- Timestamp:
- 07/10/12 15:26:13 (12 years ago)
- Location:
- branches/DatasetFeatureCorrelation/HeuristicLab.Problems.DataAnalysis.Views
- Files:
-
- 4 added
- 15 edited
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- Unmodified
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- Removed
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branches/DatasetFeatureCorrelation/HeuristicLab.Problems.DataAnalysis.Views
- Property svn:mergeinfo changed
/trunk/sources/HeuristicLab.Problems.DataAnalysis.Views (added) merged: 8098,8100,8102,8104-8105,8125,8139,8173,8176
- Property svn:mergeinfo changed
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branches/DatasetFeatureCorrelation/HeuristicLab.Problems.DataAnalysis.Views/3.4/Classification/ClassificationEnsembleSolutionEstimatedClassValuesView.cs
r7259 r8276 73 73 } 74 74 75 int[] indi zes;75 int[] indices; 76 76 double[] estimatedClassValues; 77 77 78 78 switch (SamplesComboBox.SelectedItem.ToString()) { 79 79 case SamplesComboBoxAllSamples: { 80 indi zes = Enumerable.Range(0, Content.ProblemData.Dataset.Rows).ToArray();80 indices = Enumerable.Range(0, Content.ProblemData.Dataset.Rows).ToArray(); 81 81 estimatedClassValues = Content.EstimatedClassValues.ToArray(); 82 82 break; 83 83 } 84 84 case SamplesComboBoxTrainingSamples: { 85 indi zes = Content.ProblemData.TrainingIndizes.ToArray();85 indices = Content.ProblemData.TrainingIndices.ToArray(); 86 86 estimatedClassValues = Content.EstimatedTrainingClassValues.ToArray(); 87 87 break; 88 88 } 89 89 case SamplesComboBoxTestSamples: { 90 indi zes = Content.ProblemData.TestIndizes.ToArray();90 indices = Content.ProblemData.TestIndices.ToArray(); 91 91 estimatedClassValues = Content.EstimatedTestClassValues.ToArray(); 92 92 break; … … 98 98 int classValuesCount = Content.ProblemData.ClassValues.Count; 99 99 int solutionsCount = Content.ClassificationSolutions.Count(); 100 string[,] values = new string[indi zes.Length, 5 + classValuesCount + solutionsCount];100 string[,] values = new string[indices.Length, 5 + classValuesCount + solutionsCount]; 101 101 double[] target = Content.ProblemData.Dataset.GetDoubleValues(Content.ProblemData.TargetVariable).ToArray(); 102 List<List<double?>> estimatedValuesVector = GetEstimatedValues(SamplesComboBox.SelectedItem.ToString(), indi zes,102 List<List<double?>> estimatedValuesVector = GetEstimatedValues(SamplesComboBox.SelectedItem.ToString(), indices, 103 103 Content.ClassificationSolutions); 104 104 105 for (int i = 0; i < indi zes.Length; i++) {106 int row = indi zes[i];105 for (int i = 0; i < indices.Length; i++) { 106 int row = indices[i]; 107 107 values[i, 0] = row.ToString(); 108 108 values[i, 1] = target[i].ToString(); -
branches/DatasetFeatureCorrelation/HeuristicLab.Problems.DataAnalysis.Views/3.4/Classification/ClassificationSolutionConfusionMatrixView.cs
r7259 r8276 107 107 double[] predictedValues; 108 108 if (cmbSamples.SelectedItem.ToString() == TrainingSamples) { 109 rows = Content.ProblemData.TrainingIndi zes;109 rows = Content.ProblemData.TrainingIndices; 110 110 predictedValues = Content.EstimatedTrainingClassValues.ToArray(); 111 111 } else if (cmbSamples.SelectedItem.ToString() == TestSamples) { 112 rows = Content.ProblemData.TestIndi zes;112 rows = Content.ProblemData.TestIndices; 113 113 predictedValues = Content.EstimatedTestClassValues.ToArray(); 114 114 } else throw new InvalidOperationException(); -
branches/DatasetFeatureCorrelation/HeuristicLab.Problems.DataAnalysis.Views/3.4/Classification/ClassificationSolutionEstimatedClassValuesView.cs
