Changeset 17931 for trunk/HeuristicLab.Algorithms.DataAnalysis/3.4/RandomForest/RandomForestModel.cs

Timestamp:

04/09/21 19:41:33 (3 years ago)

Author:

gkronber

Message:

#3117: update alglib to version 3.17

File:

• trunk/HeuristicLab.Algorithms.DataAnalysis/3.4/RandomForest/RandomForestModel.cs (modified) (9 diffs)

trunk/HeuristicLab.Algorithms.DataAnalysis/3.4/RandomForest/RandomForestModel.cs

@@ -20 +20 @@
 #endregion
 
+extern alias alglib_3_7;
+
 using System;
 using System.Collections.Generic;
@@ -39 +41 @@
   public sealed class RandomForestModel : ClassificationModel, IRandomForestModel {
     // not persisted
-    private alglib.decisionforest randomForest;
-    private alglib.decisionforest RandomForest {
+    private alglib_3_7.alglib.decisionforest randomForest;
+    private alglib_3_7.alglib.decisionforest RandomForest {
       get {
         // recalculate lazily
@@ -74 +76 @@
     private RandomForestModel(StorableConstructorFlag _) : base(_) {
       // for backwards compatibility (loading old solutions)
-      randomForest = new alglib.decisionforest();
+      randomForest = new alglib_3_7.alglib.decisionforest();
     }
     private RandomForestModel(RandomForestModel original, Cloner cloner)
       : base(original, cloner) {
-      randomForest = new alglib.decisionforest();
+      randomForest = new alglib_3_7.alglib.decisionforest();
       randomForest.innerobj.bufsize = original.randomForest.innerobj.bufsize;
       randomForest.innerobj.nclasses = original.randomForest.innerobj.nclasses;
@@ -100 +102 @@
 
     // random forest models can only be created through the static factory methods CreateRegressionModel and CreateClassificationModel
-    private RandomForestModel(string targetVariable, alglib.decisionforest randomForest,
+    private RandomForestModel(string targetVariable, alglib_3_7.alglib.decisionforest randomForest,
       int seed, IDataAnalysisProblemData originalTrainingData,
       int nTrees, double r, double m, double[] classValues = null)
@@ -151 +153 @@
           x[column] = inputData[row, column];
         }
-        alglib.dfprocess(RandomForest, x, ref y);
+        alglib_3_7.alglib.dfprocess(RandomForest, x, ref y);
         yield return y[0];
       }
@@ -169 +171 @@
           x[column] = inputData[row, column];
         }
-        alglib.dforest.dfprocessraw(RandomForest.innerobj, x, ref ys);
+        alglib_3_7.alglib.dforest.dfprocessraw(RandomForest.innerobj, x, ref ys);
         yield return ys.VariancePop();
       }
@@ -187 +189 @@
           x[column] = inputData[row, column];
         }
-        alglib.dfprocess(randomForest, x, ref y);
+        alglib_3_7.alglib.dfprocess(randomForest, x, ref y);
         // find class for with the largest probability value
         int maxProbClassIndex = 0;
@@ -315 +317 @@
       double[,] inputMatrix = problemData.Dataset.ToArray(variables, trainingIndices);
 
-      alglib.dfreport rep;
-      var dForest = RandomForestUtil.CreateRandomForestModel(seed, inputMatrix, nTrees, r, m, 1, out rep);
+      var dForest = RandomForestUtil.CreateRandomForestModelAlglib_3_7(seed, inputMatrix, nTrees, r, m, 1, out var rep);
 
       rmsError = rep.rmserror;
@@ -353 +354 @@
       }
 
-      alglib.dfreport rep;
-      var dForest = RandomForestUtil.CreateRandomForestModel(seed, inputMatrix, nTrees, r, m, nClasses, out rep);
+      var dForest = RandomForestUtil.CreateRandomForestModelAlglib_3_7(seed, inputMatrix, nTrees, r, m, nClasses, out var rep);
 
       rmsError = rep.rmserror;