Index: stable/HeuristicLab.Algorithms.DataAnalysis/3.4/GradientBoostedTrees/LossFunctions/QuantileRegressionLoss.cs
===================================================================
--- stable/HeuristicLab.Algorithms.DataAnalysis/3.4/GradientBoostedTrees/LossFunctions/QuantileRegressionLoss.cs	(revision 13148)
+++ stable/HeuristicLab.Algorithms.DataAnalysis/3.4/GradientBoostedTrees/LossFunctions/QuantileRegressionLoss.cs	(revision 13148)
@@ -0,0 +1,118 @@
+﻿#region License Information
+/* HeuristicLab
+ * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ * and the BEACON Center for the Study of Evolution in Action.
+ * 
+ * This file is part of HeuristicLab.
+ *
+ * HeuristicLab is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * HeuristicLab 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 General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
+ */
+#endregion
+
+using System;
+using System.Collections.Generic;
+using HeuristicLab.Common;
+using HeuristicLab.Core;
+using HeuristicLab.Data;
+using HeuristicLab.Parameters;
+using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
+
+namespace HeuristicLab.Algorithms.DataAnalysis {
+  // loss function for quantile regression
+  // Generalized Boosted Models - A Guide To The gbm Package, Greg Ridgeway, August 2007, page 11 
+  [StorableClass]
+  [Item("QuantileRegressionloss", "Loss function for quantile regression")]
+  public sealed class QuantileRegressionLoss : ParameterizedNamedItem, ILossFunction {
+    public IFixedValueParameter<PercentValue> AlphaParameter {
+      get { return (IFixedValueParameter<PercentValue>)Parameters["Alpha"]; }
+    }
+
+    public double Alpha {
+      get { return AlphaParameter.Value.Value; }
+      set {
+        if (value <= 0.0 || value >= 1.0) throw new ArgumentException("Valid values for alpha: 0 < alpha < 1");
+        AlphaParameter.Value.Value = value;
+      }
+    }
+
+    public QuantileRegressionLoss()
+      : base("QuantileRegressionLoss", "Loss function for quantile regression") {
+      Parameters.Add(new FixedValueParameter<PercentValue>("Alpha", new PercentValue(0.9)));
+    }
+
+    public double GetLoss(IEnumerable<double> target, IEnumerable<double> pred) {
+      var targetEnum = target.GetEnumerator();
+      var predEnum = pred.GetEnumerator();
+      var alpha = Alpha;
+      double leftSum = 0;
+      double rightsum = 0;
+      while (targetEnum.MoveNext() & predEnum.MoveNext()) {
+        double res = targetEnum.Current - predEnum.Current;
+        if (res > 0) leftSum += res;
+        else rightsum += -res;
+      }
+      if (targetEnum.MoveNext() | predEnum.MoveNext())
+        throw new ArgumentException("target and pred have differing lengths");
+
+      return alpha * leftSum + (1 - alpha) * rightsum;
+    }
+
+    public IEnumerable<double> GetLossGradient(IEnumerable<double> target, IEnumerable<double> pred) {
+      var targetEnum = target.GetEnumerator();
+      var predEnum = pred.GetEnumerator();
+      var alpha = AlphaParameter.Value.Value;
+
+      while (targetEnum.MoveNext() & predEnum.MoveNext()) {
+        var res = targetEnum.Current - predEnum.Current;
+        if (res > 0) yield return alpha;
+        else if (res < 0) yield return -(1.0 - alpha);
+        else yield return 0.0;
+      }
+      if (targetEnum.MoveNext() | predEnum.MoveNext())
+        throw new ArgumentException("target and pred have differing lengths");
+    }
+
+    // targetArr and predArr are not changed by LineSearch
+    public double LineSearch(double[] targetArr, double[] predArr, int[] idx, int startIdx, int endIdx) {
+      if (targetArr.Length != predArr.Length)
+        throw new ArgumentException("target and pred have differing lengths");
+
+      // Quantile() is allocating an array anyway
+      // It would be possible to pre-allocated an array for the residuals if Quantile() would allow specification of a sub-range
+      int nRows = endIdx - startIdx + 1;
+      var res = new double[nRows];
+      for (int i = startIdx; i <= endIdx; i++) {
+        var row = idx[i];
+        res[i - startIdx] = targetArr[row] - predArr[row];
+      }
+      return res.Quantile(Alpha);
+    }
+
+    #region item implementation
+    [StorableConstructor]
+    private QuantileRegressionLoss(bool deserializing) : base(deserializing) { }
+
+    private QuantileRegressionLoss(QuantileRegressionLoss original, Cloner cloner)
+      : base(original, cloner) {
+    }
+
+    public override IDeepCloneable Clone(Cloner cloner) {
+      return new QuantileRegressionLoss(this, cloner);
+    }
+    #endregion
+
+    public override bool CanChangeName { get { return false; } }
+    public override bool CanChangeDescription { get { return false; } }
+  }
+}
