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Ignore:
Timestamp:
07/18/14 13:44:53 (10 years ago)
Author:
ascheibe
Message:

merged rest of trunk into hive statistics branch

Location:
branches/HiveStatistics/sources
Files:
37 edited
1 copied

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  • branches/HiveStatistics/sources

  • branches/HiveStatistics/sources/HeuristicLab.Problems.DataAnalysis

  • branches/HiveStatistics/sources/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/ClassificationEnsembleModel.cs

    r11203 r11205  
    11#region License Information
    22/* HeuristicLab
    3  * Copyright (C) 2002-2012 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
     3 * Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    44 *
    55 * This file is part of HeuristicLab.

  • branches/HiveStatistics/sources/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/ClassificationEnsembleProblemData.cs

    r11203 r11205  
    11#region License Information
    22/* HeuristicLab
    3  * Copyright (C) 2002-2012 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
     3 * Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    44 *
    55 * This file is part of HeuristicLab.

  • branches/HiveStatistics/sources/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/ClassificationEnsembleSolution.cs

    r11203 r11205  
    11#region License Information
    22/* HeuristicLab
    3  * Copyright (C) 2002-2012 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
     3 * Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    44 *
    55 * This file is part of HeuristicLab.

  • branches/HiveStatistics/sources/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/ClassificationProblem.cs

    r11203 r11205  
    11#region License Information
    22/* HeuristicLab
    3  * Copyright (C) 2002-2012 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
     3 * Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    44 *
    55 * This file is part of HeuristicLab.

  • branches/HiveStatistics/sources/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/ClassificationProblemData.cs

    r11203 r11205  
    11#region License Information
    22/* HeuristicLab
    3  * Copyright (C) 2002-2012 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
     3 * Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    44 *
    55 * This file is part of HeuristicLab.
    … …  
    221221    public string TargetVariable {
    222222      get { return TargetVariableParameter.Value.Value; }
     223      set {
     224        if (value == null) throw new ArgumentNullException("targetVariable", "The provided value for the targetVariable is null.");
     225        if (value == TargetVariable) return;
     226
     227
     228        var matchingParameterValue = TargetVariableParameter.ValidValues.FirstOrDefault(v => v.Value == value);
     229        if (matchingParameterValue == null) throw new ArgumentException("The provided value is not valid as the targetVariable.", "targetVariable");
     230        TargetVariableParameter.Value = matchingParameterValue;
     231      }
    223232    }
    224233
    … …  
    291300    }
    292301
    293     public ClassificationProblemData(Dataset dataset, IEnumerable<string> allowedInputVariables, string targetVariable)
    294       : base(dataset, allowedInputVariables) {
     302    public ClassificationProblemData(Dataset dataset, IEnumerable<string> allowedInputVariables, string targetVariable, IEnumerable<ITransformation> transformations = null)
     303      : base(dataset, allowedInputVariables, transformations ?? Enumerable.Empty<ITransformation>()) {
    295304      var validTargetVariableValues = CheckVariablesForPossibleTargetVariables(dataset).Select(x => new StringValue(x).AsReadOnly()).ToList();
    296305      var target = validTargetVariableValues.Where(x => x.Value == targetVariable).DefaultIfEmpty(validTargetVariableValues.First()).First();
    … …  
    311320                                    .Distinct()
    312321                                    .Count()
    313                                   where distinctValues < MaximumNumberOfClasses
     322                                  where distinctValues <= MaximumNumberOfClasses
    314323                                  select v).ToArray();
    315324
    … …  
    408417    }
    409418    #endregion
     419
     420    protected override bool IsProblemDataCompatible(IDataAnalysisProblemData problemData, out string errorMessage) {
     421      if (problemData == null) throw new ArgumentNullException("problemData", "The provided problemData is null.");
     422      IClassificationProblemData classificationProblemData = problemData as IClassificationProblemData;
     423      if (classificationProblemData == null)
     424        throw new ArgumentException("The problem data is no classification problem data. Instead a " + problemData.GetType().GetPrettyName() + " was provided.", "problemData");
     425
     426      var returnValue = base.IsProblemDataCompatible(classificationProblemData, out errorMessage);
     427      //check targetVariable
     428      if (classificationProblemData.InputVariables.All(var => var.Value != TargetVariable)) {
     429        errorMessage = string.Format("The target variable {0} is not present in the new problem data.", TargetVariable)
     430                       + Environment.NewLine + errorMessage;
     431        return false;
     432      }
     433
     434      var newClassValues = classificationProblemData.Dataset.GetDoubleValues(TargetVariable).Distinct().OrderBy(x => x);
     435      if (!newClassValues.SequenceEqual(ClassValues)) {
     436        errorMessage = errorMessage + string.Format("The class values differ in the provided classification problem data.");
     437        return false;
     438      }
     439
     440      return returnValue;
     441    }
     442
     443    public override void AdjustProblemDataProperties(IDataAnalysisProblemData problemData) {
     444      if (problemData == null) throw new ArgumentNullException("problemData", "The provided problemData is null.");
     445      ClassificationProblemData classificationProblemData = problemData as ClassificationProblemData;
     446      if (classificationProblemData == null)
     447        throw new ArgumentException("The problem data is not a classification problem data. Instead a " + problemData.GetType().GetPrettyName() + " was provided.", "problemData");
     448
     449      base.AdjustProblemDataProperties(problemData);
     450      TargetVariable = classificationProblemData.TargetVariable;
     451      for (int i = 0; i < classificationProblemData.ClassNames.Count(); i++)
     452        ClassNamesParameter.Value[i, 0] = classificationProblemData.ClassNames.ElementAt(i);
     453
     454      for (int i = 0; i < Classes; i++) {
     455        for (int j = 0; j < Classes; j++) {
     456          ClassificationPenaltiesParameter.Value[i, j] = classificationProblemData.GetClassificationPenalty(ClassValuesCache[i], ClassValuesCache[j]);
     457        }
     458      }
     459    }
    410460  }
    411461}

