Changeset 6633 for trunk/sources/HeuristicLab.Algorithms.DataAnalysis
- Timestamp:
- 08/04/11 11:22:57 (13 years ago)
- Location:
- trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/Linear
- Files:
-
- 2 edited
Legend:
- Unmodified
- Added
- Removed
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trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/Linear/MultinomialLogitClassification.cs
r6576 r6633 26 26 using HeuristicLab.Core; 27 27 using HeuristicLab.Data; 28 using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;29 28 using HeuristicLab.Optimization; 30 29 using HeuristicLab.Persistence.Default.CompositeSerializers.Storable; 31 30 using HeuristicLab.Problems.DataAnalysis; 32 using HeuristicLab.Problems.DataAnalysis.Symbolic;33 using HeuristicLab.Problems.DataAnalysis.Symbolic.Regression;34 31 35 32 namespace HeuristicLab.Algorithms.DataAnalysis { … … 59 56 } 60 57 61 #region logit regression58 #region logit classification 62 59 protected override void Run() { 63 60 double rmsError, relClassError; 64 61 var solution = CreateLogitClassificationSolution(Problem.ProblemData, out rmsError, out relClassError); 65 Results.Add(new Result(LogitClassificationModelResultName, "The l inear regression solution.", solution));66 Results.Add(new Result("Root mean square error", "The root of the mean of squared errors of the logit regression solution on the training set.", new DoubleValue(rmsError)));62 Results.Add(new Result(LogitClassificationModelResultName, "The logit classification solution.", solution)); 63 Results.Add(new Result("Root mean squared error", "The root of the mean of squared errors of the logit regression solution on the training set.", new DoubleValue(rmsError))); 67 64 Results.Add(new Result("Relative classification error", "Relative classification error on the training set (percentage of misclassified cases).", new PercentValue(relClassError))); 68 65 } -
trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/Linear/MultinomialLogitModel.cs
r6604 r6633 66 66 targetVariable = original.targetVariable; 67 67 allowedInputVariables = (string[])original.allowedInputVariables.Clone(); 68 this.classValues = (double[])original.classValues.Clone();68 classValues = (double[])original.classValues.Clone(); 69 69 } 70 70 public MultinomialLogitModel(alglib.logitmodel logitModel, string targetVariable, IEnumerable<string> allowedInputVariables, double[] classValues)
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