Changeset 12420 for branches/SymbolicExpressionTreeEncoding/HeuristicLab.Problems.DataAnalysis.Symbolic

Timestamp:

06/10/15 10:49:31 (9 years ago)

Author:

mkommend

Message:

#2320: Marked SymbolicExpressionTreeEncoding.EnumberableExtensions as obsolete and called the methods in Random.RandomEnumerable whereever possible.

Location:

branches/SymbolicExpressionTreeEncoding/HeuristicLab.Problems.DataAnalysis.Symbolic/3.4

Files:

• Crossovers/SymbolicDataAnalysisExpressionContextAwareCrossover.cs (modified) (2 diffs)
• Crossovers/SymbolicDataAnalysisExpressionDepthConstrainedCrossover.cs (modified) (3 diffs)
• Crossovers/SymbolicDataAnalysisExpressionDeterministicBestCrossover.cs (modified) (2 diffs)
• Crossovers/SymbolicDataAnalysisExpressionProbabilisticFunctionalCrossover.cs (modified) (3 diffs)
• Crossovers/SymbolicDataAnalysisExpressionSemanticSimilarityCrossover.cs (modified) (2 diffs)
• Symbols/VariableConditionTreeNode.cs (modified) (2 diffs)
• Symbols/VariableTreeNode.cs (modified) (2 diffs)

branches/SymbolicExpressionTreeEncoding/HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/Crossovers/SymbolicDataAnalysisExpressionContextAwareCrossover.cs

@@ -27 +27 @@
 using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
 using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
+using HeuristicLab.Random;
 
 namespace HeuristicLab.Problems.DataAnalysis.Symbolic {
@@ -68 +69 @@
           possibleChildren.Add(n);
       });
-      var selectedChild = possibleChildren.SelectRandom(random);
+
+      var selectedChild = possibleChildren.SampleRandom(random);
       var crossoverPoints = new List<CutPoint>();
       var qualities = new List<Tuple<CutPoint, double>>();

branches/SymbolicExpressionTreeEncoding/HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/Crossovers/SymbolicDataAnalysisExpressionDepthConstrainedCrossover.cs

@@ -29 +29 @@
 using HeuristicLab.Parameters;
 using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
+using HeuristicLab.Random;
 
 namespace HeuristicLab.Problems.DataAnalysis.Symbolic {
@@ -115 +116 @@
         throw new Exception("No crossover points available in the first parent");
 
-      var crossoverPoint0 = crossoverPoints0.SelectRandom(random);
-
+      var crossoverPoint0 = crossoverPoints0.SampleRandom(random);
       int level = parent0.Root.GetBranchLevel(crossoverPoint0.Child);
       int length = parent0.Root.GetLength() - crossoverPoint0.Child.GetLength();
@@ -126 +126 @@
                              select s).ToList();
       if (allowedBranches.Count == 0) return parent0;
-      var selectedBranch = allowedBranches.SelectRandom(random);
+
+      var selectedBranch = allowedBranches.SampleRandom(random);
       Swap(crossoverPoint0, selectedBranch);
       return parent0;

branches/SymbolicExpressionTreeEncoding/HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/Crossovers/SymbolicDataAnalysisExpressionDeterministicBestCrossover.cs

@@ -27 +27 @@
 using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
 using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
+using HeuristicLab.Random;
 
 namespace HeuristicLab.Problems.DataAnalysis.Symbolic {
@@ -67 +68 @@
           crossoverPoints0.Add(new CutPoint(n.Parent, n));
       });
-      CutPoint crossoverPoint0 = crossoverPoints0.SelectRandom(random);
+
+      CutPoint crossoverPoint0 = crossoverPoints0.SampleRandom(random);
       int level = parent0.Root.GetBranchLevel(crossoverPoint0.Child);
       int length = parent0.Root.GetLength() - crossoverPoint0.Child.GetLength();

branches/SymbolicExpressionTreeEncoding/HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/Crossovers/SymbolicDataAnalysisExpressionProbabilisticFunctionalCrossover.cs