  • branches/HiveStatistics/sources/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/ClassificationSolution.cs

    r11203 r11205  
    11#region License Information
    22/* HeuristicLab
    3  * Copyright (C) 2002-2012 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
     3 * Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    44 *
    55 * This file is part of HeuristicLab.

  • branches/HiveStatistics/sources/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/ClassificationSolutionBase.cs

    r11203 r11205  
    11#region License Information
    22/* HeuristicLab
    3  * Copyright (C) 2002-2012 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
     3 * Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    44 *
    55 * This file is part of HeuristicLab.

  • branches/HiveStatistics/sources/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/DiscriminantFunctionClassificationModel.cs

    r11203 r11205  
    11#region License Information
    22/* HeuristicLab
    3  * Copyright (C) 2002-2012 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
     3 * Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    44 *
    55 * This file is part of HeuristicLab.

  • branches/HiveStatistics/sources/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/DiscriminantFunctionClassificationSolution.cs

    r11203 r11205  
    11#region License Information
    22/* HeuristicLab
    3  * Copyright (C) 2002-2012 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
     3 * Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    44 *
    55 * This file is part of HeuristicLab.

  • branches/HiveStatistics/sources/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/DiscriminantFunctionClassificationSolutionBase.cs

    r11203 r11205  
    11#region License Information
    22/* HeuristicLab
    3  * Copyright (C) 2002-2012 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
     3 * Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    44 *
    55 * This file is part of HeuristicLab.

  • branches/HiveStatistics/sources/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/ThresholdCalculators/AccuracyMaximizationThresholdCalculator.cs

    r11203 r11205  
    11#region License Information
    22/* HeuristicLab
    3  * Copyright (C) 2002-2012 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
     3 * Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    44 *
    55 * This file is part of HeuristicLab.

  • branches/HiveStatistics/sources/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/ThresholdCalculators/NormalDistributionCutPointsThresholdCalculator.cs

    r11203 r11205  
    11#region License Information
    22/* HeuristicLab
    3  * Copyright (C) 2002-2012 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
     3 * Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    44 *
    55 * This file is part of HeuristicLab.

  • branches/HiveStatistics/sources/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/ThresholdCalculators/ThresholdCalculator.cs

    r11203 r11205  
    11#region License Information
    22/* HeuristicLab
    3  * Copyright (C) 2002-2012 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
     3 * Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    44 *
    55 * This file is part of HeuristicLab.

  • branches/HiveStatistics/sources/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Clustering/ClusteringProblem.cs

    r11203 r11205  
    11#region License Information
    22/* HeuristicLab
    3  * Copyright (C) 2002-2012 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
     3 * Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    44 *
    55 * This file is part of HeuristicLab.

  • branches/HiveStatistics/sources/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Clustering/ClusteringProblemData.cs

    r11203 r11205  
    11#region License Information
    22/* HeuristicLab
    3  * Copyright (C) 2002-2012 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
     3 * Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    44 *
    55 * This file is part of HeuristicLab.
    … …  
    2121
    2222using System.Collections.Generic;
     23using System.Linq;
    2324using HeuristicLab.Common;
    2425using HeuristicLab.Core;
    … …  
    8687    }
    8788
    88     public ClusteringProblemData(Dataset dataset, IEnumerable<string> allowedInputVariables)
    89       : base(dataset, allowedInputVariables) {
     89    public ClusteringProblemData(Dataset dataset, IEnumerable<string> allowedInputVariables, IEnumerable<ITransformation> transformations = null)
     90      : base(dataset, allowedInputVariables, transformations ?? Enumerable.Empty<ITransformation>()) {
    9091    }
    9192  }

  • branches/HiveStatistics/sources/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Clustering/ClusteringSolution.cs

    r11203 r11205  
    11#region License Information
    22/* HeuristicLab
    3  * Copyright (C) 2002-2012 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
     3 * Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    44 *
    55 * This file is part of HeuristicLab.

  • branches/HiveStatistics/sources/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/DataAnalysisProblem.cs

    r11203 r11205  
    11#region License Information
    22/* HeuristicLab
    3  * Copyright (C) 2002-2012 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
     3 * Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    44 *
    55 * This file is part of HeuristicLab.

