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diff --git a/src/de/lmu/ifi/dbs/elki/math/statistics/distribution/GeneralizedExtremeValueDistribution.java b/src/de/lmu/ifi/dbs/elki/math/statistics/distribution/GeneralizedExtremeValueDistribution.java
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+package de.lmu.ifi.dbs.elki.math.statistics.distribution;
+
+import java.util.Random;
+
+/*
+ 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/>.
+ */
+
+/**
+ * Generalized Extreme Value (GEV) distribution, also known as Fisher–Tippett
+ * distribution.
+ * 
+ * This is a generalization of the Frechnet, Gumbel and (reversed) Weibull
+ * distributions.
+ * 
+ * Implementation notice: apparently (see unit tests), our definition differs
+ * from the scipy definition by having the negative shape.
+ * 
+ * @author Erich Schubert
+ */
+public class GeneralizedExtremeValueDistribution implements Distribution {
+  /**
+   * Parameters (location, scale, shape)
+   */
+  final double mu, sigma, k;
+
+  /**
+   * Random number generator.
+   */
+  Random random;
+
+  /**
+   * Constructor.
+   * 
+   * @param mu Location parameter mu
+   * @param sigma Scale parameter sigma
+   * @param k Shape parameter k
+   */
+  public GeneralizedExtremeValueDistribution(double mu, double sigma, double k) {
+    this(mu, sigma, k, null);
+  }
+
+  /**
+   * Constructor.
+   * 
+   * @param mu Location parameter mu
+   * @param sigma Scale parameter sigma
+   * @param k Shape parameter k
+   * @param random Random number generator
+   */
+  public GeneralizedExtremeValueDistribution(double mu, double sigma, double k, Random random) {
+    super();
+    this.mu = mu;
+    this.sigma = sigma;
+    this.k = k;
+    this.random = random;
+  }
+
+  /**
+   * PDF of GEV distribution
+   * 
+   * @param x Value
+   * @param mu Location parameter mu
+   * @param sigma Scale parameter sigma
+   * @param k Shape parameter k
+   * @return PDF at position x.
+   */
+  public static double pdf(double x, double mu, double sigma, double k) {
+    x = (x - mu) / sigma;
+    if (k > 0 || k < 0) {
+      if (k * x < -1) {
+        return 0.;
+      }
+      final double tx = Math.pow(1 + k * x, -1. / k);
+      return Math.pow(tx, k + 1) * Math.exp(-tx) / sigma;
+    } else { // Gumbel case:
+      return Math.exp(-x - Math.exp(-x)) / sigma;
+    }
+  }
+
+  @Override
+  public double pdf(double x) {
+    return pdf(x, mu, sigma, k);
+  }
+
+  /**
+   * CDF of GEV distribution
+   * 
+   * @param val Value
+   * @param mu Location parameter mu
+   * @param sigma Scale parameter sigma
+   * @param k Shape parameter k
+   * @return CDF at position x.
+   */
+  public static double cdf(double val, double mu, double sigma, double k) {
+    final double x = (val - mu) / sigma;
+    if (k > 0 || k < 0) {
+      if (k * x <= -1) {
+        return (k > 0) ? 0 : 1;
+      }
+      return Math.exp(-Math.pow(1 + k * x, -1. / k));
+    } else { // Gumbel case:
+      return Math.exp(-Math.exp(-x));
+    }
+  }
+
+  @Override
+  public double cdf(double val) {
+    return cdf(val, mu, sigma, k);
+  }
+
+  /**
+   * Quantile function of GEV distribution
+   * 
+   * @param val Value
+   * @param mu Location parameter mu
+   * @param sigma Scale parameter sigma
+   * @param k Shape parameter k
+   * @return Quantile function at position x.
+   */
+  public static double quantile(double val, double mu, double sigma, double k) {
+    if (val < 0.0 || val > 1.0) {
+      return Double.NaN;
+    }
+    if (k > 0) {
+      return mu + sigma * Math.max((Math.pow(-Math.log(val), -k) - 1.) / k, -1. / k);
+    } else if (k < 0) {
+      return mu + sigma * Math.min((Math.pow(-Math.log(val), -k) - 1.) / k, -1. / k);
+    } else { // Gumbel
+      return mu + sigma * Math.log(1. / Math.log(1. / val));
+    }
+  }
+
+  @Override
+  public double quantile(double val) {
+    return quantile(val, mu, sigma, k);
+  }
+
+  @Override
+  public double nextRandom() {
+    return quantile(random.nextDouble());
+  }
+
+  @Override
+  public String toString() {
+    return "GeneralizedExtremeValueDistribution(sigma=" + sigma + ", mu=" + mu + ", k=" + k + ")";
+  }
+}