r7259 r8276 96 96 var estimatedTraining = Content.EstimatedTrainingClassValues.GetEnumerator(); 97 97 estimatedTraining.MoveNext(); 98 foreach (var trainingRow in Content.ProblemData.TrainingIndi zes) {98 foreach (var trainingRow in Content.ProblemData.TrainingIndices) { 99 99 values[trainingRow, 3] = estimatedTraining.Current.ToString(); 100 100 estimatedTraining.MoveNext(); … … 102 102 var estimatedTest = Content.EstimatedTestClassValues.GetEnumerator(); 103 103 estimatedTest.MoveNext(); 104 foreach (var testRow in Content.ProblemData.TestIndi zes) {104 foreach (var testRow in Content.ProblemData.TestIndices) { 105 105 values[testRow, 4] = estimatedTest.Current.ToString(); 106 106 estimatedTest.MoveNext(); -
branches/DatasetFeatureCorrelation/HeuristicLab.Problems.DataAnalysis.Views/3.4/Classification/DiscriminantFunctionClassificationRocCurvesView.cs
r7259 r8276 101 101 102 102 if (cmbSamples.SelectedItem.ToString() == TrainingSamples) { 103 rows = Content.ProblemData.TrainingIndi zes;103 rows = Content.ProblemData.TrainingIndices; 104 104 } else if (cmbSamples.SelectedItem.ToString() == TestSamples) { 105 rows = Content.ProblemData.TestIndi zes;105 rows = Content.ProblemData.TestIndices; 106 106 } else throw new InvalidOperationException(); 107 107 -
branches/DatasetFeatureCorrelation/HeuristicLab.Problems.DataAnalysis.Views/3.4/Classification/DiscriminantFunctionClassificationSolutionThresholdView.cs
r7259 r8276 137 137 var targetValues = Content.ProblemData.Dataset.GetDoubleValues(Content.ProblemData.TargetVariable).ToList(); 138 138 139 foreach (int row in Content.ProblemData.TrainingIndi zes) {139 foreach (int row in Content.ProblemData.TrainingIndices) { 140 140 double estimatedValue = estimatedValues[row]; 141 141 double targetValue = targetValues[row]; … … 150 150 } 151 151 152 foreach (int row in Content.ProblemData.TestIndi zes) {152 foreach (int row in Content.ProblemData.TestIndices) { 153 153 double estimatedValue = estimatedValues[row]; 154 154 double targetValue = targetValues[row]; -
branches/DatasetFeatureCorrelation/HeuristicLab.Problems.DataAnalysis.Views/3.4/HeuristicLab.Problems.DataAnalysis.Views-3.4.csproj
r8038 r8276 93 93 </PropertyGroup> 94 94 <ItemGroup> 95 <Reference Include="HeuristicLab.Analysis-3.3, Version=3.3.0.0, Culture=neutral, PublicKeyToken=ba48961d6f65dcec, processorArchitecture=MSIL" /> 96 <Reference Include="HeuristicLab.Analysis.Views-3.3, Version=3.3.0.0, Culture=neutral, PublicKeyToken=ba48961d6f65dcec, processorArchitecture=MSIL" /> 95 97 <Reference Include="HeuristicLab.Collections-3.3, Version=3.3.0.0, Culture=neutral, PublicKeyToken=ba48961d6f65dcec, processorArchitecture=MSIL"> 96 98 <Private>False</Private> … … 170 172 <DependentUpon>ClassificationEnsembleSolutionModelView.cs</DependentUpon> 171 173 </Compile> 174 <Compile Include="CorrelationHeatMapView.cs"> 175 <SubType>UserControl</SubType> 176 </Compile> 177 <Compile Include="CorrelationHeatMapView.Designer.cs"> 178 <DependentUpon>CorrelationHeatMapView.cs</DependentUpon> 179 </Compile> 172 180 <Compile Include="DataAnalysisSolutionEvaluationView.cs"> 173 181 <SubType>UserControl</SubType> … … 175 183 <Compile Include="DataAnalysisSolutionEvaluationView.Designer.cs"> 176 184 <DependentUpon>DataAnalysisSolutionEvaluationView.cs</DependentUpon> 185 </Compile> 186 <Compile Include="ExtendedHeatMapView.cs"> 187 <SubType>UserControl</SubType> 188 </Compile> 189 <Compile Include="ExtendedHeatMapView.Designer.cs"> 190 <DependentUpon>ExtendedHeatMapView.cs</DependentUpon> 177 191 </Compile> 178 192 <Compile Include="Plugin.cs" /> -
branches/DatasetFeatureCorrelation/HeuristicLab.Problems.DataAnalysis.Views/3.4/Regression/RegressionSolutionErrorCharacteristicsCurveView.Designer.cs