  • branches/HiveStatistics/sources/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/DataAnalysisProblemData.cs

    r11203 r11205  
    11#region License Information
    22/* HeuristicLab
    3  * Copyright (C) 2002-2012 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
     3 * Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    44 *
    55 * This file is part of HeuristicLab.
    … …  
    2323using System.Collections.Generic;
    2424using System.Linq;
     25using System.Text;
    2526using HeuristicLab.Collections;
    2627using HeuristicLab.Common;
    … …  
    3738    protected const string TrainingPartitionParameterName = "TrainingPartition";
    3839    protected const string TestPartitionParameterName = "TestPartition";
     40    protected const string TransformationsParameterName = "Transformations";
    3941
    4042    #region parameter properites
    … …  
    5052    public IFixedValueParameter<IntRange> TestPartitionParameter {
    5153      get { return (IFixedValueParameter<IntRange>)Parameters[TestPartitionParameterName]; }
     54    }
     55    public IFixedValueParameter<ReadOnlyItemList<ITransformation>> TransformationsParameter {
     56      get { return (IFixedValueParameter<ReadOnlyItemList<ITransformation>>)Parameters[TransformationsParameterName]; }
    5257    }
    5358    #endregion
    … …  
    8893    }
    8994
     95    public IEnumerable<ITransformation> Transformations {
     96      get { return TransformationsParameter.Value; }
     97    }
     98
    9099    public virtual bool IsTrainingSample(int index) {
    91100      return index >= 0 && index < Dataset.Rows &&
    … …  
    110119    [StorableHook(HookType.AfterDeserialization)]
    111120    private void AfterDeserialization() {
     121      if (!Parameters.ContainsKey(TransformationsParameterName)) {
     122        Parameters.Add(new FixedValueParameter<ReadOnlyItemList<ITransformation>>(TransformationsParameterName, "", new ItemList<ITransformation>().AsReadOnly()));
     123        TransformationsParameter.Hidden = true;
     124      }
    112125      RegisterEventHandlers();
    113126    }
    114127
    115     protected DataAnalysisProblemData(Dataset dataset, IEnumerable<string> allowedInputVariables) {
     128    protected DataAnalysisProblemData(Dataset dataset, IEnumerable<string> allowedInputVariables, IEnumerable<ITransformation> transformations = null) {
    116129      if (dataset == null) throw new ArgumentNullException("The dataset must not be null.");
    117130      if (allowedInputVariables == null) throw new ArgumentNullException("The allowedInputVariables must not be null.");
    … …  
    129142      int testPartitionEnd = dataset.Rows;
    130143
     144      var transformationsList = new ItemList<ITransformation>(transformations ?? Enumerable.Empty<ITransformation>());
     145
    131146      Parameters.Add(new FixedValueParameter<Dataset>(DatasetParameterName, "", dataset));
    132147      Parameters.Add(new FixedValueParameter<ReadOnlyCheckedItemList<StringValue>>(InputVariablesParameterName, "", inputVariables.AsReadOnly()));
    133148      Parameters.Add(new FixedValueParameter<IntRange>(TrainingPartitionParameterName, "", new IntRange(trainingPartitionStart, trainingPartitionEnd)));
    134149      Parameters.Add(new FixedValueParameter<IntRange>(TestPartitionParameterName, "", new IntRange(testPartitionStart, testPartitionEnd)));
     150      Parameters.Add(new FixedValueParameter<ReadOnlyItemList<ITransformation>>(TransformationsParameterName, "", transformationsList.AsReadOnly()));
     151
     152      TransformationsParameter.Hidden = true;
    135153
    136154      ((ValueParameter<Dataset>)DatasetParameter).ReactOnValueToStringChangedAndValueItemImageChanged = false;
    … …  
    143161      TrainingPartition.ValueChanged += new EventHandler(Parameter_ValueChanged);
    144162      TestPartition.ValueChanged += new EventHandler(Parameter_ValueChanged);
     163      TransformationsParameter.ValueChanged += new EventHandler(Parameter_ValueChanged);
    145164    }
    146165
    … …  
    158177      if (listeners != null) listeners(this, EventArgs.Empty);
    159178    }
     179
     180    protected virtual bool IsProblemDataCompatible(IDataAnalysisProblemData problemData, out string errorMessage) {
     181      errorMessage = string.Empty;
     182      if (problemData == null) throw new ArgumentNullException("problemData", "The provided problemData is null.");
     183
     184      //check allowed input variables
     185      StringBuilder message = new StringBuilder();
     186      var variables = new HashSet<string>(problemData.InputVariables.Select(x => x.Value));
     187      foreach (var item in AllowedInputVariables) {
     188        if (!variables.Contains(item))
     189          message.AppendLine("Input variable '" + item + "' is not present in the new problem data.");
     190      }
     191
     192      if (message.Length != 0) {
     193        errorMessage = message.ToString();
     194        return false;
     195      }
     196      return true;
     197
     198    }
     199
     200    public virtual void AdjustProblemDataProperties(IDataAnalysisProblemData problemData) {
     201      DataAnalysisProblemData data = problemData as DataAnalysisProblemData;
     202      if (data == null) throw new ArgumentException("The problem data is not a data analysis problem data. Instead a " + problemData.GetType().GetPrettyName() + " was provided.", "problemData");
     203
     204      string errorMessage;
     205      if (!data.IsProblemDataCompatible(this, out errorMessage)) {
     206        throw new InvalidOperationException(errorMessage);
     207      }
     208
     209      foreach (var inputVariable in InputVariables) {
     210        var variable = data.InputVariables.FirstOrDefault(i => i.Value == inputVariable.Value);
     211        InputVariables.SetItemCheckedState(inputVariable, variable != null && data.InputVariables.ItemChecked(variable));
     212      }
     213
     214      TrainingPartition.Start = TrainingPartition.End = 0;
     215      TestPartition.Start = 0;
     216      TestPartition.End = Dataset.Rows;
     217    }
    160218  }
    161219}

  • branches/HiveStatistics/sources/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/DataAnalysisSolution.cs

    r11203 r11205  
    11#region License Information
    22/* HeuristicLab
    3  * Copyright (C) 2002-2012 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
     3 * Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    44 *
    55 * This file is part of HeuristicLab.

