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Ignore:
Timestamp:
08/11/12 14:45:15 (12 years ago)
Author:
gkronber
Message:

#1902 fixed bug in calculation of variance in GPR model

File:
1 edited

Legend:

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Removed
  • trunk/sources/HeuristicLab.Algorithms.DataAnalysis.Views/3.4/GaussianProcessRegressionSolutionLineChartView.cs

    r8473 r8475  
    6363        this.chart.ChartAreas[0].AxisX.Maximum = Content.ProblemData.Dataset.Rows - 1;
    6464
    65         this.chart.Series.Add(TARGETVARIABLE_SERIES_NAME);
    66         this.chart.Series[TARGETVARIABLE_SERIES_NAME].LegendText = Content.ProblemData.TargetVariable;
    67         this.chart.Series[TARGETVARIABLE_SERIES_NAME].ChartType = SeriesChartType.FastLine;
    68         this.chart.Series[TARGETVARIABLE_SERIES_NAME].Points.DataBindXY(Enumerable.Range(0, Content.ProblemData.Dataset.Rows).ToArray(),
    69           Content.ProblemData.Dataset.GetDoubleValues(Content.ProblemData.TargetVariable).ToArray());
    7065        // training series
    7166        this.chart.Series.Add(ESTIMATEDVALUES_TRAINING_SERIES_NAME);
     
    7570        var mean = Content.EstimatedTrainingValues.ToArray();
    7671        var s2 = Content.EstimatedTrainingVariance.ToArray();
    77         var lower = mean.Zip(s2, (m, s) => m - s).ToArray();
    78         var upper = mean.Zip(s2, (m, s) => m + s).ToArray();
     72        var lower = mean.Zip(s2, (m, s) => m - 1.96 * Math.Sqrt(s)).ToArray();
     73        var upper = mean.Zip(s2, (m, s) => m + 1.96 * Math.Sqrt(s)).ToArray();
    7974        this.chart.Series[ESTIMATEDVALUES_TRAINING_SERIES_NAME].Points.DataBindXY(Content.ProblemData.TrainingIndices.ToArray(), lower, upper);
    8075        this.InsertEmptyPoints(this.chart.Series[ESTIMATEDVALUES_TRAINING_SERIES_NAME]);
    8176        this.chart.Series[ESTIMATEDVALUES_TRAINING_SERIES_NAME].Tag = Content;
     77
    8278        // test series
    8379        this.chart.Series.Add(ESTIMATEDVALUES_TEST_SERIES_NAME);
     
    8783        mean = Content.EstimatedTestValues.ToArray();
    8884        s2 = Content.EstimatedTestVariance.ToArray();
    89         lower = mean.Zip(s2, (m, s) => m - s).ToArray();
    90         upper = mean.Zip(s2, (m, s) => m + s).ToArray();
     85        lower = mean.Zip(s2, (m, s) => m - 1.96 * Math.Sqrt(s)).ToArray();
     86        upper = mean.Zip(s2, (m, s) => m + 1.96 * Math.Sqrt(s)).ToArray();
    9187        this.chart.Series[ESTIMATEDVALUES_TEST_SERIES_NAME].Points.DataBindXY(Content.ProblemData.TestIndices.ToArray(), lower, upper);
    9288        this.InsertEmptyPoints(this.chart.Series[ESTIMATEDVALUES_TEST_SERIES_NAME]);
    9389        this.chart.Series[ESTIMATEDVALUES_TEST_SERIES_NAME].Tag = Content;
     90
    9491        // series of remaining points
    9592        int[] allIndices = Enumerable.Range(0, Content.ProblemData.Dataset.Rows).Except(Content.ProblemData.TrainingIndices).Except(Content.ProblemData.TestIndices).ToArray();
     
    10299        this.InsertEmptyPoints(this.chart.Series[ESTIMATEDVALUES_ALL_SERIES_NAME]);
    103100        this.chart.Series[ESTIMATEDVALUES_ALL_SERIES_NAME].Tag = Content;
     101
     102        // target
     103        this.chart.Series.Add(TARGETVARIABLE_SERIES_NAME);
     104        this.chart.Series[TARGETVARIABLE_SERIES_NAME].LegendText = Content.ProblemData.TargetVariable;
     105        this.chart.Series[TARGETVARIABLE_SERIES_NAME].ChartType = SeriesChartType.FastLine;
     106        this.chart.Series[TARGETVARIABLE_SERIES_NAME].Points.DataBindXY(Enumerable.Range(0, Content.ProblemData.Dataset.Rows).ToArray(),
     107          Content.ProblemData.Dataset.GetDoubleValues(Content.ProblemData.TargetVariable).ToArray());
     108
    104109        this.ToggleSeriesData(this.chart.Series[ESTIMATEDVALUES_ALL_SERIES_NAME]);
    105110
     
    243248            mean = Content.EstimatedValues.ToArray();
    244249            s2 = Content.EstimatedVariance.ToArray();
    245             lower = mean.Zip(s2, (m, s) => m - s).ToArray();
    246             upper = mean.Zip(s2, (m, s) => m + s).ToArray();
     250            lower = mean.Zip(s2, (m, s) => m - 1.96 * Math.Sqrt(s)).ToArray();
     251            upper = mean.Zip(s2, (m, s) => m + 1.96 * Math.Sqrt(s)).ToArray();
    247252            lower = indices.Select(index => lower[index]).ToArray();
    248253            upper = indices.Select(index => upper[index]).ToArray();
     
    252257            mean = Content.EstimatedTrainingValues.ToArray();
    253258            s2 = Content.EstimatedTrainingVariance.ToArray();
    254             lower = mean.Zip(s2, (m, s) => m - s).ToArray();
    255             upper = mean.Zip(s2, (m, s) => m + s).ToArray();
     259            lower = mean.Zip(s2, (m, s) => m - 1.96 * Math.Sqrt(s)).ToArray();
     260            upper = mean.Zip(s2, (m, s) => m + 1.96 * Math.Sqrt(s)).ToArray();
    256261            break;
    257262          case ESTIMATEDVALUES_TEST_SERIES_NAME:
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