r7967 r8276 36 36 // 37 37 this.chart.Anchor = ((System.Windows.Forms.AnchorStyles)((((System.Windows.Forms.AnchorStyles.Top | System.Windows.Forms.AnchorStyles.Bottom) 38 | System.Windows.Forms.AnchorStyles.Left)39 | System.Windows.Forms.AnchorStyles.Right)));38 | System.Windows.Forms.AnchorStyles.Left) 39 | System.Windows.Forms.AnchorStyles.Right))); 40 40 chartArea1.Name = "ChartArea1"; 41 41 this.chart.ChartAreas.Add(chartArea1); … … 73 73 // RegressionSolutionErrorCharacteristicsCurveView 74 74 // 75 this.AutoScaleDimensions = new System.Drawing.SizeF(6F, 13F);76 75 this.AutoScaleMode = System.Windows.Forms.AutoScaleMode.Inherit; 77 76 this.Controls.Add(this.label1); -
branches/DatasetFeatureCorrelation/HeuristicLab.Problems.DataAnalysis.Views/3.4/Regression/RegressionSolutionErrorCharacteristicsCurveView.cs
r7701 r8276 26 26 using System.Windows.Forms.DataVisualization.Charting; 27 27 using HeuristicLab.MainForm; 28 using HeuristicLab.MainForm.WindowsForms;29 28 30 29 namespace HeuristicLab.Problems.DataAnalysis.Views { … … 32 31 [Content(typeof(IRegressionSolution))] 33 32 public partial class RegressionSolutionErrorCharacteristicsCurveView : DataAnalysisSolutionEvaluationView { 34 private IRegressionSolution constantModel;35 33 protected const string TrainingSamples = "Training"; 36 34 protected const string TestSamples = "Test"; … … 103 101 if (Content == null) return; 104 102 103 var constantModel = CreateConstantModel(); 105 104 var originalValues = GetOriginalValues().ToList(); 106 constantModel = CreateConstantModel();107 105 var baselineEstimatedValues = GetEstimatedValues(constantModel); 108 106 var baselineResiduals = GetResiduals(originalValues, baselineEstimatedValues); … … 117 115 baselineSeries.ToolTip = "Area over Curve: " + CalculateAreaOverCurve(baselineSeries); 118 116 baselineSeries.Tag = constantModel; 117 baselineSeries.LegendToolTip = "Double-click to open model"; 119 118 chart.Series.Add(baselineSeries); 120 119 … … 131 130 UpdateSeries(estimatedValues, solutionSeries); 132 131 solutionSeries.ToolTip = "Area over Curve: " + CalculateAreaOverCurve(solutionSeries); 132 solutionSeries.LegendToolTip = "Double-click to open model"; 133 133 chart.Series.Add(solutionSeries); 134 134 } … … 169 169 switch (cmbSamples.SelectedItem.ToString()) { 170 170 case TrainingSamples: 171 originalValues = ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TrainingIndi zes);171 originalValues = ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TrainingIndices); 172 172 break; 173 173 case TestSamples: 174 originalValues = ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TestIndi zes);174 originalValues = ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TestIndices); 175 175 break; 176 176 case AllSamples: … … 201 201 } 202 202 203 protected IEnumerable<double> GetbaselineEstimatedValues(IEnumerable<double> originalValues) {204 double averageTrainingTarget = ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TrainingIndizes).Average();205 return Enumerable.Repeat(averageTrainingTarget, originalValues.Count());206 }207 208 203 protected virtual List<double> GetResiduals(IEnumerable<double> originalValues, IEnumerable<double> estimatedValues) { 209 204 return originalValues.Zip(estimatedValues, (x, y) => Math.Abs(x - y)).ToList(); … … 239 234 240 235 private IRegressionSolution CreateConstantModel() { 241 double averageTrainingTarget = ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TrainingIndi zes).Average();236 double averageTrainingTarget = ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TrainingIndices).Average(); 242 237 var solution = new ConstantRegressionModel(averageTrainingTarget).CreateRegressionSolution(ProblemData); 243 238 solution.Name = "Baseline"; … … 248 243 private void chart_MouseMove(object sender, MouseEventArgs e) { 249 244 HitTestResult result = chart.HitTest(e.X, e.Y); 250 if (result.ChartElementType == ChartElementType.LegendItem) 245 if (result.ChartElementType == ChartElementType.LegendItem) { 251 246 Cursor = Cursors.Hand; 252 else247 } else { 253 248 Cursor = Cursors.Default; 249 } 254 250 } 255 251 } -