  • branches/HiveStatistics/sources/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Regression/ConstantRegressionModel.cs

    r11203 r11205  
    11#region License Information
    22/* HeuristicLab
    3  * Copyright (C) 2002-2012 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
     3 * Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    44 *
    55 * This file is part of HeuristicLab.

  • branches/HiveStatistics/sources/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Regression/ConstantRegressionSolution.cs

    r11203 r11205  
    11#region License Information
    22/* HeuristicLab
    3  * Copyright (C) 2002-2012 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
     3 * Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    44 *
    55 * This file is part of HeuristicLab.

  • branches/HiveStatistics/sources/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Regression/RegressionEnsembleModel.cs

    r11203 r11205  
    11#region License Information
    22/* HeuristicLab
    3  * Copyright (C) 2002-2012 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
     3 * Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    44 *
    55 * This file is part of HeuristicLab.

  • branches/HiveStatistics/sources/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Regression/RegressionEnsembleProblemData.cs

    r11203 r11205  
    11#region License Information
    22/* HeuristicLab
    3  * Copyright (C) 2002-2012 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
     3 * Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    44 *
    55 * This file is part of HeuristicLab.

  • branches/HiveStatistics/sources/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Regression/RegressionEnsembleSolution.cs

    r11203 r11205  
    11#region License Information
    22/* HeuristicLab
    3  * Copyright (C) 2002-2012 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
     3 * Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    44 *
    55 * This file is part of HeuristicLab.

  • branches/HiveStatistics/sources/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Regression/RegressionProblem.cs

    r11203 r11205  
    11#region License Information
    22/* HeuristicLab
    3  * Copyright (C) 2002-2012 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
     3 * Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    44 *
    55 * This file is part of HeuristicLab.

  • branches/HiveStatistics/sources/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Regression/RegressionProblemData.cs

    r11203 r11205  
    11#region License Information
    22/* HeuristicLab
    3  * Copyright (C) 2002-2012 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
     3 * Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    44 *
    55 * This file is part of HeuristicLab.
    … …  
    100100    public string TargetVariable {
    101101      get { return TargetVariableParameter.Value.Value; }
     102      set {
     103        if (value == null) throw new ArgumentNullException("targetVariable", "The provided value for the targetVariable is null.");
     104        if (value == TargetVariable) return;
     105
     106        var matchingParameterValue = TargetVariableParameter.ValidValues.FirstOrDefault(v => v.Value == value);
     107        if (matchingParameterValue == null) throw new ArgumentException("The provided value is not valid as the targetVariable.", "targetVariable");
     108        TargetVariableParameter.Value = matchingParameterValue;
     109      }
    102110    }
    103111
    … …  
    129137    }
    130138
    131     public RegressionProblemData(Dataset dataset, IEnumerable<string> allowedInputVariables, string targetVariable)
    132       : base(dataset, allowedInputVariables) {
     139    public RegressionProblemData(Dataset dataset, IEnumerable<string> allowedInputVariables, string targetVariable, IEnumerable<ITransformation> transformations = null)
     140      : base(dataset, allowedInputVariables, transformations ?? Enumerable.Empty<ITransformation>()) {
    133141      var variables = InputVariables.Select(x => x.AsReadOnly()).ToList();
    134142      Parameters.Add(new ConstrainedValueParameter<StringValue>(TargetVariableParameterName, new ItemSet<StringValue>(variables), variables.Where(x => x.Value == targetVariable).First()));
    … …  
    142150      OnChanged();
    143151    }
     152
     153    protected override bool IsProblemDataCompatible(IDataAnalysisProblemData problemData, out string errorMessage) {
     154      if (problemData == null) throw new ArgumentNullException("problemData", "The provided problemData is null.");
     155      IRegressionProblemData regressionProblemData = problemData as IRegressionProblemData;
     156      if (regressionProblemData == null)
     157        throw new ArgumentException("The problem data is not a regression problem data. Instead a " + problemData.GetType().GetPrettyName() + " was provided.", "problemData");
     158
     159      var returnValue = base.IsProblemDataCompatible(problemData, out errorMessage);
     160      //check targetVariable
     161      if (problemData.InputVariables.All(var => var.Value != TargetVariable)) {
     162        errorMessage = string.Format("The target variable {0} is not present in the new problem data.", TargetVariable)
     163                       + Environment.NewLine + errorMessage;
     164        return false;
     165      }
     166      return returnValue;
     167    }
     168
     169    public override void AdjustProblemDataProperties(IDataAnalysisProblemData problemData) {
     170      if (problemData == null) throw new ArgumentNullException("problemData", "The provided problemData is null.");
     171      RegressionProblemData regressionProblemData = problemData as RegressionProblemData;
     172      if (regressionProblemData == null)
     173        throw new ArgumentException("The problem data is not a regression problem data. Instead a " + problemData.GetType().GetPrettyName() + " was provided.", "problemData");
     174
     175      base.AdjustProblemDataProperties(problemData);
     176      TargetVariable = regressionProblemData.TargetVariable;
     177    }
    144178  }
    145179}

  • branches/HiveStatistics/sources/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Regression/RegressionSolution.cs

    r11203 r11205  
    11#region License Information
    22/* HeuristicLab
    3  * Copyright (C) 2002-2012 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
     3 * Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    44 *
    55 * This file is part of HeuristicLab.

