Import Debian changes 0.5.0~beta2-1

elki (0.5.0~beta2-1) unstable; urgency=low

  * New upstream beta release.
  * Needs GNU Trove 3, in NEW.
  * Build with OpenJDK7, as OpenJDK6 complains.

elki (0.5.0~beta1-1) unstable; urgency=low

  * New upstream beta release.
  * Needs GNU Trove 3, not yet in Debian (private package)
  * Build with OpenJDK7, as OpenJDK6 complains.

1 files changed, 149 insertions, 0 deletions

diff --git a/src/de/lmu/ifi/dbs/elki/distance/distancefunction/subspace/SubspaceEuclideanDistanceFunction.java b/src/de/lmu/ifi/dbs/elki/distance/distancefunction/subspace/SubspaceEuclideanDistanceFunction.java
new file mode 100644
index 00000000..6330ed34
--- /dev/null
+++ b/src/de/lmu/ifi/dbs/elki/distance/distancefunction/subspace/SubspaceEuclideanDistanceFunction.java
@@ -0,0 +1,149 @@
+package de.lmu.ifi.dbs.elki.distance.distancefunction.subspace;
+
+/*
+ This file is part of ELKI:
+ Environment for Developing KDD-Applications Supported by Index-Structures
+
+ Copyright (C) 2012
+ Ludwig-Maximilians-Universität München
+ Lehr- und Forschungseinheit für Datenbanksysteme
+ ELKI Development Team
+
+ This program is free software: you can redistribute it and/or modify
+ it under the terms of the GNU Affero General Public License as published by
+ the Free Software Foundation, either version 3 of the License, or
+ (at your option) any later version.
+
+ This program is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+ MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ GNU Affero General Public License for more details.
+
+ You should have received a copy of the GNU Affero General Public License
+ along with this program.  If not, see <http://www.gnu.org/licenses/>.
+ */
+
+import java.util.BitSet;
+
+import de.lmu.ifi.dbs.elki.data.NumberVector;
+import de.lmu.ifi.dbs.elki.data.spatial.SpatialComparable;
+
+/**
+ * Provides a distance function that computes the Euclidean distance between
+ * feature vectors only in specified dimensions.
+ * 
+ * @author Elke Achtert
+ */
+public class SubspaceEuclideanDistanceFunction extends SubspaceLPNormDistanceFunction {
+  /**
+   * Constructor.
+   * 
+   * @param dimensions Selected dimensions
+   */
+  public SubspaceEuclideanDistanceFunction(BitSet dimensions) {
+    super(2.0, dimensions);
+  }
+
+  /**
+   * Provides the Euclidean distance between two given feature vectors in the
+   * selected dimensions.
+   * 
+   * @param v1 first feature vector
+   * @param v2 second feature vector
+   * @return the Euclidean distance between two given feature vectors in the
+   *         selected dimensions
+   */
+  @Override
+  public double doubleDistance(NumberVector<?, ?> v1, NumberVector<?, ?> v2) {
+    if(v1.getDimensionality() != v2.getDimensionality()) {
+      throw new IllegalArgumentException("Different dimensionality of FeatureVectors\n  " + "first argument: " + v1 + "\n  " + "second argument: " + v2);
+    }
+
+    double sqrDist = 0;
+    for(int d = dimensions.nextSetBit(0); d >= 0; d = dimensions.nextSetBit(d + 1)) {
+      final double delta = v1.doubleValue(d + 1) - v2.doubleValue(d + 1);
+      sqrDist += delta * delta;
+    }
+    return Math.sqrt(sqrDist);
+  }
+
+  @Override
+  protected double doubleMinDistObject(SpatialComparable mbr, NumberVector<?, ?> v) {
+    if(mbr.getDimensionality() != v.getDimensionality()) {
+      throw new IllegalArgumentException("Different dimensionality of objects\n  " + "first argument: " + mbr.toString() + "\n  " + "second argument: " + v.toString());
+    }
+
+    double sqrDist = 0;
+    for(int d = dimensions.nextSetBit(0); d >= 0; d = dimensions.nextSetBit(d + 1)) {
+      final double delta;
+      final double value = v.doubleValue(d + 1);
+      final double omin = mbr.getMin(d + 1);
+      if(value < omin) {
+        delta = omin - value;
+      }
+      else {
+        final double omax = mbr.getMax(d + 1);
+        if(value > omax) {
+          delta = value - omax;
+        }
+        else {
+          continue;
+        }
+      }
+      sqrDist += delta * delta;
+    }
+    return Math.sqrt(sqrDist);
+  }
+
+  @Override
+  public double doubleMinDist(SpatialComparable mbr1, SpatialComparable mbr2) {
+    if(mbr1.getDimensionality() != mbr2.getDimensionality()) {
+      throw new IllegalArgumentException("Different dimensionality of objects\n  " + "first argument: " + mbr1.toString() + "\n  " + "second argument: " + mbr2.toString());
+    }
+    double sqrDist = 0;
+    for(int d = dimensions.nextSetBit(0); d >= 0; d = dimensions.nextSetBit(d + 1)) {
+      final double delta;
+      final double max1 = mbr1.getMax(d + 1);
+      final double min2 = mbr2.getMin(d + 1);
+      if(max1 < min2) {
+        delta = min2 - max1;
+      }
+      else {
+        final double min1 = mbr1.getMin(d + 1);
+        final double max2 = mbr2.getMax(d + 1);
+        if(min1 > max2) {
+          delta = min1 - max2;
+        }
+        else { // The mbrs intersect!
+          continue;
+        }
+      }
+      sqrDist += delta * delta;
+    }
+    return Math.sqrt(sqrDist);
+  }
+
+  @Override
+  public double doubleNorm(NumberVector<?, ?> obj) {
+    double sqrDist = 0;
+    for(int d = dimensions.nextSetBit(0); d >= 0; d = dimensions.nextSetBit(d + 1)) {
+      final double delta = obj.doubleValue(d + 1);
+      sqrDist += delta * delta;
+    }
+    return Math.sqrt(sqrDist);
+  }
+
+  /**
+   * Parameterization class.
+   * 
+   * @author Erich Schubert
+   * 
+   * @apiviz.exclude
+   */
+  public static class Parameterizer extends AbstractDimensionsSelectingDoubleDistanceFunction.Parameterizer {
+    @Override
+    protected SubspaceEuclideanDistanceFunction makeInstance() {
+      return new SubspaceEuclideanDistanceFunction(dimensions);
+    }
+  }
+}
\ No newline at end of file