Changeset 367

Timestamp:

07/07/08 16:59:35 (16 years ago)

Author:

gkronber

Message:

fixed small bugs in GP evaluators

Location:

trunk/sources/HeuristicLab.StructureIdentification/Evaluation

Files:

• CoefficientOfDeterminationEvaluator.cs (modified) (1 diff)
• EarlyStoppingMeanSquaredErrorEvaluator.cs (modified) (1 diff)
• MCCEvaluator.cs (modified) (2 diffs)
• MeanSquaredErrorEvaluator.cs (modified) (1 diff)
• VarianceAccountedForEvaluator.cs (modified) (2 diffs)

trunk/sources/HeuristicLab.StructureIdentification/Evaluation/CoefficientOfDeterminationEvaluator.cs

@@ -44 +44 @@
 
     public override double Evaluate(IScope scope, IFunctionTree functionTree, int targetVariable, Dataset dataset) {
+      int trainingStart = GetVariableValue<IntData>("TrainingSamplesStart", scope, true).Data;
+      int trainingEnd = GetVariableValue<IntData>("TrainingSamplesEnd", scope, true).Data;
       double errorsSquaredSum = 0.0;
       double originalDeviationTotalSumOfSquares = 0.0;
-      double targetMean = dataset.GetMean(targetVariable);
+      double targetMean = dataset.GetMean(targetVariable, trainingStart, trainingEnd);
       functionTree.PrepareEvaluation(dataset);
-      for(int sample = 0; sample < dataset.Rows; sample++) {
+      for(int sample = trainingStart; sample < trainingEnd; sample++) {
         double estimated = functionTree.Evaluate(sample);
         double original = dataset.GetValue(sample, targetVariable);

trunk/sources/HeuristicLab.StructureIdentification/Evaluation/EarlyStoppingMeanSquaredErrorEvaluator.cs

@@ -59 +59 @@
       }
       double errorsSquaredSum = 0;
-      double targetMean = dataset.GetMean(targetVariable);
+      double targetMean = dataset.GetMean(targetVariable, trainingStart, trainingEnd);
       functionTree.PrepareEvaluation(dataset);
       for(int sample = trainingStart; sample < trainingEnd; sample++) {

trunk/sources/HeuristicLab.StructureIdentification/Evaluation/MCCEvaluator.cs

@@ -46 +46 @@
     private double[] estimated = new double[1];
     public override double Evaluate(IScope scope, IFunctionTree functionTree, int targetVariable, Dataset dataset) {
+      int trainingStart = GetVariableValue<IntData>("TrainingSamplesStart", scope, true).Data;
+      int trainingEnd = GetVariableValue<IntData>("TrainingSamplesEnd", scope, true).Data;
+      int nSamples = trainingEnd-trainingStart;
       double limit = GetVariableValue<DoubleData>("ClassSeparation", scope, false).Data;
-      if(estimated.Length != dataset.Rows) {
-        estimated = new double[dataset.Rows];
-        original = new double[dataset.Rows];
+      if(estimated.Length != nSamples) {
+        estimated = new double[nSamples];
+        original = new double[nSamples];
       }
+
       double positive = 0;
       double negative = 0;
-      double targetMean = dataset.GetMean(targetVariable);
+      double targetMean = dataset.GetMean(targetVariable, trainingStart, trainingEnd);
       functionTree.PrepareEvaluation(dataset);
-      for(int sample = 0; sample < dataset.Rows; sample++) {
+      for(int sample = trainingStart; sample < trainingEnd; sample++) {
         double est = functionTree.Evaluate(sample);
         double orig = dataset.GetValue(sample, targetVariable);
@@ -65 +69 @@
           est = targetMean - maximumPunishment;
         }
-        estimated[sample] = est;
-        original[sample] = orig;
+        estimated[sample-trainingStart] = est;
+        original[sample-trainingStart] = orig;
         if(orig >= limit) positive++;
         else negative++;

trunk/sources/HeuristicLab.StructureIdentification/Evaluation/MeanSquaredErrorEvaluator.cs

@@ -48 +48 @@
 
     public override double Evaluate(IScope scope, IFunctionTree functionTree, int targetVariable, Dataset dataset) {
-      double errorsSquaredSum = 0;
-      double targetMean = dataset.GetMean(targetVariable);
-      bool useEstimatedValues = GetVariableValue<BoolData>("UseEstimatedTargetValue", scope, false).Data;
       int trainingStart = GetVariableValue<IntData>("TrainingSamplesStart", scope, true).Data;
       int trainingEnd = GetVariableValue<IntData>("TrainingSamplesEnd", scope, true).Data;
+      double errorsSquaredSum = 0;
+      double targetMean = dataset.GetMean(targetVariable, trainingStart, trainingEnd);
+      bool useEstimatedValues = GetVariableValue<BoolData>("UseEstimatedTargetValue", scope, false).Data;
       if(useEstimatedValues && backupValues == null) {
         backupValues = new double[trainingEnd - trainingStart];

trunk/sources/HeuristicLab.StructureIdentification/Evaluation/VarianceAccountedForEvaluator.cs

@@ -54 +54 @@
 
     public override double Evaluate(IScope scope, IFunctionTree functionTree, int targetVariable, Dataset dataset) {
-      double[] errors = new double[dataset.Rows];
-      double[] originalTargetVariableValues = new double[dataset.Rows];
-      double targetMean = dataset.GetMean(targetVariable);
+      int trainingStart = GetVariableValue<IntData>("TrainingSamplesStart", scope, true).Data;
+      int trainingEnd = GetVariableValue<IntData>("TrainingSamplesEnd", scope, true).Data;
+      double[] errors = new double[trainingEnd-trainingStart];
+      double[] originalTargetVariableValues = new double[trainingEnd-trainingStart];
+      double targetMean = dataset.GetMean(targetVariable, trainingStart, trainingEnd);
       functionTree.PrepareEvaluation(dataset);
-      for(int sample = 0; sample < dataset.Rows; sample++) {
+      for(int sample = trainingStart; sample < trainingEnd; sample++) {
         double estimated = functionTree.Evaluate(sample);
         double original = dataset.GetValue(sample, targetVariable);
@@ -70 +72 @@
         }
 
-        errors[sample] = original - estimated;
-        originalTargetVariableValues[sample] = original;
+        errors[sample-trainingStart] = original - estimated;
+        originalTargetVariableValues[sample-trainingStart] = original;
       }