  • branches/HiveStatistics/sources/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Regression/RegressionSolutionBase.cs

    r11203 r11205  
    11#region License Information
    22/* HeuristicLab
    3  * Copyright (C) 2002-2012 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
     3 * Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    44 *
    55 * This file is part of HeuristicLab.

  • branches/HiveStatistics/sources/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/TimeSeriesPrognosis/Models/ConstantTimeSeriesPrognosisModel.cs

    r11203 r11205  
    11#region License Information
    22/* HeuristicLab
    3  * Copyright (C) 2002-2012 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
     3 * Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    44 *
    55 * This file is part of HeuristicLab.

  • branches/HiveStatistics/sources/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/TimeSeriesPrognosis/Models/TimeSeriesPrognosisAutoRegressiveModel.cs

    r11203 r11205  
    11#region License Information
    22/* HeuristicLab
    3  * Copyright (C) 2002-2012 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
     3 * Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    44 *
    55 * This file is part of HeuristicLab.

  • branches/HiveStatistics/sources/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/TimeSeriesPrognosis/TimeSeriesPrognosisProblem.cs

    r11203 r11205  
    11#region License Information
    22/* HeuristicLab
    3  * Copyright (C) 2002-2011 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
     3 * Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    44 *
    55 * This file is part of HeuristicLab.

  • branches/HiveStatistics/sources/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/TimeSeriesPrognosis/TimeSeriesPrognosisProblemData.cs

    r11203 r11205  
    11#region License Information
    22/* HeuristicLab
    3  * Copyright (C) 2002-2011 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
     3 * Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    44 *
    55 * This file is part of HeuristicLab.
    … …  
    15821582      TrainingPartition.Start = 50;
    15831583    }
    1584     public TimeSeriesPrognosisProblemData(Dataset dataset, IEnumerable<string> allowedInputVariables, string targetVariable)
    1585       : base(dataset, allowedInputVariables, targetVariable) {
     1584    public TimeSeriesPrognosisProblemData(Dataset dataset, IEnumerable<string> allowedInputVariables, string targetVariable, IEnumerable<ITransformation> transformations = null)
     1585      : base(dataset, allowedInputVariables, targetVariable, transformations ?? Enumerable.Empty<ITransformation>()) {
    15861586      Parameters.Add(new FixedValueParameter<IntValue>(TrainingHorizonParameterName, "Specifies the horizon (how far the prognosis reaches in the future) for each training sample.", new IntValue(1)));
    15871587      Parameters.Add(new FixedValueParameter<IntValue>(TestHorizonParameterName, "Specifies the horizon (how far the prognosis reaches in the future) for each test sample.", new IntValue(1)));
    … …  
    15981598      : this(timeseriesProblemData.Dataset, timeseriesProblemData.AllowedInputVariables, timeseriesProblemData.TargetVariable) {
    15991599
     1600      TrainingPartition.Start = timeseriesProblemData.TrainingPartition.Start;
     1601      TrainingPartition.End = timeseriesProblemData.TrainingPartition.End;
     1602      TestPartition.Start = timeseriesProblemData.TestPartition.Start;
     1603      TestPartition.End = timeseriesProblemData.TestPartition.End;
     1604
     1605      TrainingHorizon = timeseriesProblemData.TrainingHorizon;
    16001606      TestHorizon = timeseriesProblemData.TestHorizon;
    1601       TrainingHorizon = timeseriesProblemData.TestHorizon;
    16021607    }
    16031608
    … …  
    16161621    }
    16171622
     1623    protected override bool IsProblemDataCompatible(IDataAnalysisProblemData problemData, out string errorMessage) {
     1624      if (problemData == null) throw new ArgumentNullException("problemData", "The provided problemData is null.");
     1625      ITimeSeriesPrognosisProblemData timeseriesProblemData = problemData as ITimeSeriesPrognosisProblemData;
     1626      if (timeseriesProblemData == null)
     1627        throw new ArgumentException("The problem data is not a time-series problem data. Instead a " + problemData.GetType().GetPrettyName() + " was provided.", "problemData");
     1628
     1629      var returnValue = base.IsProblemDataCompatible(problemData, out errorMessage);
     1630      //check targetVariable
     1631      if (problemData.InputVariables.All(var => var.Value != TargetVariable)) {
     1632        errorMessage = string.Format("The target variable {0} is not present in the new problem data.", TargetVariable)
     1633                       + Environment.NewLine + errorMessage;
     1634        return false;
     1635      }
     1636      return returnValue;
     1637    }
     1638
     1639    public override void AdjustProblemDataProperties(IDataAnalysisProblemData problemData) {
     1640      TimeSeriesPrognosisProblemData timeSeriesProblemData = problemData as TimeSeriesPrognosisProblemData;
     1641      if (timeSeriesProblemData == null)
     1642        throw new ArgumentException("The problem data is not a timeseries problem data. Instead a " + problemData.GetType().GetPrettyName() + " was provided.", "problemData");
     1643
     1644      var trainingDataStart = TrainingIndices.First();
     1645
     1646      base.AdjustProblemDataProperties(problemData);
     1647
     1648      TestPartition.Start = trainingDataStart;
     1649
     1650      TrainingHorizon = timeSeriesProblemData.TrainingHorizon;
     1651      TestHorizon = timeSeriesProblemData.TestHorizon;
     1652    }
     1653
    16181654  }
    16191655}

