Changeset 16311 for branches/2845_EnhancedProgress/HeuristicLab.Algorithms.DataAnalysis/3.4/NonlinearRegression/NonlinearRegression.cs

Timestamp:

11/20/18 15:26:57 (6 years ago)

Author:

pfleck

Message:

#2845 Merged trunk changes into branch (15406-15681, 15683-16308)

Location:

branches/2845_EnhancedProgress

Files:

• . (modified) (1 prop)
• HeuristicLab.Algorithms.DataAnalysis (modified) (1 prop)
• HeuristicLab.Algorithms.DataAnalysis/3.4 (modified) (1 prop)
• HeuristicLab.Algorithms.DataAnalysis/3.4/NonlinearRegression/NonlinearRegression.cs (modified) (10 diffs)

branches/2845_EnhancedProgress/HeuristicLab.Algorithms.DataAnalysis/3.4/NonlinearRegression/NonlinearRegression.cs

@@ -1 +1 @@
 #region License Information
 /* HeuristicLab
- * Copyright (C) 2002-2016 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ * Copyright (C) 2002-2018 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
  *
  * This file is part of HeuristicLab.
@@ -51 +51 @@
     private const string SeedParameterName = "Seed";
     private const string InitParamsRandomlyParameterName = "InitializeParametersRandomly";
+    private const string ApplyLinearScalingParameterName = "Apply linear scaling";
 
     public IFixedValueParameter<StringValue> ModelStructureParameter {
@@ -73 +74 @@
     public IFixedValueParameter<BoolValue> InitParametersRandomlyParameter {
       get { return (IFixedValueParameter<BoolValue>)Parameters[InitParamsRandomlyParameterName]; }
+    }
+
+    public IFixedValueParameter<BoolValue> ApplyLinearScalingParameter {
+      get { return (IFixedValueParameter<BoolValue>)Parameters[ApplyLinearScalingParameterName]; }
     }
 
@@ -103 +108 @@
       get { return InitParametersRandomlyParameter.Value.Value; }
       set { InitParametersRandomlyParameter.Value.Value = value; }
+    }
+
+    public bool ApplyLinearScaling {
+      get { return ApplyLinearScalingParameter.Value.Value; }
+      set { ApplyLinearScalingParameter.Value.Value = value; }
     }
 
@@ -119 +129 @@
       Parameters.Add(new FixedValueParameter<BoolValue>(SetSeedRandomlyParameterName, "Switch to determine if the random number seed should be initialized randomly.", new BoolValue(true)));
       Parameters.Add(new FixedValueParameter<BoolValue>(InitParamsRandomlyParameterName, "Switch to determine if the real-valued model parameters should be initialized randomly in each restart.", new BoolValue(false)));
+      Parameters.Add(new FixedValueParameter<BoolValue>(ApplyLinearScalingParameterName, "Switch to determine if linear scaling terms should be added to the model", new BoolValue(true)));
 
       SetParameterHiddenState();
@@ -146 +157 @@
       if (!Parameters.ContainsKey(InitParamsRandomlyParameterName))
         Parameters.Add(new FixedValueParameter<BoolValue>(InitParamsRandomlyParameterName, "Switch to determine if the numeric parameters of the model should be initialized randomly.", new BoolValue(false)));
+      if (!Parameters.ContainsKey(ApplyLinearScalingParameterName))
+        Parameters.Add(new FixedValueParameter<BoolValue>(ApplyLinearScalingParameterName, "Switch to determine if linear scaling terms should be added to the model", new BoolValue(true)));
+
 
       SetParameterHiddenState();
@@ -172 +186 @@
         qualityTable.Rows.Add(testRMSERow);
         Results.Add(new Result(qualityTable.Name, qualityTable.Name + " for all restarts", qualityTable));
-        if (SetSeedRandomly) Seed = (new System.Random()).Next();
+        if (SetSeedRandomly) Seed = RandomSeedGenerator.GetSeed();
         var rand = new MersenneTwister((uint)Seed);
-        bestSolution = CreateRegressionSolution(Problem.ProblemData, ModelStructure, Iterations, rand);
+        bestSolution = CreateRegressionSolution(Problem.ProblemData, ModelStructure, Iterations, ApplyLinearScaling, rand);
         trainRMSERow.Values.Add(bestSolution.TrainingRootMeanSquaredError);
         testRMSERow.Values.Add(bestSolution.TestRootMeanSquaredError);
         for (int r = 0; r < Restarts; r++) {
-          var solution = CreateRegressionSolution(Problem.ProblemData, ModelStructure, Iterations, rand);
+          var solution = CreateRegressionSolution(Problem.ProblemData, ModelStructure, Iterations, ApplyLinearScaling, rand);
           trainRMSERow.Values.Add(solution.TrainingRootMeanSquaredError);
           testRMSERow.Values.Add(solution.TestRootMeanSquaredError);
@@ -186 +200 @@
         }
       } else {
-        bestSolution = CreateRegressionSolution(Problem.ProblemData, ModelStructure, Iterations);
+        bestSolution = CreateRegressionSolution(Problem.ProblemData, ModelStructure, Iterations, ApplyLinearScaling);
       }
 
@@ -206 +220 @@
     /// <param name="random">Optional random number generator for random initialization of numeric constants.</param>
     /// <returns></returns>
-    public static ISymbolicRegressionSolution CreateRegressionSolution(IRegressionProblemData problemData, string modelStructure, int maxIterations, IRandom rand = null) {
+    public static ISymbolicRegressionSolution CreateRegressionSolution(IRegressionProblemData problemData, string modelStructure, int maxIterations, bool applyLinearScaling, IRandom rand = null) {
       var parser = new InfixExpressionParser();
       var tree = parser.Parse(modelStructure);
@@ -262 +276 @@
 
       SymbolicRegressionConstantOptimizationEvaluator.OptimizeConstants(interpreter, tree, problemData, problemData.TrainingIndices,
-        applyLinearScaling: false, maxIterations: maxIterations,
+        applyLinearScaling: applyLinearScaling, maxIterations: maxIterations,
         updateVariableWeights: false, updateConstantsInTree: true);
 
-      var scaledModel = new SymbolicRegressionModel(problemData.TargetVariable, tree, (ISymbolicDataAnalysisExpressionTreeInterpreter)interpreter.Clone());
-      scaledModel.Scale(problemData);
-      SymbolicRegressionSolution solution = new SymbolicRegressionSolution(scaledModel, (IRegressionProblemData)problemData.Clone());
+      var model = new SymbolicRegressionModel(problemData.TargetVariable, tree, (ISymbolicDataAnalysisExpressionTreeInterpreter)interpreter.Clone());
+      if (applyLinearScaling)
+        model.Scale(problemData);
+
+      SymbolicRegressionSolution solution = new SymbolicRegressionSolution(model, (IRegressionProblemData)problemData.Clone());
       solution.Model.Name = "Regression Model";
       solution.Name = "Regression Solution";