@@ -27 +27 @@
 using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
 using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
+using HeuristicLab.Random;
 
 namespace HeuristicLab.Problems.DataAnalysis.Symbolic {
@@ -69 +70 @@
         }
       });
-      var crossoverPoint0 = crossoverPoints0.SelectRandom(random);
+
+      var crossoverPoint0 = crossoverPoints0.SampleRandom(random);
       int level = parent0.Root.GetBranchLevel(crossoverPoint0.Child);
       int length = parent0.Root.GetLength() - crossoverPoint0.Child.GetLength();
@@ -137 +139 @@
           weights[i] /= sum;
 
+#pragma warning disable 612, 618
         selectedBranch = allowedBranches.SelectRandom(weights, random);
+#pragma warning restore 612, 618
       }
       Swap(crossoverPoint0, selectedBranch);

branches/SymbolicExpressionTreeEncoding/HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/Crossovers/SymbolicDataAnalysisExpressionSemanticSimilarityCrossover.cs

@@ -28 +28 @@
 using HeuristicLab.Parameters;
 using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
+using HeuristicLab.Random;
 
 namespace HeuristicLab.Problems.DataAnalysis.Symbolic {
@@ -81 +82 @@
           crossoverPoints0.Add(new CutPoint(n.Parent, n));
       });
-      var crossoverPoint0 = crossoverPoints0.SelectRandom(random);
+
+      var crossoverPoint0 = crossoverPoints0.SampleRandom(random);
       int level = parent0.Root.GetBranchLevel(crossoverPoint0.Child);
       int length = parent0.Root.GetLength() - crossoverPoint0.Child.GetLength();

branches/SymbolicExpressionTreeEncoding/HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/Symbols/VariableConditionTreeNode.cs

@@ -74 +74 @@
       base.ResetLocalParameters(random);
       threshold = NormalDistributedRandom.NextDouble(random, Symbol.ThresholdInitializerMu, Symbol.ThresholdInitializerSigma);
+
+#pragma warning disable 612, 618
       variableName = Symbol.VariableNames.SelectRandom(random);
+#pragma warning restore 612, 618
+
       slope = NormalDistributedRandom.NextDouble(random, Symbol.SlopeInitializerMu, Symbol.SlopeInitializerSigma);
     }
@@ -82 +86 @@
       double x = NormalDistributedRandom.NextDouble(random, Symbol.ThresholdManipulatorMu, Symbol.ThresholdManipulatorSigma);
       threshold = threshold + x * shakingFactor;
+
+#pragma warning disable 612, 618
       variableName = Symbol.VariableNames.SelectRandom(random);
+#pragma warning restore 612, 618
+
       x = NormalDistributedRandom.NextDouble(random, Symbol.SlopeManipulatorMu, Symbol.SlopeManipulatorSigma);
       slope = slope + x * shakingFactor;

branches/SymbolicExpressionTreeEncoding/HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/Symbols/VariableTreeNode.cs

@@ -61 +61 @@
       base.ResetLocalParameters(random);
       weight = NormalDistributedRandom.NextDouble(random, Symbol.WeightMu, Symbol.WeightSigma);
+
+#pragma warning disable 612, 618
       variableName = Symbol.VariableNames.SelectRandom(random);
+#pragma warning restore 612, 618
     }
 
@@ -70 +73 @@
         double x = NormalDistributedRandom.NextDouble(random, Symbol.WeightManipulatorMu, Symbol.WeightManipulatorSigma);
         weight = weight + x * shakingFactor;
-      } else {        
+      } else {
         double x = NormalDistributedRandom.NextDouble(random, 1.0, Symbol.MultiplicativeWeightManipulatorSigma);
         weight = weight * x;
       }
+#pragma warning disable 612, 618
       variableName = Symbol.VariableNames.SelectRandom(random);
+#pragma warning restore 612, 618
     }