Changeset 4112


Ignore:
Timestamp:
07/27/10 14:36:27 (3 years ago)
Author:
gkronber
Message:

Fixed some bugs in multi-variate regression classes. #1089

Location:
trunk/sources
Files:
9 edited
1 moved

Legend:

Unmodified
Added
Removed

  • trunk/sources/HeuristicLab.Problems.DataAnalysis.MultiVariate.Regression/3.3/Symbolic/Analyzer/ValidationBestScaledSymbolicVectorRegressionSolutionAnalyzer.cs

    r4068 r4112  
    145145      if (Alpha.Length == trees.Length) { 
    146146        scaledTrees = from i in Enumerable.Range(0, trees.Length) 
    147                       select SymbolicVectorRegressionSolutionLinearScaler.Scale(trees[i], Alpha[i].ToArray(), Beta[i].ToArray()); 
     147                      select SymbolicVectorRegressionSolutionLinearScaler.Scale(trees[i], Beta[i].ToArray(), Alpha[i].ToArray()); 
    148148      } else { 
    149149        scaledTrees = trees; 

  • trunk/sources/HeuristicLab.Problems.DataAnalysis.MultiVariate.Regression/3.3/Symbolic/Evaluators/SymbolicRegressionNormalizedMeanSquaredErrorEvaluator.cs

    r4068 r4112  
    6565 
    6666    public static double Calculate(ISymbolicExpressionTreeInterpreter interpreter, SymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, Dataset dataset, string targetVariable, IEnumerable<int> rows) { 
    67       int targetVariableIndex = dataset.GetVariableIndex(targetVariable); 
    68       var estimatedValues = interpreter.GetSymbolicExpressionTreeValues(solution, dataset, rows); 
    69       var originalValues = dataset.GetEnumeratedVariableValues(targetVariableIndex, rows); 
    70       IEnumerator<double> originalEnumerator = originalValues.GetEnumerator(); 
    71       IEnumerator<double> estimatedEnumerator = estimatedValues.GetEnumerator(); 
    72       OnlineNormalizedMeanSquaredErrorEvaluator mseEvaluator = new OnlineNormalizedMeanSquaredErrorEvaluator(); 
    73  
    74       while (originalEnumerator.MoveNext() & estimatedEnumerator.MoveNext()) { 
    75         double estimated = estimatedEnumerator.Current; 
    76         double original = originalEnumerator.Current; 
    77         if (double.IsNaN(estimated)) 
    78           estimated = upperEstimationLimit; 
    79         else 
    80           estimated = Math.Min(upperEstimationLimit, Math.Max(lowerEstimationLimit, estimated)); 
    81         mseEvaluator.Add(original, estimated); 
    82       } 
    83  
    84       if (estimatedEnumerator.MoveNext() || originalEnumerator.MoveNext()) { 
    85         throw new ArgumentException("Number of elements in original and estimated enumeration doesn't match."); 
    86       } else { 
    87         return mseEvaluator.NormalizedMeanSquaredError; 
    88       } 
     67      return SymbolicRegressionScaledNormalizedMeanSquaredErrorEvaluator.CalculateWithScaling(interpreter, solution, lowerEstimationLimit, upperEstimationLimit, dataset, targetVariable, rows, 1.0, 0.0); 
    8968    } 
    9069  } 

  • trunk/sources/HeuristicLab.Problems.DataAnalysis.MultiVariate.Regression/3.3/Symbolic/Evaluators/SymbolicVectorRegressionNormalizedMseEvaluator.cs

    r4068 r4112  
    7373        tree.Root.SubTrees[0].AddSubTree(treeNode); 
    7474      } 
    75       return nmseSum / targetVariablesList.Count; 
     75      return nmseSum; 
    7676    } 
    7777  } 

  • trunk/sources/HeuristicLab.Problems.DataAnalysis.MultiVariate.Regression/3.3/Symbolic/Evaluators/SymbolicVectorRegressionScaledMseEvaluator.cs

    r4068 r4112  
    7575        double mse = SymbolicRegressionScaledMeanSquaredErrorEvaluator.Calculate(interpreter, tree, 
    7676          lowerEstimationBound[i], upperEstimationBound[i], 
    77           problemData.Dataset, targetVariablesList[i], rows, out compAlpha, out compBeta); 
     77          problemData.Dataset, targetVariablesList[i], rows, out compBeta, out compAlpha); 
    7878 
    7979        qualities[i] = mse; 

