Changeset 2578 for trunk/sources/HeuristicLab.GP.StructureIdentification.Classification

Timestamp:

01/02/10 18:10:15 (15 years ago)

Author:

gkronber

Message:

Implemented #824 (Refactor: ITreeEvaluator interface to provide a method that evaluates a tree on a range of samples.)

Location:

trunk/sources/HeuristicLab.GP.StructureIdentification.Classification/3.3

Files:

• ClassificationMeanSquaredErrorEvaluator.cs (modified) (3 diffs)
• GPClassificationEvaluatorBase.cs (modified) (2 diffs)

trunk/sources/HeuristicLab.GP.StructureIdentification.Classification/3.3/ClassificationMeanSquaredErrorEvaluator.cs ¶

@@ -24 +24 @@
 using HeuristicLab.Data;
 using HeuristicLab.Common;
+using HeuristicLab.GP.Interfaces;
+using System.Linq;
+using HeuristicLab.DataAnalysis;
 
 namespace HeuristicLab.GP.StructureIdentification.Classification {
@@ -40 +43 @@
     }
 
-    public override void Evaluate(IScope scope, ITreeEvaluator evaluator, HeuristicLab.DataAnalysis.Dataset dataset, int targetVariable, double[] classes, double[] thresholds, int start, int end) {
+    public override void Evaluate(IScope scope, IFunctionTree tree, ITreeEvaluator evaluator, Dataset dataset, int targetVariable, double[] classes, double[] thresholds, int start, int end) {
       double errorsSquaredSum = 0;
+      double[] estimatedValues = evaluator.Evaluate(dataset, tree, Enumerable.Range(start, end - start)).ToArray();
       for (int sample = start; sample < end; sample++) {
-        double estimated = evaluator.Evaluate(sample);
         double original = dataset.GetValue(sample, targetVariable);
         if (!double.IsNaN(original) && !double.IsInfinity(original)) {
-          double error = estimated - original;
+          double error = estimatedValues[sample - start] - original;
           // between classes use squared error
           // on the lower end and upper end only add linear error if the absolute error is larger than 1
@@ -57 +60 @@
           }
         }
+
       }
 

trunk/sources/HeuristicLab.GP.StructureIdentification.Classification/3.3/GPClassificationEvaluatorBase.cs ¶

@@ -24 +24 @@
 using HeuristicLab.Data;
 using HeuristicLab.DataAnalysis;
+using HeuristicLab.GP.Interfaces;
 
 namespace HeuristicLab.GP.StructureIdentification.Classification {
@@ -33 +34 @@
     }
 
-    public override void Evaluate(IScope scope, ITreeEvaluator evaluator, Dataset dataset, int targetVariable, int start, int end) {
+    public override void Evaluate(IScope scope, IFunctionTree tree, ITreeEvaluator evaluator, Dataset dataset, int targetVariable, int start, int end) {
 
       ItemList<DoubleData> classes = GetVariableValue<ItemList<DoubleData>>("TargetClassValues", scope, true);
       double[] classesArr = new double[classes.Count];
-      for(int i = 0; i < classesArr.Length; i++) classesArr[i] = classes[i].Data;
+      for (int i = 0; i < classesArr.Length; i++) classesArr[i] = classes[i].Data;
       Array.Sort(classesArr);
       double[] thresholds = new double[classes.Count - 1];
-      for(int i = 0; i < classesArr.Length - 1; i++) {
+      for (int i = 0; i < classesArr.Length - 1; i++) {
         thresholds[i] = (classesArr[i] + classesArr[i + 1]) / 2.0;
       }
 
-      Evaluate(scope, evaluator, dataset, targetVariable, classesArr, thresholds, start, end);
+      Evaluate(scope, tree, evaluator, dataset, targetVariable, classesArr, thresholds, start, end);
     }
 
-    public abstract void Evaluate(IScope scope, ITreeEvaluator evaluator, Dataset dataset, int targetVariable, double[] classes, double[] thresholds, int start, int end);
+    public abstract void Evaluate(IScope scope, IFunctionTree tree, ITreeEvaluator evaluator, Dataset dataset, int targetVariable, double[] classes, double[] thresholds, int start, int end);
   }
 }