branches/DatasetFeatureCorrelation/HeuristicLab.Problems.DataAnalysis.Views/3.4/Regression/RegressionSolutionEstimatedValuesView.cs
r7259 r8276 93 93 var estimated_test = Content.EstimatedTestValues.GetEnumerator(); 94 94 95 foreach (var row in Content.ProblemData.TrainingIndi zes) {95 foreach (var row in Content.ProblemData.TrainingIndices) { 96 96 estimated_training.MoveNext(); 97 97 values[row, 3] = estimated_training.Current.ToString(); 98 98 } 99 99 100 foreach (var row in Content.ProblemData.TestIndi zes) {100 foreach (var row in Content.ProblemData.TestIndices) { 101 101 estimated_test.MoveNext(); 102 102 values[row, 4] = estimated_test.Current.ToString(); -
branches/DatasetFeatureCorrelation/HeuristicLab.Problems.DataAnalysis.Views/3.4/Regression/RegressionSolutionLineChartView.cs
r7406 r8276 72 72 this.chart.Series[ESTIMATEDVALUES_TRAINING_SERIES_NAME].ChartType = SeriesChartType.FastLine; 73 73 this.chart.Series[ESTIMATEDVALUES_TRAINING_SERIES_NAME].EmptyPointStyle.Color = this.chart.Series[ESTIMATEDVALUES_TRAINING_SERIES_NAME].Color; 74 this.chart.Series[ESTIMATEDVALUES_TRAINING_SERIES_NAME].Points.DataBindXY(Content.ProblemData.TrainingIndi zes.ToArray(), Content.EstimatedTrainingValues.ToArray());74 this.chart.Series[ESTIMATEDVALUES_TRAINING_SERIES_NAME].Points.DataBindXY(Content.ProblemData.TrainingIndices.ToArray(), Content.EstimatedTrainingValues.ToArray()); 75 75 this.InsertEmptyPoints(this.chart.Series[ESTIMATEDVALUES_TRAINING_SERIES_NAME]); 76 76 this.chart.Series[ESTIMATEDVALUES_TRAINING_SERIES_NAME].Tag = Content; … … 79 79 this.chart.Series[ESTIMATEDVALUES_TEST_SERIES_NAME].LegendText = ESTIMATEDVALUES_TEST_SERIES_NAME; 80 80 this.chart.Series[ESTIMATEDVALUES_TEST_SERIES_NAME].ChartType = SeriesChartType.FastLine; 81 this.chart.Series[ESTIMATEDVALUES_TEST_SERIES_NAME].Points.DataBindXY(Content.ProblemData.TestIndi zes.ToArray(), Content.EstimatedTestValues.ToArray());81 this.chart.Series[ESTIMATEDVALUES_TEST_SERIES_NAME].Points.DataBindXY(Content.ProblemData.TestIndices.ToArray(), Content.EstimatedTestValues.ToArray()); 82 82 this.InsertEmptyPoints(this.chart.Series[ESTIMATEDVALUES_TEST_SERIES_NAME]); 83 83 this.chart.Series[ESTIMATEDVALUES_TEST_SERIES_NAME].Tag = Content; 84 84 // series of remaining points 85 int[] allIndi zes = Enumerable.Range(0, Content.ProblemData.Dataset.Rows).Except(Content.ProblemData.TrainingIndizes).Except(Content.ProblemData.TestIndizes).ToArray();85 int[] allIndices = Enumerable.Range(0, Content.ProblemData.Dataset.Rows).Except(Content.ProblemData.TrainingIndices).Except(Content.ProblemData.TestIndices).ToArray(); 86 86 var estimatedValues = Content.EstimatedValues.ToArray(); 87 List<double> allEstimatedValues = allIndi zes.Select(index => estimatedValues[index]).ToList();87 List<double> allEstimatedValues = allIndices.Select(index => estimatedValues[index]).ToList(); 88 88 this.chart.Series.Add(ESTIMATEDVALUES_ALL_SERIES_NAME); 89 89 this.chart.Series[ESTIMATEDVALUES_ALL_SERIES_NAME].LegendText = ESTIMATEDVALUES_ALL_SERIES_NAME; 90 90 this.chart.Series[ESTIMATEDVALUES_ALL_SERIES_NAME].ChartType = SeriesChartType.FastLine; 91 this.chart.Series[ESTIMATEDVALUES_ALL_SERIES_NAME].Points.DataBindXY(allIndi