  • trunk/sources/HeuristicLab.Problems.DataAnalysis.MultiVariate.Regression/3.3/Symbolic/Evaluators/SymbolicVectorRegressionScaledNormalizedMseEvaluator.cs

    r4087 r4112  
    8787      AlphaParameter.ActualValue = alpha; 
    8888      BetaParameter.ActualValue = beta; 
    89       QualityParameter.ActualValue = new DoubleValue(nmseSum / targetVariables.Count()); 
     89      QualityParameter.ActualValue = new DoubleValue(nmseSum); 
    9090    } 
    9191  } 

  • trunk/sources/HeuristicLab.Problems.DataAnalysis.MultiVariate.Regression/3.3/Symbolic/SymbolicVectorRegressionSolutionLinearScaler.cs

    r4068 r4112  
    6868      DoubleArray beta = BetaParameter.ActualValue; 
    6969      if (alpha != null && beta != null) { 
    70         ScaledSymbolicExpressionTreeParameter.ActualValue = Scale(tree, alpha.ToArray(), beta.ToArray()); 
     70        ScaledSymbolicExpressionTreeParameter.ActualValue = Scale(tree, beta.ToArray(), alpha.ToArray()); 
    7171      } else { 
    7272        // alpha or beta parameter not available => do not scale tree 
    … …  
    7777    } 
    7878 
    79     public static SymbolicExpressionTree Scale(SymbolicExpressionTree original, double[] alpha, double[] beta) { 
     79    public static SymbolicExpressionTree Scale(SymbolicExpressionTree original, double[] beta, double[] alpha) { 
    8080      List<SymbolicExpressionTreeNode> resultProducingBranches = new List<SymbolicExpressionTreeNode>(original.Root.SubTrees[0].SubTrees); 
    8181      // remove the main branch before cloning to prevent cloning of sub-trees 

  • trunk/sources/HeuristicLab.Problems.DataAnalysis.MultiVariate/3.3/MultiVariateDataAnalysisProblem.cs

    r4098 r4112  
    5151 
    5252    [StorableConstructor] 
    53     private MultiVariateDataAnalysisProblem(bool deserializing) : base() { } 
     53    protected MultiVariateDataAnalysisProblem(bool deserializing) : base(deserializing) { } 
    5454    public MultiVariateDataAnalysisProblem() 
    5555      : base() { 

  • trunk/sources/HeuristicLab.Problems.DataAnalysis.Regression/3.3/HeuristicLab.Problems.DataAnalysis.Regression-3.3.csproj

    r4065 r4112  
    115115    <Compile Include="LinearRegression\LinearRegressionSolutionCreator.cs" /> 
    116116    <Compile Include="LinearRegression\LinearRegressionUtil.cs" /> 
    117     <Compile Include="RandomEnumerable.cs" /> 
    118117    <Compile Include="Properties\AssemblyInfo.cs" /> 
    119118    <Compile Include="SupportVectorRegression\BestSupportVectorRegressionSolutionAnalyzer.cs" /> 

  • trunk/sources/HeuristicLab.Problems.DataAnalysis/3.3/HeuristicLab.Problems.DataAnalysis-3.3.csproj

    r4065 r4112  
    135135    <Compile Include="Operators\WeightedParentsQualityVarianceComparator.cs" /> 
    136136    <Compile Include="Properties\AssemblyInfo.cs" /> 
     137    <Compile Include="RandomEnumerable.cs" /> 
    137138    <Compile Include="SupportVectorMachine\ParameterAdjustmentProblem\SupportVectorMachineParameterAdjustmentBestSolutionAnalyzer.cs" /> 
    138139    <Compile Include="SupportVectorMachine\ParameterAdjustmentProblem\SupportVectorMachineParameterAdjustmentEvaluator.cs" /> 

  • trunk/sources/HeuristicLab.Problems.DataAnalysis/3.3/RandomEnumerable.cs

    r4068 r4112  
    2424using HeuristicLab.Random; 
    2525 
    26 namespace HeuristicLab.Problems.DataAnalysis.Regression { 
     26namespace HeuristicLab.Problems.DataAnalysis { 
    2727  public class RandomEnumerable { 
    2828    public static IEnumerable<int> SampleRandomNumbers(int maxElement, int count) { 
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