Changeset 12590 for branches/GBT-trunkintegration/HeuristicLab.Algorithms.DataAnalysis/3.4/GradientBoostedTrees/LossFunctions/SquaredErrorLoss.cs

Timestamp:

07/04/15 16:03:36 (9 years ago)

Author:

gkronber

Message:

#2261: preparations for trunk integration (adapt to current trunk version, add license headers, add comments, improve code quality)

File:

• branches/GBT-trunkintegration/HeuristicLab.Algorithms.DataAnalysis/3.4/GradientBoostedTrees/LossFunctions/SquaredErrorLoss.cs (modified) (2 diffs)

branches/GBT-trunkintegration/HeuristicLab.Algorithms.DataAnalysis/3.4/GradientBoostedTrees/LossFunctions/SquaredErrorLoss.cs

@@ -1 @@
-using System;
+#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 System.Linq;
-using System.Text;
-using System.Threading.Tasks;
-using HeuristicLab.Common;
-using HeuristicLab.Core;
 
-namespace GradientBoostedTrees {
+namespace HeuristicLab.Algorithms.DataAnalysis {
   public class SquaredErrorLoss : ILossFunction {
     public double GetLoss(IEnumerable<double> target, IEnumerable<double> pred, IEnumerable<double> weight) {
@@ -44 +62 @@
         throw new ArgumentException("target, pred and weight have differing lengths");
 
-      // line search for 
+      // line search for squared error loss => return the average value
       LineSearchFunc lineSearch = (idx, startIdx, endIdx) => {
         double s = 0.0;