Changeset 17472 for branches/3040_VectorBasedGP/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression.Views/3.4

Timestamp:

03/10/20 08:28:49 (4 years ago)

Author:

pfleck

Message:

#3040 Moved Alglib+AutoDiff constant optimizer in own class and created base class to provide multiple constant-opt implementations.

File:

• branches/3040_VectorBasedGP/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression.Views/3.4/InteractiveSymbolicRegressionSolutionSimplifierView.cs (modified) (4 diffs)

branches/3040_VectorBasedGP/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression.Views/3.4/InteractiveSymbolicRegressionSolutionSimplifierView.cs

@@ -21 +21 @@
 
 using System;
+using System.Threading;
 using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
 using HeuristicLab.MainForm;
@@ -42 +43 @@
 
       var tree = Content?.Model?.SymbolicExpressionTree;
-      btnOptimizeConstants.Enabled = tree != null && SymbolicRegressionConstantOptimizationEvaluator.CanOptimizeConstants(tree);
+      btnOptimizeConstants.Enabled = tree != null && NonlinearLeastSquaresConstantOptimizationEvaluator.CanOptimizeConstants(tree);
     }
 
@@ -51 +52 @@
     }
 
-    protected override ISymbolicExpressionTree OptimizeConstants(ISymbolicExpressionTree tree, IProgress progress) {
+    protected override ISymbolicExpressionTree OptimizeConstants(ISymbolicExpressionTree tree, CancellationToken cancellationToken, IProgress progress) {
       const int constOptIterations = 50;
       const int maxRepetitions = 100;
@@ -64 +65 @@
       do {
         prevResult = result;
-        result = SymbolicRegressionConstantOptimizationEvaluator.OptimizeConstants(model.Interpreter, tree, regressionProblemData, regressionProblemData.TrainingIndices,
-          applyLinearScaling: true, maxIterations: constOptIterations, updateVariableWeights: true, lowerEstimationLimit: model.LowerEstimationLimit, upperEstimationLimit: model.UpperEstimationLimit,
-          iterationCallback: (args, func, obj) => {
+        tree = NonlinearLeastSquaresConstantOptimizationEvaluator.OptimizeTree(tree, regressionProblemData, regressionProblemData.TrainingIndices,
+          applyLinearScaling: true, maxIterations: constOptIterations, updateVariableWeights: true,
+          cancellationToken: cancellationToken, iterationCallback: (args, func, obj) => {
             double newProgressValue = progress.ProgressValue + (1.0 / (constOptIterations + 2) / maxRepetitions); // (constOptIterations + 2) iterations are reported
             progress.ProgressValue = Math.Min(newProgressValue, 1.0);
           });
+        result = SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator.Calculate(model.Interpreter, tree,
+          model.LowerEstimationLimit, model.UpperEstimationLimit, regressionProblemData, regressionProblemData.TrainingIndices, applyLinearScaling: true);
         reps++;
         improvement = result - prevResult;