  • branches/HiveStatistics/sources/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/TimeSeriesPrognosis/TimeSeriesPrognosisResults.cs

    r11203 r11205  
    11#region License Information
    22/* HeuristicLab
    3  * Copyright (C) 2002-2012 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
     3 * Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    44 *
    55 * This file is part of HeuristicLab.
    … …  
    369369      OnlineCalculatorError errorState;
    370370      var problemData = Solution.ProblemData;
     371      if (!problemData.TrainingIndices.Any()) return;
    371372      var model = Solution.Model;
    372373      //mean model
    … …  
    415416      OnlineCalculatorError errorState;
    416417      var problemData = Solution.ProblemData;
     418      if (!problemData.TestIndices.Any()) return;
    417419      var model = Solution.Model;
    418       //mean model
    419       double trainingMean = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, problemData.TrainingIndices).Average();
    420       var meanModel = new ConstantTimeSeriesPrognosisModel(trainingMean);
    421 
    422       //AR1 model
    423       double alpha, beta;
    424       IEnumerable<double> trainingStartValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, problemData.TrainingIndices.Select(r => r - 1).Where(r => r > 0)).ToList();
    425       OnlineLinearScalingParameterCalculator.Calculate(problemData.Dataset.GetDoubleValues(problemData.TargetVariable, problemData.TrainingIndices.Where(x => x > 0)), trainingStartValues, out alpha, out beta, out errorState);
    426       var AR1model = new TimeSeriesPrognosisAutoRegressiveModel(problemData.TargetVariable, new double[] { beta }, alpha);
    427 
    428420      var testHorizions = problemData.TestIndices.Select(r => Math.Min(testHorizon, problemData.TestPartition.End - r)).ToList();
    429421      IEnumerable<IEnumerable<double>> testTargetValues = problemData.TestIndices.Zip(testHorizions, Enumerable.Range).Select(r => problemData.Dataset.GetDoubleValues(problemData.TargetVariable, r)).ToList();
    430422      IEnumerable<IEnumerable<double>> testEstimatedValues = model.GetPrognosedValues(problemData.Dataset, problemData.TestIndices, testHorizions).ToList();
    431423      IEnumerable<double> testStartValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, problemData.TestIndices.Select(r => r - 1).Where(r => r > 0)).ToList();
    432       IEnumerable<IEnumerable<double>> testMeanModelPredictions = meanModel.GetPrognosedValues(problemData.Dataset, problemData.TestIndices, testHorizions).ToList();
    433       IEnumerable<IEnumerable<double>> testAR1ModelPredictions = AR1model.GetPrognosedValues(problemData.Dataset, problemData.TestIndices, testHorizions).ToList();
    434424
    435425      IEnumerable<double> originalTestValues = testTargetValues.SelectMany(x => x).ToList();
    … …  
    453443      PrognosisTestWeightedDirectionalSymmetry = OnlineWeightedDirectionalSymmetryCalculator.Calculate(testStartValues, testTargetValues, testEstimatedValues, out errorState);
    454444      PrognosisTestWeightedDirectionalSymmetry = errorState == OnlineCalculatorError.None ? PrognosisTestWeightedDirectionalSymmetry : 0.0;
    455       PrognosisTestTheilsUStatisticAR1 = OnlineTheilsUStatisticCalculator.Calculate(testStartValues, testTargetValues, testAR1ModelPredictions, testEstimatedValues, out errorState);
    456       PrognosisTestTheilsUStatisticAR1 = errorState == OnlineCalculatorError.None ? PrognosisTestTheilsUStatisticAR1 : double.PositiveInfinity;
    457       PrognosisTestTheilsUStatisticMean = OnlineTheilsUStatisticCalculator.Calculate(testStartValues, testTargetValues, testMeanModelPredictions, testEstimatedValues, out errorState);
    458       PrognosisTestTheilsUStatisticMean = errorState == OnlineCalculatorError.None ? PrognosisTestTheilsUStatisticMean : double.PositiveInfinity;
     445
     446
     447      if (problemData.TrainingIndices.Any()) {
     448        //mean model
     449        double trainingMean = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, problemData.TrainingIndices).Average();
     450        var meanModel = new ConstantTimeSeriesPrognosisModel(trainingMean);
     451
     452        //AR1 model
     453        double alpha, beta;
     454        IEnumerable<double> trainingStartValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, problemData.TrainingIndices.Select(r => r - 1).Where(r => r > 0)).ToList();
     455        OnlineLinearScalingParameterCalculator.Calculate(problemData.Dataset.GetDoubleValues(problemData.TargetVariable, problemData.TrainingIndices.Where(x => x > 0)), trainingStartValues, out alpha, out beta, out errorState);
     456        var AR1model = new TimeSeriesPrognosisAutoRegressiveModel(problemData.TargetVariable, new double[] { beta }, alpha);
     457
     458        IEnumerable<IEnumerable<double>> testMeanModelPredictions = meanModel.GetPrognosedValues(problemData.Dataset, problemData.TestIndices, testHorizions).ToList();
     459        IEnumerable<IEnumerable<double>> testAR1ModelPredictions = AR1model.GetPrognosedValues(problemData.Dataset, problemData.TestIndices, testHorizions).ToList();
     460
     461        PrognosisTestTheilsUStatisticAR1 = OnlineTheilsUStatisticCalculator.Calculate(testStartValues, testTargetValues, testAR1ModelPredictions, testEstimatedValues, out errorState);
     462        PrognosisTestTheilsUStatisticAR1 = errorState == OnlineCalculatorError.None ? PrognosisTestTheilsUStatisticAR1 : double.PositiveInfinity;
     463        PrognosisTestTheilsUStatisticMean = OnlineTheilsUStatisticCalculator.Calculate(testStartValues, testTargetValues, testMeanModelPredictions, testEstimatedValues, out errorState);
     464        PrognosisTestTheilsUStatisticMean = errorState == OnlineCalculatorError.None ? PrognosisTestTheilsUStatisticMean : double.PositiveInfinity;
     465      }
    459466    }
    460467  }

