- Timestamp:
- 03/15/11 08:25:27 (13 years ago)
- Location:
- branches/DataAnalysis Refactoring/HeuristicLab.Algorithms.DataAnalysis
- Files:
-
- 3 edited
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branches/DataAnalysis Refactoring/HeuristicLab.Algorithms.DataAnalysis/3.3
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old new 3 3 HeuristicLabAlgorithmsDataAnalysisPlugin.cs 4 4 obj 5 *.vs10x
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branches/DataAnalysis Refactoring/HeuristicLab.Algorithms.DataAnalysis/3.4
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old new 3 3 HeuristicLabAlgorithmsDataAnalysisPlugin.cs 4 4 HeuristicLab.Algorithms.DataAnalysis-3.4.csproj.user 5 *.vs10x
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branches/DataAnalysis Refactoring/HeuristicLab.Algorithms.DataAnalysis/3.4/Linear/LinearDiscriminantAnalysis.cs
r5680 r5681 120 120 IEnumerable<int> rows) { 121 121 string targetVariable = problemData.TargetVariable; 122 List<double> originalClasses = problemData.ClassValues.ToList(); 123 int nClasses = problemData.Classes; 124 List<double> estimatedValues = interpreter.GetSymbolicExpressionTreeValues(tree, problemData.Dataset, rows).ToList(); 125 double maxEstimatedValue = estimatedValues.Max(); 126 double minEstimatedValue = estimatedValues.Min(); 127 var estimatedTargetValues = 128 (from row in problemData.TrainingIndizes 129 select new { EstimatedValue = estimatedValues[row], TargetValue = problemData.Dataset[targetVariable, row] }) 130 .ToList(); 122 var estimatedValues = interpreter.GetSymbolicExpressionTreeValues(tree, problemData.Dataset, rows); 123 var targetClassValues = problemData.Dataset.GetEnumeratedVariableValues(targetVariable, rows); 131 124 132 Dictionary<double, double> classMean = new Dictionary<double, double>(); 133 Dictionary<double, double> classStdDev = new Dictionary<double, double>(); 134 // calculate moments per class 135 foreach (var classValue in originalClasses) { 136 var estimatedValuesForClass = from x in estimatedTargetValues 137 where x.TargetValue == classValue 138 select x.EstimatedValue; 139 double mean, variance; 140 OnlineMeanAndVarianceCalculator.Calculate(estimatedValuesForClass, out mean, out variance); 141 classMean[classValue] = mean; 142 classStdDev[classValue] = Math.Sqrt(variance); 143 } 144 List<double> thresholds = new List<double>(); 145 for (int i = 0; i < nClasses - 1; i++) { 146 for (int j = i + 1; j < nClasses; j++) { 147 double x1, x2; 148 double class0 = originalClasses[i]; 149 double class1 = originalClasses[j]; 150 // calculate all thresholds 151 CalculateCutPoints(classMean[class0], classStdDev[class0], classMean[class1], classStdDev[class1], out x1, out x2); 152 if (!thresholds.Any(x => x.IsAlmost(x1))) thresholds.Add(x1); 153 if (!thresholds.Any(x => x.IsAlmost(x2))) thresholds.Add(x2); 154 } 155 } 156 thresholds.Sort(); 157 thresholds.Insert(0, double.NegativeInfinity); 158 thresholds.Add(double.PositiveInfinity); 159 List<double> classValues = new List<double>(); 160 for (int i = 0; i < thresholds.Count - 1; i++) { 161 double m; 162 if (double.IsNegativeInfinity(thresholds[i])) { 163 m = thresholds[i + 1] - 1.0; 164 } else if (double.IsPositiveInfinity(thresholds[i + 1])) { 165 m = thresholds[i] + 1.0; 166 } else { 167 m = thresholds[i] + (thresholds[i + 1] - thresholds[i]) / 2.0; 168 } 169 170 double maxDensity = 0; 171 double maxDensityClassValue = -1; 172 foreach (var classValue in originalClasses) { 173 double density = NormalDensity(m, classMean[classValue], classStdDev[classValue]); 174 if (density > maxDensity) { 175 maxDensity = density; 176 maxDensityClassValue = classValue; 177 } 178 } 179 classValues.Add(maxDensityClassValue); 180 } 181 List<double> filteredThresholds = new List<double>(); 182 List<double> filteredClassValues = new List<double>(); 183 filteredThresholds.Add(thresholds[0]); 184 filteredClassValues.Add(classValues[0]); 185 for (int i = 0; i < classValues.Count - 1; i++) { 186 if (classValues[i] != classValues[i + 1]) { 187 filteredThresholds.Add(thresholds[i + 1]); 188 filteredClassValues.Add(classValues[i + 1]); 189 } 190 } 191 filteredThresholds.Add(double.PositiveInfinity); 192 193 return new SymbolicDiscriminantFunctionClassificationModel(tree, interpreter, filteredClassValues, filteredThresholds); 194 } 195 196 private static double NormalDensity(double x, double mu, double sigma) { 197 return (1.0 / Math.Sqrt(2.0 * Math.PI * sigma * sigma)) * Math.Exp(-((x - mu) * (x - mu)) / (2.0 * sigma * sigma)); 198 } 199 200 private static void CalculateCutPoints(double m1, double s1, double m2, double s2, out double x1, out double x2) { 201 double a = (s1 * s1 - s2 * s2); 202 x1 = -(-m2 * s1 * s1 + m1 * s2 * s2 + Math.Sqrt(s1 * s1 * s2 * s2 * ((m1 - m2) * (m1 - m2) + 2.0 * (-s1 * s1 + s2 * s2) * Math.Log(s2 / s1)))) / a; 203 x2 = (m2 * s1 * s1 - m1 * s2 * s2 + Math.Sqrt(s1 * s1 * s2 * s2 * ((m1 - m2) * (m1 - m2) + 2.0 * (-s1 * s1 + s2 * s2) * Math.Log(s2 / s1)))) / a; 125 double[] classValues; 126 double[] thresholds; 127 NormalDistributionCutPointsThresholdCalculator.CalculateThresholds(problemData, estimatedValues, targetClassValues, out classValues, out thresholds); 128 return new SymbolicDiscriminantFunctionClassificationModel(tree, interpreter, classValues, thresholds); 204 129 } 205 130 }
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