zes, allEstimatedValues);91 this.chart.Series[ESTIMATEDVALUES_ALL_SERIES_NAME].Points.DataBindXY(allIndices, allEstimatedValues); 92 92 this.InsertEmptyPoints(this.chart.Series[ESTIMATEDVALUES_ALL_SERIES_NAME]); 93 93 this.chart.Series[ESTIMATEDVALUES_ALL_SERIES_NAME].Tag = Content; … … 170 170 171 171 int[] attr = new int[Content.ProblemData.Dataset.Rows + 1]; // add a virtual last row that is again empty to simplify loop further down 172 foreach (var row in Content.ProblemData.TrainingIndi zes) {172 foreach (var row in Content.ProblemData.TrainingIndices) { 173 173 attr[row] += 1; 174 174 } 175 foreach (var row in Content.ProblemData.TestIndi zes) {175 foreach (var row in Content.ProblemData.TestIndices) { 176 176 attr[row] += 2; 177 177 } … … 223 223 string targetVariableName = Content.ProblemData.TargetVariable; 224 224 225 IEnumerable<int> indi zes = null;225 IEnumerable<int> indices = null; 226 226 IEnumerable<double> predictedValues = null; 227 227 switch (series.Name) { 228 228 case ESTIMATEDVALUES_ALL_SERIES_NAME: 229 indi zes = Enumerable.Range(0, Content.ProblemData.Dataset.Rows).Except(Content.ProblemData.TrainingIndizes).Except(Content.ProblemData.TestIndizes).ToArray();229 indices = Enumerable.Range(0, Content.ProblemData.Dataset.Rows).Except(Content.ProblemData.TrainingIndices).Except(Content.ProblemData.TestIndices).ToArray(); 230 230 var estimatedValues = Content.EstimatedValues.ToArray(); 231 predictedValues = indi zes.Select(index => estimatedValues[index]).ToList();231 predictedValues = indices.Select(index => estimatedValues[index]).ToList(); 232 232 break; 233 233 case ESTIMATEDVALUES_TRAINING_SERIES_NAME: 234 indi zes = Content.ProblemData.TrainingIndizes.ToArray();234 indices = Content.ProblemData.TrainingIndices.ToArray(); 235 235 predictedValues = Content.EstimatedTrainingValues.ToArray(); 236 236 break; 237 237 case ESTIMATEDVALUES_TEST_SERIES_NAME: 238 indi zes = Content.ProblemData.TestIndizes.ToArray();238 indices = Content.ProblemData.TestIndices.ToArray(); 239 239 predictedValues = Content.EstimatedTestValues.ToArray(); 240 240 break; 241 241 } 242 series.Points.DataBindXY(indi zes, predictedValues);242 series.Points.DataBindXY(indices, predictedValues); 243 243 this.InsertEmptyPoints(series); 244 244 chart.Legends[series.Legend].ForeColor = Color.Black; -
branches/DatasetFeatureCorrelation/HeuristicLab.Problems.DataAnalysis.Views/3.4/Regression/RegressionSolutionResidualHistogram.Designer.cs
r7967 r8276 41 41 this.chart.Location = new System.Drawing.Point(0, 0); 42 42 this.chart.Name = "chart"; 43 this.chart.Size = new System.Drawing.Size( 358, 225);43 this.chart.Size = new System.Drawing.Size(289, 220); 44 44 this.chart.TabIndex = 0; 45 45 this.chart.CustomizeLegend += new System.EventHandler<System.Windows.Forms.DataVisualization.Charting.CustomizeLegendEventArgs>(this.chart_CustomizeLegend); … … 50 50 // 51 51 this.AllowDrop = true; 52 this.AutoScaleDimensions = new System.Drawing.SizeF(6F, 13F);53 52 this.AutoScaleMode = System.Windows.Forms.AutoScaleMode.Inherit; 54 53 this.Controls.Add(this.chart); -
branches/DatasetFeatureCorrelation/HeuristicLab.Problems.DataAnalysis.Views/3.4/Regression/RegressionSolutionResidualHistogram.cs
