Changeset 16457 for branches/2974_Constants_Optimization/HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/Converters/TreeToAutoDiffTermConverter.cs

Timestamp:

12/28/18 10:18:39 (5 years ago)

Author:

mkommend

Message:

#2974: Extracted linear scaling terms in auto diff converter.

File:

• branches/2974_Constants_Optimization/HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/Converters/TreeToAutoDiffTermConverter.cs (modified) (5 diffs)

branches/2974_Constants_Optimization/HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/Converters/TreeToAutoDiffTermConverter.cs

@@ -98 +98 @@
 
       // use a transformator object which holds the state (variable list, parameter list, ...) for recursive transformation of the tree
-      var transformator = new TreeToAutoDiffTermConverter(makeVariableWeightsVariable, addLinearScalingTerms);
+      var transformator = new TreeToAutoDiffTermConverter(makeVariableWeightsVariable);
       AutoDiff.Term term;
       try {
         term = transformator.ConvertToAutoDiff(tree.Root.GetSubtree(0));
+
+        if (addLinearScalingTerms) {
+          // scaling variables α, β are given at the beginning of the parameter vector
+          var alpha = new AutoDiff.Variable();
+          var beta = new AutoDiff.Variable();
+          transformator.variables.Insert(0, alpha);
+          transformator.variables.Insert(0, beta);
+
+          term = term * alpha + beta;
+        }
+
         var parameterEntries = transformator.parameters.ToArray(); // guarantee same order for keys and values
         var compiledTerm = term.Compile(transformator.variables.ToArray(),
           parameterEntries.Select(kvp => kvp.Value).ToArray());
+
         parameters = new List<DataForVariable>(parameterEntries.Select(kvp => kvp.Key));
         initialConstants = transformator.initialConstants.ToArray();
@@ -111 +123 @@
         return true;
       } catch (ConversionException) {
+        parameters = null;
+        initialConstants = null;
         func = null;
         func_grad = null;
-        parameters = null;
-        initialConstants = null;
       }
       return false;
@@ -122 +134 @@
     private readonly
     List<double> initialConstants;
-    private readonly Dictionary<DataForVariable, AutoDiff.Variable> parameters;
+    private Dictionary<DataForVariable, AutoDiff.Variable> parameters;
     private readonly List<AutoDiff.Variable> variables;
     private readonly bool makeVariableWeightsVariable;
-    private readonly bool addLinearScalingTerms;
-
-    private TreeToAutoDiffTermConverter(bool makeVariableWeightsVariable, bool addLinearScalingTerms) {
+
+    private TreeToAutoDiffTermConverter(bool makeVariableWeightsVariable) {
       this.makeVariableWeightsVariable = makeVariableWeightsVariable;
-      this.addLinearScalingTerms = addLinearScalingTerms;
       this.initialConstants = new List<double>();
       this.parameters = new Dictionary<DataForVariable, AutoDiff.Variable>();
@@ -249 +259 @@
       if (node.Symbol is CubeRoot) {
         return AutoDiff.TermBuilder.Power(
-          ConvertToAutoDiff(node.GetSubtree(0)), 1.0/3.0);
+          ConvertToAutoDiff(node.GetSubtree(0)), 1.0 / 3.0);
       }
       if (node.Symbol is Sine) {
@@ -272 +282 @@
       }
       if (node.Symbol is StartSymbol) {
-        if (addLinearScalingTerms) {
-          // scaling variables α, β are given at the beginning of the parameter vector
-          var alpha = new AutoDiff.Variable();
-          var beta = new AutoDiff.Variable();
-          variables.Add(beta);
-          variables.Add(alpha);
-          var t = ConvertToAutoDiff(node.GetSubtree(0));
-          return t * alpha + beta;
-        } else return ConvertToAutoDiff(node.GetSubtree(0));
+        return ConvertToAutoDiff(node.GetSubtree(0));
       }
       throw new ConversionException();