1 files changed, 110 insertions, 0 deletions
diff --git a/elki/src/main/java/de/lmu/ifi/dbs/elki/distance/similarityfunction/kernel/SigmoidKernelFunction.java b/elki/src/main/java/de/lmu/ifi/dbs/elki/distance/similarityfunction/kernel/SigmoidKernelFunction.java
new file mode 100644
index 00000000..82eb5ea6
--- /dev/null
+++ b/elki/src/main/java/de/lmu/ifi/dbs/elki/distance/similarityfunction/kernel/SigmoidKernelFunction.java
@@ -0,0 +1,110 @@
+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) 2015
+ 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.distance.distancefunction.AbstractNumberVectorDistanceFunction;
+import de.lmu.ifi.dbs.elki.distance.similarityfunction.AbstractVectorSimilarityFunction;
+import de.lmu.ifi.dbs.elki.utilities.optionhandling.AbstractParameterizer;
+import de.lmu.ifi.dbs.elki.utilities.optionhandling.OptionID;
+import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameterization.Parameterization;
+import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameters.DoubleParameter;
+
+/**
+ * Sigmoid kernel function (aka: hyperbolic tangent kernel, multilayer
+ * perceptron MLP kernel).
+ * 
+ * @author Erich Schubert
+ */
+public class SigmoidKernelFunction extends AbstractVectorSimilarityFunction {
+  /**
+   * Scaling factor c, bias theta
+   */
+  private final double c, theta;
+
+  /**
+   * Constructor.
+   * 
+   * @param c Scaling factor c.
+   * @param theta Bias parameter theta.
+   */
+  public SigmoidKernelFunction(double c, double theta) {
+    super();
+    this.c = c;
+    this.theta = theta;
+  }
+
+  @Override
+  public double similarity(NumberVector o1, NumberVector o2) {
+    final int dim = AbstractNumberVectorDistanceFunction.dimensionality(o1, o2);
+    double sim = 0.;
+    for (int i = 0; i < dim; i++) {
+      final double v = o1.doubleValue(i) * o2.doubleValue(i);
+      sim += v;
+    }
+    return Math.tanh(c * sim + theta);
+  }
+
+  /**
+   * Parameterization class.
+   * 
+   * @author Erich Schubert
+   * 
+   * @apiviz.exclude
+   */
+  public static class Parameterizer extends AbstractParameterizer {
+    /**
+     * C parameter: scaling
+     */
+    public static final OptionID C_ID = new OptionID("kernel.sigmoid.c", "Sigmoid c parameter (scaling).");
+
+    /**
+     * Theta parameter: bias
+     */
+    public static final OptionID THETA_ID = new OptionID("kernel.sigmoid.theta", "Sigmoid theta parameter (bias).");
+
+    /**
+     * C parameter, theta parameter
+     */
+    protected double c = 1., theta = 0.;
+
+    @Override
+    protected void makeOptions(Parameterization config) {
+      super.makeOptions(config);
+      final DoubleParameter cP = new DoubleParameter(C_ID, 1.);
+      if (config.grab(cP)) {
+        c = cP.doubleValue();
+      }
+      final DoubleParameter thetaP = new DoubleParameter(THETA_ID, 0.);
+      if (config.grab(thetaP)) {
+        theta = thetaP.doubleValue();
+      }
+    }
+
+    @Override
+    protected SigmoidKernelFunction makeInstance() {
+      return new SigmoidKernelFunction(c, theta);
+    }
+  }
+}