r7503 r8276 39 39 protected const string TEST_SAMPLES = "Test samples"; 40 40 /// <summary> 41 /// used to reduce code duplication42 /// </summary>43 protected static string[] ALL_SERIES = new string[] { ALL_SAMPLES, TRAINING_SAMPLES, TEST_SAMPLES };44 /// <summary>45 41 /// approximate amount of bins 46 42 /// </summary> 47 43 protected const double bins = 25; 48 /// <summary>49 /// keeps for all series a list for every bin with the position of the bin, the relative frequency of the50 /// residuals and the beginning and the end of the interval of the bin51 /// </summary>52 protected Dictionary<string, List<List<double>>> relativeFrequencies;53 44 #endregion 54 45 … … 61 52 : base() { 62 53 InitializeComponent(); 63 relativeFrequencies = new Dictionary<string, List<List<double>>>(); 64 foreach (string series in ALL_SERIES) { 54 foreach (string series in new List<String>() { ALL_SAMPLES, TRAINING_SAMPLES, TEST_SAMPLES }) { 65 55 chart.Series.Add(series); 66 56 chart.Series[series].LegendText = series; … … 71 61 chart.Series[series].BorderColor = Color.Black; 72 62 chart.Series[series].ToolTip = series + " Y = #VALY from #CUSTOMPROPERTY(from) to #CUSTOMPROPERTY(to)"; 73 relativeFrequencies[series] = new List<List<double>>();74 63 } 75 64 //configure axis … … 87 76 88 77 private void RedrawChart() { 89 foreach (string series in ALL_SERIES) { 90 chart.Series[series].Points.Clear(); 91 relativeFrequencies[series].Clear(); 78 foreach (Series series in chart.Series) { 79 series.Points.Clear(); 92 80 } 93 81 if (Content != null) { 94 Dictionary<string, List<double>> residuals = CalculateResiduals(); 95 double realMax = Math.Max(Math.Abs(residuals[ALL_SAMPLES].Min()), Math.Abs(residuals[ALL_SAMPLES].Max())); 96 double roundedMax = HumanRoundMax(realMax); 97 double intervalWidth = (roundedMax * 2.0) / bins; 98 intervalWidth = HumanRoundMax(intervalWidth); 99 // sets roundedMax to a value, so that zero will be in the middle of the x axis 100 double help = realMax / intervalWidth; 101 help = help % 1 < 0.5 ? (int)help : (int)help + 1; 102 roundedMax = help * intervalWidth; 103 104 foreach (string series in ALL_SERIES) { 105 CalculateFrequencies(residuals[series], series, roundedMax, intervalWidth); 106 if (!series.Equals(ALL_SAMPLES)) 107 ShowValues(chart.Series[series], relativeFrequencies[series]); 82 List<double> residuals = CalculateResiduals(Content); 83 84 double max = 0.0; 85 foreach (Series series in chart.Series) { 86 CalculateFrequencies(residuals, series); 87 double seriesMax = series.Points.Select(p => p.YValues.First()).Max(); 88 max = max < seriesMax ? seriesMax : max; 108 89 } 90 91 // ALL_SAMPLES has to be calculated to know its highest frequency, but it is not shown in the beginning 92 chart.Series.First(s => s.Name.Equals(ALL_SAMPLES)).Points.Clear(); 93 94 double roundedMax, intervalWidth; 95 CalculateResidualParameters(residuals, out roundedMax, out intervalWidth); 109 96 110 97 ChartArea chartArea = chart.ChartAreas[0]; … … 112 99 chartArea.AxisX.Maximum = roundedMax + intervalWidth; 113 100 // get the highest frequency of a residual of any series 114 chartArea.AxisY.Maximum = (from series in relativeFrequencies.Values 115 select (from residual in series 116 select residual.ElementAt(1)).Max()).Max(); 101 chartArea.AxisY.Maximum = max; 117 102 if (chartArea.AxisY.Maximum < 0.1) { 118 103 chartArea.AxisY.Interval = 0.01; … … 132 117 } 133 118 134 private Dictionary<string, List<double>> CalculateResiduals() { 135 Dictionary<string, List<double>> residuals = new Dictionary<string, List<double>>(); 136 137 foreach (string series in ALL_SERIES) { 138 residuals[series] = new List<double>(); 139 } 119 private List<double> CalculateResiduals(IRegressionSolution solution) { 120 List<double> residuals = new List<double>(); 121 122 IRegressionProblemData problemdata = solution.ProblemData; 123 List<double> targetValues = problemdata.Dataset.GetDoubleValues(Content.ProblemData.TargetVariable).ToList(); 124 List<double> estimatedValues = solution.EstimatedValues.ToList(); 125 126 for (int i = 0; i < solution.ProblemData.Dataset.Rows; i++) { 