutlierScoreMeta;
 import de.lmu.ifi.dbs.elki.result.outlier.OutlierResult;
@@ -95,11 +95,11 @@ public class GaussianModel<V extends NumberVector<V, ?>> extends AbstractAlgorit
   public OutlierResult run(Relation<V> relation) throws IllegalStateException {
     DoubleMinMax mm = new DoubleMinMax();
     // resulting scores
-    WritableDataStore<Double> oscores = DataStoreUtil.makeStorage(relation.getDBIDs(), DataStoreFactory.HINT_TEMP | DataStoreFactory.HINT_HOT, Double.class);
+    WritableDoubleDataStore oscores = DataStoreUtil.makeDoubleStorage(relation.getDBIDs(), DataStoreFactory.HINT_TEMP | DataStoreFactory.HINT_HOT);
 
     // Compute mean and covariance Matrix
     CovarianceMatrix temp = CovarianceMatrix.make(relation);
-    V mean = temp.getMeanVector(relation);
+    Vector mean = temp.getMeanVector(relation).getColumnVector();
     // debugFine(mean.toString());
     Matrix covarianceMatrix = temp.destroyToNaiveMatrix();
     // debugFine(covarianceMatrix.toString());
@@ -110,21 +110,20 @@ public class GaussianModel<V extends NumberVector<V, ?>> extends AbstractAlgorit
 
     // for each object compute Mahalanobis distance
     for(DBID id : relation.iterDBIDs()) {
-      V x = relation.get(id);
-      Vector x_minus_mean = x.minus(mean).getColumnVector();
+      Vector x = relation.get(id).getColumnVector().minusEquals(mean);
       // Gaussian PDF
-      final double mDist = x_minus_mean.transposeTimes(covarianceTransposed).times(x_minus_mean).get(0, 0);
+      final double mDist = x.transposeTimesTimes(covarianceTransposed, x);
       final double prob = fakt * Math.exp(-mDist / 2.0);
 
       mm.put(prob);
-      oscores.put(id, prob);
+      oscores.putDouble(id, prob);
     }
 
     final OutlierScoreMeta meta;
     if(invert) {
       double max = mm.getMax() != 0 ? mm.getMax() : 1.;
       for(DBID id : relation.iterDBIDs()) {
-        oscores.put(id, (max - oscores.get(id)) / max);
+        oscores.putDouble(id, (max - oscores.doubleValue(id)) / max);
       }
       meta = new BasicOutlierScoreMeta(0.0, 1.0);
     }