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Timestamp:
12/02/10 15:25:55 (13 years ago)
Author:
gkronber
Message:

commit of local changes in data-analysis feature exploration branch. #1142

Location:
branches/DataAnalysis/HeuristicLab.Problems.DataAnalysis.MultiVariate.TimeSeriesPrognosis.Views/3.3
Files:
2 edited

Legend:

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  • branches/DataAnalysis/HeuristicLab.Problems.DataAnalysis.MultiVariate.TimeSeriesPrognosis.Views/3.3/PrognosisView.cs

    r4556 r5010  
    7676      DoubleMatrix matrix = null;
    7777        if (Content != null) {
    78           IEnumerable<string> targetVariables = Content.ProblemData.TargetVariables.CheckedItems.Select(x => x.Value.Value);
    79           List<double[]> prognosis = Content.GetPrognosis(currentTimePoint).ToList();
    80           double[,] values = new double[prognosis.Count, targetVariables.Count() * 2];
    81           for (int row = 0; row < prognosis.Count; row++) {
    82             int col = 0;
    83             int t = currentTimePoint + row;
    84             foreach (string targetVariable in targetVariables) {
    85               values[row, col++] = t<Content.ProblemData.Dataset.Rows ? Content.ProblemData.Dataset[targetVariable, t] : double.NaN;
    86               values[row, col++] = prognosis[row][(col - 1) / 2];
    87             }
    88           }
    89           matrix = new DoubleMatrix(values);
    90           string[] partitions = new string[] { "(original)", "(estimated)" };
    91           matrix.ColumnNames = from targetVariable in targetVariables
    92                                from partition in partitions
    93                                select targetVariable + " " + partition;
    94           ;
     78          matrix = CalculateMatrix();
    9579        }
    9680        valuesView.Content = matrix;
     81    }
     82
     83    public DoubleMatrix CalculateMatrix() {
     84      DoubleMatrix matrix = null;
     85      IEnumerable<string> targetVariables = Content.ProblemData.TargetVariables.CheckedItems.Select(x => x.Value.Value);
     86      List<double[]> prognosis = Content.GetPrognosis(currentTimePoint).ToList();
     87      double[,] values = new double[prognosis.Count, targetVariables.Count() * 2];
     88      for (int row = 0; row < prognosis.Count; row++) {
     89        int col = 0;
     90        int t = currentTimePoint + row;
     91        foreach (string targetVariable in targetVariables) {
     92          values[row, col++] = t < Content.ProblemData.Dataset.Rows ? Content.ProblemData.Dataset[targetVariable, t] : double.NaN;
     93          values[row, col++] = prognosis[row][(col - 1) / 2];
     94        }
     95      }
     96      matrix = new DoubleMatrix(values);
     97      string[] partitions = new string[] { "(original)", "(estimated)" };
     98      matrix.ColumnNames = from targetVariable in targetVariables
     99                           from partition in partitions
     100                           select targetVariable + " " + partition;
     101      ;
     102      return matrix;
    97103    }
    98104

  • branches/DataAnalysis/HeuristicLab.Problems.DataAnalysis.MultiVariate.TimeSeriesPrognosis.Views/3.3/ResultsView.cs