  • branches/HiveStatistics/sources/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/TimeSeriesPrognosis/TimeSeriesPrognosisSolution.cs

    r11203 r11205  
    11#region License Information
    22/* HeuristicLab
    3  * Copyright (C) 2002-2011 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
     3 * Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    44 *
    55 * This file is part of HeuristicLab.

  • branches/HiveStatistics/sources/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/TimeSeriesPrognosis/TimeSeriesPrognosisSolutionBase.cs

    r11203 r11205  
    11#region License Information
    22/* HeuristicLab
    3  * Copyright (C) 2002-2011 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
     3 * Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    44 *
    55 * This file is part of HeuristicLab.
    … …  
    149149    protected void CalculateTimeSeriesResults() {
    150150      OnlineCalculatorError errorState;
    151       double trainingMean = ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TrainingIndices).Average();
     151      double trainingMean = ProblemData.TrainingIndices.Any() ? ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TrainingIndices).Average() : double.NaN;
    152152      var meanModel = new ConstantTimeSeriesPrognosisModel(trainingMean);
    153153
    … …  
    159159
    160160      #region Calculate training quality measures
    161       IEnumerable<double> trainingTargetValues = ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TrainingIndices).ToList();
    162       IEnumerable<double> trainingEstimatedValues = EstimatedTrainingValues.ToList();
    163       IEnumerable<double> trainingMeanModelPredictions = meanModel.GetEstimatedValues(ProblemData.Dataset, ProblemData.TrainingIndices).ToList();
    164       IEnumerable<double> trainingAR1ModelPredictions = AR1model.GetEstimatedValues(ProblemData.Dataset, ProblemData.TrainingIndices).ToList();
    165 
    166       TrainingDirectionalSymmetry = OnlineDirectionalSymmetryCalculator.Calculate(trainingTargetValues.First(), trainingTargetValues, trainingEstimatedValues, out errorState);
    167       TrainingDirectionalSymmetry = errorState == OnlineCalculatorError.None ? TrainingDirectionalSymmetry : 0.0;
    168       TrainingWeightedDirectionalSymmetry = OnlineWeightedDirectionalSymmetryCalculator.Calculate(trainingTargetValues.First(), trainingTargetValues, trainingEstimatedValues, out errorState);
    169       TrainingWeightedDirectionalSymmetry = errorState == OnlineCalculatorError.None ? TrainingWeightedDirectionalSymmetry : 0.0;
    170       TrainingTheilsUStatisticAR1 = OnlineTheilsUStatisticCalculator.Calculate(trainingTargetValues.First(), trainingTargetValues, trainingAR1ModelPredictions, trainingEstimatedValues, out errorState);
    171       TrainingTheilsUStatisticAR1 = errorState == OnlineCalculatorError.None ? TrainingTheilsUStatisticAR1 : double.PositiveInfinity;
    172       TrainingTheilsUStatisticMean = OnlineTheilsUStatisticCalculator.Calculate(trainingTargetValues.First(), trainingTargetValues, trainingMeanModelPredictions, trainingEstimatedValues, out errorState);
    173       TrainingTheilsUStatisticMean = errorState == OnlineCalculatorError.None ? TrainingTheilsUStatisticMean : double.PositiveInfinity;
     161      if (ProblemData.TrainingIndices.Any()) {
     162        IEnumerable<double> trainingTargetValues = ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TrainingIndices).ToList();
     163        IEnumerable<double> trainingEstimatedValues = EstimatedTrainingValues.ToList();
     164        IEnumerable<double> trainingMeanModelPredictions = meanModel.GetEstimatedValues(ProblemData.Dataset, ProblemData.TrainingIndices).ToList();
     165        IEnumerable<double> trainingAR1ModelPredictions = AR1model.GetEstimatedValues(ProblemData.Dataset, ProblemData.TrainingIndices).ToList();
     166
     167        TrainingDirectionalSymmetry = OnlineDirectionalSymmetryCalculator.Calculate(trainingTargetValues.First(), trainingTargetValues, trainingEstimatedValues, out errorState);
     168        TrainingDirectionalSymmetry = errorState == OnlineCalculatorError.None ? TrainingDirectionalSymmetry : 0.0;
     169        TrainingWeightedDirectionalSymmetry = OnlineWeightedDirectionalSymmetryCalculator.Calculate(trainingTargetValues.First(), trainingTargetValues, trainingEstimatedValues, out errorState);
     170        TrainingWeightedDirectionalSymmetry = errorState == OnlineCalculatorError.None ? TrainingWeightedDirectionalSymmetry : 0.0;
