Changeset 15480

Timestamp:

11/20/17 20:36:29 (7 years ago)

Author:

gkronber

Message:

#2852: moved the scaling parameters to the end of the parameter vector to be able to remove the c.Skip(2).ToArray() call and removed unnecessary .ToArray() calls

Location:

trunk/sources

Files:

• HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/Evaluators/SymbolicRegressionConstantOptimizationEvaluator.cs (modified) (3 diffs)
• HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/Converters/TreeToAutoDiffTermConverter.cs (modified) (1 diff)

trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/Evaluators/SymbolicRegressionConstantOptimizationEvaluator.cs

@@ -214 +214 @@
         c = new double[initialConstants.Length + 2];
         {
-          c[0] = 0.0;
-          c[1] = 1.0;
-          Array.Copy(initialConstants, 0, c, 2, initialConstants.Length);
+          Array.Copy(initialConstants, 0, c, 0, initialConstants.Length);
+          c[c.Length - 2] = 0.0;
+          c[c.Length - 1] = 1.0;
         }
       } else {
@@ -273 +273 @@
       //retVal == -7  => constant optimization failed due to wrong gradient
       if (retVal != -7) {
-        if (applyLinearScaling) UpdateConstants(tree, c.Skip(2).ToArray(), updateVariableWeights);
-        else UpdateConstants(tree, c.ToArray(), updateVariableWeights);
+        if (applyLinearScaling) UpdateConstants(tree, c, updateVariableWeights);
+        else UpdateConstants(tree, c, updateVariableWeights);
       }
       var quality = SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator.Calculate(interpreter, tree, lowerEstimationLimit, upperEstimationLimit, problemData, rows, applyLinearScaling);
 
-      if (!updateConstantsInTree) UpdateConstants(tree, initialConstants.ToArray(), updateVariableWeights);
+      if (!updateConstantsInTree) UpdateConstants(tree, initialConstants, updateVariableWeights);
 
       if (originalQuality - quality > 0.001 || double.IsNaN(quality)) {
-        UpdateConstants(tree, initialConstants.ToArray(), updateVariableWeights);
+        UpdateConstants(tree, initialConstants, updateVariableWeights);
         return originalQuality;
       }
@@ -314 +314 @@
     private static alglib.ndimensional_pgrad CreatePGrad(TreeToAutoDiffTermConverter.ParametricFunctionGradient func_grad) {
       return (double[] c, double[] x, ref double fx, double[] grad, object o) => {
-        var tupel = func_grad(c, x);
-        fx = tupel.Item2;
-        Array.Copy(tupel.Item1, grad, grad.Length);
+        var tuple = func_grad(c, x);
+        fx = tuple.Item2;
+        Array.Copy(tuple.Item1, grad, grad.Length);
         var counter = (EvaluationsCounter)o;
         counter.GradientEvaluations++;

trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/Converters/TreeToAutoDiffTermConverter.cs

@@ -251 +251 @@
       if (node.Symbol is StartSymbol) {
         if (addLinearScalingTerms) {
+          var t = ConvertToAutoDiff(node.GetSubtree(0));
+          // scaling variables α, β are given at the end of the parameter vector
           var alpha = new AutoDiff.Variable();
           var beta = new AutoDiff.Variable();
           variables.Add(beta);
           variables.Add(alpha);
-          return ConvertToAutoDiff(node.GetSubtree(0)) * alpha + beta;
+          return t * alpha + beta;
         } else return ConvertToAutoDiff(node.GetSubtree(0));
       }