127 double residual = estimatedValues[i] - targetValues[i]; 128 residuals.Add(residual); 129 } 130 return residuals; 131 } 132 133 private void CalculateFrequencies(List<double> residualValues, Series series) { 134 double roundedMax, intervalWidth; 135 CalculateResidualParameters(residualValues, out roundedMax, out intervalWidth); 136 137 IEnumerable<double> relevantResiduals = residualValues; 140 138 IRegressionProblemData problemdata = Content.ProblemData; 141 List<double> targetValues = problemdata.Dataset.GetDoubleValues(Content.ProblemData.TargetVariable).ToList(); 142 List<double> estimatedValues = Content.EstimatedValues.ToList(); 143 144 for (int i = 0; i < Content.ProblemData.Dataset.Rows; i++) { 145 double residual = estimatedValues[i] - targetValues[i]; 146 residuals[ALL_SAMPLES].Add(residual); 147 if (i >= problemdata.TrainingPartition.Start && i < problemdata.TrainingPartition.End) 148 residuals[TRAINING_SAMPLES].Add(residual); 149 if (i >= problemdata.TestPartition.Start && i < problemdata.TestPartition.End) 150 residuals[TEST_SAMPLES].Add(residual); 151 } 152 return residuals; 153 } 154 155 private void CalculateFrequencies(List<double> residualValues, string series, double max, double intervalWidth) { 139 if (series.Name.Equals(TRAINING_SAMPLES)) { 140 relevantResiduals = residualValues.Skip(problemdata.TrainingPartition.Start).Take(problemdata.TrainingPartition.Size); 141 } else if (series.Name.Equals(TEST_SAMPLES)) { 142 relevantResiduals = residualValues.Skip(problemdata.TestPartition.Start).Take(problemdata.TestPartition.Size); 143 } 144 156 145 double intervalCenter = intervalWidth / 2.0; 157 double sampleCount = residualValues.Count(); 158 double current = -max; 146 double sampleCount = relevantResiduals.Count(); 147 double current = -roundedMax; 148 DataPointCollection seriesPoints = series.Points; 159 149 160 150 for (int i = 0; i <= bins; i++) { 161 IEnumerable<double> help = residualValues.Where(x => x >= (current - intervalCenter) && x < (current + intervalCenter)); 162 relativeFrequencies[series].Add(new List<double>() { current, help.Count() / sampleCount, current - intervalCenter, current + intervalCenter }); 151 IEnumerable<double> help = relevantResiduals.Where(x => x >= (current - intervalCenter) && x < (current + intervalCenter)); 152 seriesPoints.AddXY(current, help.Count() / sampleCount); 153 seriesPoints[seriesPoints.Count - 1]["from"] = (current - intervalCenter).ToString(); 154 seriesPoints[seriesPoints.Count - 1]["to"] = (current + intervalCenter).ToString(); 163 155 current += intervalWidth; 164 156 } 165 157 } 166 158 167 private double HumanRoundMax(double max) { 159 private void ToggleSeriesData(Series series) { 160 if (series.Points.Count > 0) { //checks if series is shown 161 if (chart.Series.Any(s => s != series && s.Points.Count > 0)) { 162 series.Points.Clear(); 163 } 164 } else if (Content != null) { 165 List<double> residuals = CalculateResiduals(Content); 166 CalculateFrequencies(residuals, series); 167 chart.Legends[series.Legend].ForeColor = Color.Black; 168 chart.Refresh(); 169 } 170 } 171 172 private static void CalculateResidualParameters(List<double> residuals, out double roundedMax, out double intervalWidth) { 173 double realMax = Math.Max(Math.Abs(residuals.Min()), Math.Abs(residuals.Max())); 174 roundedMax = HumanRoundMax(realMax); 175 intervalWidth = (roundedMax * 2.0) / bins; 176 intervalWidth = HumanRoundMax(intervalWidth); 177 // sets roundedMax to a value, so that zero will be in the middle of the x axis 178 double help = realMax / intervalWidth; 179 help = help % 1 < 0.5 ? (int)help : (int)help + 1; 180 roundedMax = help * intervalWidth; 181 } 182 183 private static double HumanRoundMax(double max) { 168 184 double base10; 169 185 if (max > 0) base10 = Math.Pow(10.0, Math.Floor(Math.Log10(max))); … … 216 232 } 217 233 #endregion 218 219 private void ToggleSeriesData(Series series) {220 if (series.Points.Count > 0) { //checks if series is shown221 if (chart.Series.Any(s => s != series && s.Points.Count > 0)) {222 series.Points.Clear();223 }224 } else if (Content != null) {225 ShowValues(series, relativeFrequencies[series.Name]);226 chart.Legends[series.Legend].ForeColor = Color.Black;227 chart.Refresh();228 }229 }230 private void ShowValues(Series series, List<List<double>> relativeSeriesFrequencies) {231 DataPointCollection seriesPoints = series.Points;232 233 foreach (var valueList in relativeSeriesFrequencies) {234 seriesPoints.AddXY(valueList[0], valueList[1]);235 seriesPoints[seriesPoints.Count - 1]["from"] = valueList[2].ToString();236 seriesPoints[seriesPoints.Count - 1]["to"] = valueList[3].ToString();237 }238 }239 234 } 240 235 } -
branches/DatasetFeatureCorrelation/HeuristicLab.Problems.DataAnalysis.Views/3.4/Regression/RegressionSolutionScatterPlotView.cs
r7990 r8276 148 148 if (this.chart.Series[TRAINING_SERIES].Points.Count > 0) 149 149 this.chart.Series[TRAINING_SERIES].Points.DataBindXY(Content.EstimatedTrainingValues.ToArray(), "", 150 dataset.GetDoubleValues(targetVariableName, Content.ProblemData.TrainingIndi zes).ToArray(), "");150 dataset.GetDoubleValues(targetVariableName, Content.ProblemData.TrainingIndices).ToArray(), ""); 151 151 if (this.chart.Series[TEST_SERIES].Points.Count > 0) 152 152 this.chart.Series[TEST_SERIES].Points.DataBindXY(Content.EstimatedTestValues.ToArray(), "", 153 dataset.GetDoubleValues(targetVariableName, Content.ProblemData.TestIndi zes).ToArray(), "");153 dataset.GetDoubleValues(targetVariableName, Content.ProblemData.TestIndices).ToArray(), ""); 154 154 155 155 double max = Content.EstimatedTrainingValues.Concat(Content.EstimatedTestValues.Concat(Content.EstimatedValues.Concat(dataset.GetDoubleValues(targetVariableName)))).Max(); … … 196 196 case TRAINING_SERIES: 197 197 predictedValues = Content.EstimatedTrainingValues.ToArray(); 198 targetValues = Content.ProblemData.Dataset.GetDoubleValues(targetVariableName, Content.ProblemData.TrainingIndi zes).ToArray();198 targetValues = Content.ProblemData.Dataset.GetDoubleValues(targetVariableName, Content.ProblemData.TrainingIndices).ToArray(); 199 199 break; 200 200 case TEST_SERIES: 201 201 predictedValues = Content.EstimatedTestValues.ToArray(); 202 targetValues = Content.ProblemData.Dataset.GetDoubleValues(targetVariableName, Content.ProblemData.TestIndi zes).ToArray();202 targetValues = Content.ProblemData.Dataset.GetDoubleValues(targetVariableName, Content.ProblemData.TestIndices).ToArray(); 203 203 break; 204 204 } -
branches/DatasetFeatureCorrelation/HeuristicLab.Problems.DataAnalysis.Views/3.4/Solution Views/DataAnalysisSolutionView.cs
r7259 r8276 137 137 } 138 138 139 protected override void showDetailsCheckBox_CheckedChanged(object sender, EventArgs e) { 140 if (showDetailsCheckBox.Checked && itemsListView.SelectedItems.Count == 1 && itemsListView.SelectedItems[0].Tag is Type) { 141 Type viewType = (Type)itemsListView.SelectedItems[0].Tag; 142 viewHost.ViewType = viewType; 143 viewHost.Content = Content; 144 splitContainer.Panel2Collapsed = false; 145 detailsGroupBox.Enabled = true; 146 } else base.showDetailsCheckBox_CheckedChanged(sender, e); 147 } 148 139 149 #region drag and drop 140 150 protected override void itemsListView_DragEnter(object sender, DragEventArgs e) {
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