1 files changed, 97 insertions, 0 deletions
diff --git a/src/de/lmu/ifi/dbs/elki/datasource/filter/normalization/instancewise/HellingerHistogramNormalization.java b/src/de/lmu/ifi/dbs/elki/datasource/filter/normalization/instancewise/HellingerHistogramNormalization.java
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--- /dev/null
+++ b/src/de/lmu/ifi/dbs/elki/datasource/filter/normalization/instancewise/HellingerHistogramNormalization.java
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+package de.lmu.ifi.dbs.elki.datasource.filter.normalization.instancewise;
+
+/*
+ This file is part of ELKI:
+ Environment for Developing KDD-Applications Supported by Index-Structures
+
+ Copyright (C) 2014
+ 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.type.SimpleTypeInformation;
+import de.lmu.ifi.dbs.elki.data.type.TypeUtil;
+import de.lmu.ifi.dbs.elki.datasource.filter.normalization.AbstractStreamNormalization;
+import de.lmu.ifi.dbs.elki.utilities.Alias;
+import de.lmu.ifi.dbs.elki.utilities.optionhandling.AbstractParameterizer;
+
+/**
+ * Normalize histograms by scaling them to L1 norm 1, then taking the square
+ * root in each attribute.
+ * 
+ * Using Euclidean distance (linear kernel) and this transformation is the same
+ * as using Hellinger distance:
+ * {@link de.lmu.ifi.dbs.elki.distance.distancefunction.probabilistic.HellingerDistanceFunction}
+ * 
+ * @author Erich Schubert
+ * 
+ * @param <V> vector type
+ */
+@Alias({ "de.lmu.ifi.dbs.elki.datasource.filter.normalization.HellingerHistogramNormalization" })
+public class HellingerHistogramNormalization<V extends NumberVector> extends AbstractStreamNormalization<V> {
+  /**
+   * Static instance.
+   */
+  public static final HellingerHistogramNormalization<NumberVector> STATIC = new HellingerHistogramNormalization<>();
+
+  /**
+   * Constructor.
+   */
+  public HellingerHistogramNormalization() {
+    super();
+  }
+
+  @Override
+  protected V filterSingleObject(V featureVector) {
+    double[] data = new double[featureVector.getDimensionality()];
+    double sum = 0.;
+    for(int d = 0; d < data.length; ++d) {
+      data[d] = featureVector.doubleValue(d);
+      data[d] = data[d] > 0 ? data[d] : -data[d];
+      sum += data[d];
+    }
+    // Normalize and sqrt:
+    if(sum > 0.) {
+      for(int d = 0; d < data.length; ++d) {
+        if(data[d] > 0) {
+          data[d] = Math.sqrt(data[d] / sum);
+        }
+      }
+    }
+    return factory.newNumberVector(data);
+  }
+
+  @Override
+  protected SimpleTypeInformation<? super V> getInputTypeRestriction() {
+    return TypeUtil.NUMBER_VECTOR_VARIABLE_LENGTH;
+  }
+
+  /**
+   * Parameterization class.
+   * 
+   * @author Erich Schubert
+   * 
+   * @apiviz.exclude
+   */
+  public static class Parameterizer extends AbstractParameterizer {
+    @Override
+    protected HellingerHistogramNormalization<NumberVector> makeInstance() {
+      return STATIC;
+    }
+  }
+}
+\ No newline at end of file