1 files changed, 130 insertions, 0 deletions
diff --git a/src/de/lmu/ifi/dbs/elki/math/statistics/distribution/estimator/LogNormalLMMEstimator.java b/src/de/lmu/ifi/dbs/elki/math/statistics/distribution/estimator/LogNormalLMMEstimator.java
new file mode 100644
index 00000000..48865d3f
--- /dev/null
+++ b/src/de/lmu/ifi/dbs/elki/math/statistics/distribution/estimator/LogNormalLMMEstimator.java
@@ -0,0 +1,130 @@
+package de.lmu.ifi.dbs.elki.math.statistics.distribution.estimator;
+
+/*
+ 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.math.MathUtil;
+import de.lmu.ifi.dbs.elki.math.statistics.distribution.LogNormalDistribution;
+import de.lmu.ifi.dbs.elki.math.statistics.distribution.NormalDistribution;
+import de.lmu.ifi.dbs.elki.utilities.documentation.Reference;
+import de.lmu.ifi.dbs.elki.utilities.optionhandling.AbstractParameterizer;
+
+/**
+ * Estimate the parameters of a log Normal Distribution, using the methods of
+ * L-Moments (LMM) for the Generalized Normal Distribution.
+ * 
+ * Reference:
+ * <p>
+ * J. R. M. Hosking<br />
+ * Fortran routines for use with the method of L-moments Version 3.03<br />
+ * IBM Research.
+ * </p>
+ * 
+ * @author Erich Schubert
+ * 
+ * @apiviz.has LogNormalDistribution
+ */
+@Reference(authors = "J.R.M. Hosking", title = "Fortran routines for use with the method of L-moments Version 3.03", booktitle = "IBM Research Technical Report")
+public class LogNormalLMMEstimator extends AbstractLMMEstimator<LogNormalDistribution> {
+  /**
+   * Static instance.
+   */
+  public static final LogNormalLMMEstimator STATIC = new LogNormalLMMEstimator();
+
+  /** Polynomial approximation */
+  private static final double //
+      A0 = 0.20466534e+01, //
+      A1 = -0.36544371e+01, //
+      A2 = 0.18396733e+01, //
+      A3 = -0.20360244;
+
+  /** Polynomial approximation */
+  private static final double //
+      B1 = -0.20182173e+01, //
+      B2 = 0.12420401e+01, //
+      B3 = -0.21741801;
+
+  /**
+   * Constructor. Private: use static instance.
+   */
+  private LogNormalLMMEstimator() {
+    super();
+  }
+
+  @Override
+  public int getNumMoments() {
+    return 3;
+  }
+
+  @Override
+  public LogNormalDistribution estimateFromLMoments(double[] xmom) {
+    // Note: the third condition probably is okay for Generalized Normal, but
+    // not for lognormal estimation.
+    if (!(xmom[1] > 0.) || !(Math.abs(xmom[2]) < 1.0) || !(xmom[2] > 0.0)) {
+      throw new ArithmeticException("L-Moments invalid");
+    }
+    // Generalized Normal Distribution estimation:
+    double t3 = xmom[2];
+    final double location, scale, shape;
+    if (Math.abs(t3) >= .95) {
+      // Extreme skewness
+      location = 0.;
+      scale = -1;
+      shape = 0.;
+    } else if (Math.abs(t3) < 1e-8) {
+      // t3 effectively zero.
+      location = xmom[0];
+      scale = xmom[1] * MathUtil.SQRTPI;
+      shape = 0.;
+    } else {
+      final double tt = t3 * t3;
+      shape = -t3 * (A0 + tt * (A1 + tt * (A2 + tt * A3))) / (1. + tt * (B1 + tt * (B2 + tt * B3)));
+      final double e = Math.exp(.5 * shape * shape);
+      scale = xmom[1] * shape / (e * NormalDistribution.erf(.5 * shape));
+      location = xmom[0] + scale * (e - 1.) / shape;
+    }
+    // Estimate logNormal from generalized normal:
+    final double sigma = -shape;
+    final double expmu = scale / sigma;
+    return new LogNormalDistribution(Math.log(expmu), Math.max(sigma, Double.MIN_NORMAL), location - expmu);
+  }
+
+  @Override
+  public Class<? super LogNormalDistribution> getDistributionClass() {
+    return LogNormalDistribution.class;
+  }
+
+  /**
+   * Parameterization class.
+   * 
+   * @author Erich Schubert
+   * 
+   * @apiviz.exclude
+   */
+  public static class Parameterizer extends AbstractParameterizer {
+    @Override
+    protected LogNormalLMMEstimator makeInstance() {
+      return STATIC;
+    }
+  }
+}