Changeset 3884 for trunk/sources/HeuristicLab.ExtLibs

Timestamp:

06/01/10 17:58:03 (15 years ago)

Author:

gkronber

Message:

Worked on support vector regression operators and views. #1009

Location:

trunk/sources/HeuristicLab.ExtLibs/HeuristicLab.LibSVM/1.6.3/LibSVM-1.6.3

Files:

• Model.cs (modified) (1 diff)
• ParameterSelection.cs (modified) (1 diff)
• Solver.cs (modified) (3 diffs)
• Training.cs (modified) (3 diffs)

trunk/sources/HeuristicLab.ExtLibs/HeuristicLab.LibSVM/1.6.3/LibSVM-1.6.3/Model.cs

@@ -217 +217 @@
       model.ClassLabels = null;
       model.NumberOfSVPerClass = null;
+      model.SupportVectorIndizes = new int[0];
 
       bool headerFinished = false;

trunk/sources/HeuristicLab.ExtLibs/HeuristicLab.LibSVM/1.6.3/LibSVM-1.6.3/ParameterSelection.cs

@@ -150 +150 @@
                     parameters.C = CValues[i];
                     parameters.Gamma = GammaValues[j];
-                    double test = Training.PerformCrossValidation(problem, parameters, nrfold);
+                    double test = Training.PerformCrossValidation(problem, parameters, nrfold, true);
                     Console.Write("{0} {1} {2}", parameters.C, parameters.Gamma, test);
                     if(output != null)

trunk/sources/HeuristicLab.ExtLibs/HeuristicLab.LibSVM/1.6.3/LibSVM-1.6.3/Solver.cs

@@ -1555 +1555 @@
             Parameter newparam = (Parameter)param.Clone();
             newparam.Probability = false;
-            svm_cross_validation(prob, newparam, nr_fold, ymv);
+            svm_cross_validation(prob, newparam, nr_fold, ymv, true);
             for (i = 0; i < prob.Count; i++)
             {
@@ -1872 +1872 @@
 
         // Stratified cross validation
-        public static void svm_cross_validation(Problem prob, Parameter param, int nr_fold, double[] target)
+        public static void svm_cross_validation(Problem prob, Parameter param, int nr_fold, double[] target, bool shuffleTraining)
         {
             Random rand = new Random();
@@ -1936 +1936 @@
             {
                 for (i = 0; i < l; i++) perm[i] = i;
-                for (i = 0; i < l; i++)
-                {
+                if (shuffleTraining) {
+                  for (i = 0; i < l; i++) {
                     int j = i + (int)(rand.NextDouble() * (l - i));
                     do { int _ = perm[i]; perm[i] = perm[j]; perm[j] = _; } while (false);
+                  }
                 }
                 for (i = 0; i <= nr_fold; i++)

trunk/sources/HeuristicLab.ExtLibs/HeuristicLab.LibSVM/1.6.3/LibSVM-1.6.3/Training.cs

@@ -43 +43 @@
         }
 
-        private static double doCrossValidation(Problem problem, Parameter parameters, int nr_fold)
+        private static double doCrossValidation(Problem problem, Parameter parameters, int nr_fold, bool shuffleTraining)
         {
             int i;
             double[] target = new double[problem.Count];
-            Procedures.svm_cross_validation(problem, parameters, nr_fold, target);
+            Procedures.svm_cross_validation(problem, parameters, nr_fold, target, shuffleTraining);
             int total_correct = 0;
             double total_error = 0;
@@ -87 +87 @@
             parseCommandLine(args, out parameters, out problem, out crossValidation, out nrfold, out modelFilename);
             if (crossValidation)
-                PerformCrossValidation(problem, parameters, nrfold);
+                PerformCrossValidation(problem, parameters, nrfold, true);
             else Model.Write(modelFilename, Train(problem, parameters));
         }
@@ -98 +98 @@
         /// <param name="nrfold">The number of cross validations to use</param>
         /// <returns>The cross validation score</returns>
-        public static double PerformCrossValidation(Problem problem, Parameter parameters, int nrfold)
+        public static double PerformCrossValidation(Problem problem, Parameter parameters, int nrfold, bool shuffleTraining)
         {
             string error = Procedures.svm_check_parameter(problem, parameters);
             if (error == null)
-                return doCrossValidation(problem, parameters, nrfold);
+                return doCrossValidation(problem, parameters, nrfold, shuffleTraining);
             else throw new Exception(error);
         }