    r4556 r5010  
    8585    private void UpdateView() {
    8686      if (Content != null) {
    87         List<string> targetVariables = Content.ProblemData.TargetVariables.CheckedItems.Select(x => x.Value.Value).ToList();
    88         DoubleMatrix matrix = new DoubleMatrix(rowNames.Count, targetVariables.Count() * 2);
    89         matrix.RowNames = rowNames;
    90         matrix.ColumnNames = targetVariables.SelectMany(x => new List<string>() { x + " (training)", x + " (test)" });
    91         matrix.SortableView = false;
    92 
    93         int trainingStart = Content.ProblemData.TrainingSamplesStart.Value;
    94         int trainingEnd = Content.ProblemData.TrainingSamplesEnd.Value;
    95         int testStart = Content.ProblemData.TestSamplesStart.Value;
    96         int testEnd = Content.ProblemData.TestSamplesEnd.Value;
    97         // create a list of time series evaluators for each target variable
    98         Dictionary<string, List<IOnlineEvaluator>> trainingEvaluators =
    99           new Dictionary<string, List<IOnlineEvaluator>>();
    100         Dictionary<string, List<IOnlineEvaluator>> testEvaluators =
    101           new Dictionary<string, List<IOnlineEvaluator>>();
    102         foreach (string targetVariable in targetVariables) {
    103           trainingEvaluators.Add(targetVariable, new List<IOnlineEvaluator>());
    104           trainingEvaluators[targetVariable].Add(new OnlineMeanSquaredErrorEvaluator());
    105           trainingEvaluators[targetVariable].Add(new OnlinePearsonsRSquaredEvaluator());
    106           trainingEvaluators[targetVariable].Add(new OnlineMeanAbsolutePercentageErrorEvaluator());
    107           trainingEvaluators[targetVariable].Add(new OnlineDirectionalSymmetryEvaluator());
    108           trainingEvaluators[targetVariable].Add(new OnlineWeightedDirectionalSymmetryEvaluator());
    109           trainingEvaluators[targetVariable].Add(new OnlineTheilsUStatisticEvaluator());
    110 
    111           testEvaluators.Add(targetVariable, new List<IOnlineEvaluator>());
    112           testEvaluators[targetVariable].Add(new OnlineMeanSquaredErrorEvaluator());
    113           testEvaluators[targetVariable].Add(new OnlinePearsonsRSquaredEvaluator());
    114           testEvaluators[targetVariable].Add(new OnlineMeanAbsolutePercentageErrorEvaluator());
    115           testEvaluators[targetVariable].Add(new OnlineDirectionalSymmetryEvaluator());
    116           testEvaluators[targetVariable].Add(new OnlineWeightedDirectionalSymmetryEvaluator());
    117           testEvaluators[targetVariable].Add(new OnlineTheilsUStatisticEvaluator());
    118         }
    119 
    120         Evaluate(trainingStart, trainingEnd, trainingEvaluators);
    121         Evaluate(testStart, testEnd, testEvaluators);
    122 
    123         int columnIndex = 0;
    124         foreach (string targetVariable in targetVariables) {
    125           int rowIndex = 0;
    126           // training
    127           foreach (var evaluator in trainingEvaluators[targetVariable]) {
    128             matrix[rowIndex++, columnIndex] = evaluator.Value;
    129           }
    130           columnIndex++;
    131           // test
    132           rowIndex = 0;
    133           foreach (var evaluator in testEvaluators[targetVariable]) {
    134             matrix[rowIndex++, columnIndex] = evaluator.Value;
    135           }
    136           columnIndex++;
    137         }
    138 
    139         matrixView.Content = matrix;
     87        matrixView.Content = CalculateMatrix();
    14088      } else
    14189        matrixView.Content = null;
     90    }
     91
     92    public DoubleMatrix CalculateMatrix() {
     93      List<string> targetVariables = Content.ProblemData.TargetVariables.CheckedItems.Select(x => x.Value.Value).ToList();
     94      DoubleMatrix matrix = new DoubleMatrix(rowNames.Count, targetVariables.Count() * 2);
     95      matrix.RowNames = rowNames;
     96      matrix.ColumnNames = targetVariables.SelectMany(x => new List<string>() { x + " (training)", x + " (test)" });
     97      matrix.SortableView = false;
     98
     99      int trainingStart = Content.ProblemData.TrainingSamplesStart.Value;
     100      int trainingEnd = Content.ProblemData.TrainingSamplesEnd.Value;
     101      int testStart = Content.ProblemData.TestSamplesStart.Value;
     102      int testEnd = Content.ProblemData.TestSamplesEnd.Value;
     103      // create a list of time series evaluators for each target variable
     104      Dictionary<string, List<IOnlineEvaluator>> trainingEvaluators =
     105        new Dictionary<string, List<IOnlineEvaluator>>();
     106      Dictionary<string, List<IOnlineEvaluator>> testEvaluators =
     107        new Dictionary<string, List<IOnlineEvaluator>>();
     108      foreach (string targetVariable in targetVariables) {
     109        trainingEvaluators.Add(targetVariable, new List<IOnlineEvaluator>());
     110        trainingEvaluators[targetVariable].Add(new OnlineMeanSquaredErrorEvaluator());
     111        trainingEvaluators[targetVariable].Add(new OnlinePearsonsRSquaredEvaluator());
     112        trainingEvaluators[targetVariable].Add(new OnlineMeanAbsolutePercentageErrorEvaluator());
     113        trainingEvaluators[targetVariable].Add(new OnlineDirectionalSymmetryEvaluator());
     114        trainingEvaluators[targetVariable].Add(new OnlineWeightedDirectionalSymmetryEvaluator());
     115        trainingEvaluators[targetVariable].Add(new OnlineTheilsUStatisticEvaluator());
     116
     117        testEvaluators.Add(targetVariable, new List<IOnlineEvaluator>());
     118        testEvaluators[targetVariable].Add(new OnlineMeanSquaredErrorEvaluator());
     119        testEvaluators[targetVariable].Add(new OnlinePearsonsRSquaredEvaluator());
     120        testEvaluators[targetVariable].Add(new OnlineMeanAbsolutePercentageErrorEvaluator());
     121        testEvaluators[targetVariable].Add(new OnlineDirectionalSymmetryEvaluator());
     122        testEvaluators[targetVariable].Add(new OnlineWeightedDirectionalSymmetryEvaluator());
     123        testEvaluators[targetVariable].Add(new OnlineTheilsUStatisticEvaluator());
     124      }
     125
     126      Evaluate(trainingStart, trainingEnd, trainingEvaluators);
     127      Evaluate(testStart, testEnd, testEvaluators);
     128
     129      int columnIndex = 0;
     130      foreach (string targetVariable in targetVariables) {
     131        int rowIndex = 0;
     132        // training
     133        foreach (var evaluator in trainingEvaluators[targetVariable]) {
     134          matrix[rowIndex++, columnIndex] = evaluator.Value;
     135        }
     136        columnIndex++;
     137        // test
     138        rowIndex = 0;
     139        foreach (var evaluator in testEvaluators[targetVariable]) {
     140          matrix[rowIndex++, columnIndex] = evaluator.Value;
     141        }
     142        columnIndex++;
     143      }
     144      return matrix;
    142145    }
    143146
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