     171        TrainingTheilsUStatisticAR1 = OnlineTheilsUStatisticCalculator.Calculate(trainingTargetValues.First(), trainingTargetValues, trainingAR1ModelPredictions, trainingEstimatedValues, out errorState);
     172        TrainingTheilsUStatisticAR1 = errorState == OnlineCalculatorError.None ? TrainingTheilsUStatisticAR1 : double.PositiveInfinity;
     173        TrainingTheilsUStatisticMean = OnlineTheilsUStatisticCalculator.Calculate(trainingTargetValues.First(), trainingTargetValues, trainingMeanModelPredictions, trainingEstimatedValues, out errorState);
     174        TrainingTheilsUStatisticMean = errorState == OnlineCalculatorError.None ? TrainingTheilsUStatisticMean : double.PositiveInfinity;
     175      }
    174176      #endregion
    175177
    176178      #region Calculate test quality measures
    177       IEnumerable<double> testTargetValues = ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TestIndices).ToList();
    178       IEnumerable<double> testEstimatedValues = EstimatedTestValues.ToList();
    179       IEnumerable<double> testMeanModelPredictions = meanModel.GetEstimatedValues(ProblemData.Dataset, ProblemData.TestIndices).ToList();
    180       IEnumerable<double> testAR1ModelPredictions = AR1model.GetEstimatedValues(ProblemData.Dataset, ProblemData.TestIndices).ToList();
    181 
    182       TestDirectionalSymmetry = OnlineDirectionalSymmetryCalculator.Calculate(testTargetValues.First(), testTargetValues, testEstimatedValues, out errorState);
    183       TestDirectionalSymmetry = errorState == OnlineCalculatorError.None ? TestDirectionalSymmetry : 0.0;
    184       TestWeightedDirectionalSymmetry = OnlineWeightedDirectionalSymmetryCalculator.Calculate(testTargetValues.First(), testTargetValues, testEstimatedValues, out errorState);
    185       TestWeightedDirectionalSymmetry = errorState == OnlineCalculatorError.None ? TestWeightedDirectionalSymmetry : 0.0;
    186       TestTheilsUStatisticAR1 = OnlineTheilsUStatisticCalculator.Calculate(testTargetValues.First(), testTargetValues, testAR1ModelPredictions, testEstimatedValues, out errorState);
    187       TestTheilsUStatisticAR1 = errorState == OnlineCalculatorError.None ? TestTheilsUStatisticAR1 : double.PositiveInfinity;
    188       TestTheilsUStatisticMean = OnlineTheilsUStatisticCalculator.Calculate(testTargetValues.First(), testTargetValues, testMeanModelPredictions, testEstimatedValues, out errorState);
    189       TestTheilsUStatisticMean = errorState == OnlineCalculatorError.None ? TestTheilsUStatisticMean : double.PositiveInfinity;
     179      if (ProblemData.TestIndices.Any()) {
     180        IEnumerable<double> testTargetValues = ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TestIndices).ToList();
     181        IEnumerable<double> testEstimatedValues = EstimatedTestValues.ToList();
     182        IEnumerable<double> testMeanModelPredictions = meanModel.GetEstimatedValues(ProblemData.Dataset, ProblemData.TestIndices).ToList();
     183        IEnumerable<double> testAR1ModelPredictions = AR1model.GetEstimatedValues(ProblemData.Dataset, ProblemData.TestIndices).ToList();
     184
     185        TestDirectionalSymmetry = OnlineDirectionalSymmetryCalculator.Calculate(testTargetValues.First(), testTargetValues, testEstimatedValues, out errorState);
     186        TestDirectionalSymmetry = errorState == OnlineCalculatorError.None ? TestDirectionalSymmetry : 0.0;
     187        TestWeightedDirectionalSymmetry = OnlineWeightedDirectionalSymmetryCalculator.Calculate(testTargetValues.First(), testTargetValues, testEstimatedValues, out errorState);
     188        TestWeightedDirectionalSymmetry = errorState == OnlineCalculatorError.None ? TestWeightedDirectionalSymmetry : 0.0;
     189        TestTheilsUStatisticAR1 = OnlineTheilsUStatisticCalculator.Calculate(testTargetValues.First(), testTargetValues, testAR1ModelPredictions, testEstimatedValues, out errorState);
     190        TestTheilsUStatisticAR1 = errorState == OnlineCalculatorError.None ? TestTheilsUStatisticAR1 : double.PositiveInfinity;
     191        TestTheilsUStatisticMean = OnlineTheilsUStatisticCalculator.Calculate(testTargetValues.First(), testTargetValues, testMeanModelPredictions, testEstimatedValues, out errorState);
     192        TestTheilsUStatisticMean = errorState == OnlineCalculatorError.None ? TestTheilsUStatisticMean : double.PositiveInfinity;
     193      }
    190194      #endregion
    191195    }
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