Changeset 6649 for trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/Linear

Timestamp:

08/09/11 18:58:09 (13 years ago)

Author:

gkronber

Message:

#1615 clone problem before creating a solution

Location:

trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/Linear

Files:

• LinearDiscriminantAnalysis.cs (modified) (1 diff)
• LinearRegression.cs (modified) (1 diff)
• MultinomialLogitClassification.cs (modified) (1 diff)

trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/Linear/LinearDiscriminantAnalysis.cs

@@ -106 +106 @@
 
       var model = LinearDiscriminantAnalysis.CreateDiscriminantFunctionModel(tree, new SymbolicDataAnalysisExpressionTreeInterpreter(), problemData, rows);
-      SymbolicDiscriminantFunctionClassificationSolution solution = new SymbolicDiscriminantFunctionClassificationSolution(model, problemData);
+      SymbolicDiscriminantFunctionClassificationSolution solution = new SymbolicDiscriminantFunctionClassificationSolution(model, (IClassificationProblemData)problemData.Clone());
 
       return solution;

trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/Linear/LinearRegression.cs

@@ -110 +110 @@
       addition.AddSubtree(cNode);
 
-      SymbolicRegressionSolution solution = new SymbolicRegressionSolution(new SymbolicRegressionModel(tree, new SymbolicDataAnalysisExpressionTreeInterpreter()), problemData);
+      SymbolicRegressionSolution solution = new SymbolicRegressionSolution(new SymbolicRegressionModel(tree, new SymbolicDataAnalysisExpressionTreeInterpreter()), (IRegressionProblemData)problemData.Clone());
       solution.Model.Name = "Linear Regression Model";
       return solution;

trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/Linear/MultinomialLogitClassification.cs

@@ -95 +95 @@
       relClassError = alglib.mnlrelclserror(lm, inputMatrix, nRows);
 
-      MultinomialLogitClassificationSolution solution = new MultinomialLogitClassificationSolution(problemData, new MultinomialLogitModel(lm, targetVariable, allowedInputVariables, classValues));
+      MultinomialLogitClassificationSolution solution = new MultinomialLogitClassificationSolution((IClassificationProblemData)problemData.Clone(), new MultinomialLogitModel(lm, targetVariable, allowedInputVariables, classValues));
       return solution;
     }