Import Debian changes 0.6.0~beta2-1

elki (0.6.0~beta2-1) unstable; urgency=low

  * New upstream beta release.
  * 3DPC extension is not yet included.

1 files changed, 166 insertions, 0 deletions

diff --git a/src/de/lmu/ifi/dbs/elki/distance/distancefunction/minkowski/SquaredEuclideanDistanceFunction.java b/src/de/lmu/ifi/dbs/elki/distance/distancefunction/minkowski/SquaredEuclideanDistanceFunction.java
new file mode 100644
index 00000000..8f02e40b
--- /dev/null
+++ b/src/de/lmu/ifi/dbs/elki/distance/distancefunction/minkowski/SquaredEuclideanDistanceFunction.java
@@ -0,0 +1,166 @@
+package de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski;
+
+/*
+ This file is part of ELKI:
+ Environment for Developing KDD-Applications Supported by Index-Structures
+
+ Copyright (C) 2013
+ 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 de.lmu.ifi.dbs.elki.data.NumberVector;
+import de.lmu.ifi.dbs.elki.data.spatial.SpatialComparable;
+import de.lmu.ifi.dbs.elki.distance.distancefunction.AbstractSpatialDoubleDistanceNorm;
+import de.lmu.ifi.dbs.elki.utilities.Alias;
+import de.lmu.ifi.dbs.elki.utilities.optionhandling.AbstractParameterizer;
+
+/**
+ * Provides the squared Euclidean distance for FeatureVectors. This results in
+ * the same rankings, but saves computing the square root as often.
+ * 
+ * @author Arthur Zimek
+ */
+@Alias({ "squaredeuclidean", "de.lmu.ifi.dbs.elki.distance.distancefunction.SquaredEuclideanDistanceFunction" })
+public class SquaredEuclideanDistanceFunction extends AbstractSpatialDoubleDistanceNorm {
+  /**
+   * Static instance. Use this!
+   */
+  public static final SquaredEuclideanDistanceFunction STATIC = new SquaredEuclideanDistanceFunction();
+
+  /**
+   * Provides a Euclidean distance function that can compute the Euclidean
+   * distance (that is a DoubleDistance) for FeatureVectors.
+   * 
+   * @deprecated Use static instance!
+   */
+  @Deprecated
+  public SquaredEuclideanDistanceFunction() {
+    super();
+  }
+
+  @Override
+  public double doubleDistance(NumberVector<?> v1, NumberVector<?> v2) {
+    final int dim = dimensionality(v1, v2);
+    double agg = 0.;
+    for (int d = 0; d < dim; d++) {
+      final double delta = v1.doubleValue(d) - v2.doubleValue(d);
+      agg += delta * delta;
+    }
+    return agg;
+  }
+
+  @Override
+  public double doubleNorm(NumberVector<?> v) {
+    final int dim = v.getDimensionality();
+    double agg = 0.;
+    for (int d = 0; d < dim; d++) {
+      final double val = v.doubleValue(d);
+      agg += val * val;
+    }
+    return agg;
+  }
+
+  protected double doubleMinDistObject(NumberVector<?> v, SpatialComparable mbr) {
+    final int dim = dimensionality(mbr, v);
+    double agg = 0.;
+    for (int d = 0; d < dim; d++) {
+      final double value = v.doubleValue(d), min = mbr.getMin(d);
+      final double diff;
+      if (value < min) {
+        diff = min - value;
+      } else {
+        final double max = mbr.getMax(d);
+        if (value > max) {
+          diff = value - max;
+        } else {
+          continue;
+        }
+      }
+      agg += diff * diff;
+    }
+    return agg;
+  }
+
+  @Override
+  public double doubleMinDist(SpatialComparable mbr1, SpatialComparable mbr2) {
+    // Some optimizations for simpler cases.
+    if (mbr1 instanceof NumberVector) {
+      if (mbr2 instanceof NumberVector) {
+        return doubleDistance((NumberVector<?>) mbr1, (NumberVector<?>) mbr2);
+      } else {
+        return doubleMinDistObject((NumberVector<?>) mbr1, mbr2);
+      }
+    } else if (mbr2 instanceof NumberVector) {
+      return doubleMinDistObject((NumberVector<?>) mbr2, mbr1);
+    }
+    final int dim = dimensionality(mbr1, mbr2);
+
+    double agg = 0.;
+    for (int d = 0; d < dim; d++) {
+      final double diff;
+      final double d1 = mbr2.getMin(d) - mbr1.getMax(d);
+      if (d1 > 0.) {
+        diff = d1;
+      } else {
+        final double d2 = mbr1.getMin(d) - mbr2.getMax(d);
+        if (d2 > 0.) {
+          diff = d2;
+        } else {
+          continue;
+        }
+      }
+      agg += diff * diff;
+    }
+    return agg;
+  }
+
+  @Override
+  public boolean isMetric() {
+    return false;
+  }
+
+  @Override
+  public String toString() {
+    return "SquaredEuclideanDistance";
+  }
+
+  @Override
+  public boolean equals(Object obj) {
+    if (obj == null) {
+      return false;
+    }
+    if (obj == this) {
+      return true;
+    }
+    return this.getClass().equals(obj.getClass());
+  }
+
+  /**
+   * Parameterization class.
+   * 
+   * @author Erich Schubert
+   * 
+   * @apiviz.exclude
+   */
+  public static class Parameterizer extends AbstractParameterizer {
+    @Override
+    protected SquaredEuclideanDistanceFunction makeInstance() {
+      return SquaredEuclideanDistanceFunction.STATIC;
+    }
+  }
+}