Changeset 8240

Timestamp:

07/05/12 17:25:44 (12 years ago)

Author:

gkronber

Message:

#1784 adapted Vladislavleva instances

Location:

trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Vladislavleva

Files:

• KotanchekFunction.cs (modified) (3 diffs)
• RationalPolynomialThreeDimensional.cs (modified) (2 diffs)
• RationalPolynomialTwoDimensional.cs (modified) (1 diff)
• RippleFunction.cs (modified) (1 diff)
• SalutowiczFunctionOneDimensional.cs (modified) (1 diff)
• SalutowiczFunctionTwoDimensional.cs (modified) (3 diffs)
• SineCosineFunction.cs (modified) (1 diff)
• UnwrappedBallFunctionFiveDimensional.cs (modified) (1 diff)
• VladislavlevaInstanceProvider.cs (modified) (1 diff)

trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Vladislavleva/KotanchekFunction.cs

@@ -27 +27 @@
   public class KotanchekFunction : ArtificialRegressionDataDescriptor {
 
-    public override string Name { get { return "Vladislavleva Kotanchek"; } }
+    public override string Name { get { return "Vladislavleva-1 F1(X1,X2) = exp(-(X1 - 1))² / (1.2 + (X2 -2.5)²"; } }
     public override string Description {
       get {
         return "Paper: Order of Nonlinearity as a Complexity Measure for Models Generated by Symbolic Regression via Pareto Genetic Programming " + Environment.NewLine
         + "Authors: Ekaterina J. Vladislavleva, Member, IEEE, Guido F. Smits, Member, IEEE, and Dick den Hertog" + Environment.NewLine
-        + "Function: F1(X1, X2) = e^-(X1 - 1)^2 / (1.2 + (X2 -2.5)^2" + Environment.NewLine
+        + "Function: F1(X1, X2) = exp(-(X1 - 1))² / (1.2 + (X2 -2.5)²" + Environment.NewLine
         + "Training Data: 100 points X1, X2 = Rand(0.3, 4)" + Environment.NewLine
         + "Test Data: 2026 points (X1, X2) = (-0.2:0.1:4.2)" + Environment.NewLine
@@ -43 +43 @@
     protected override int TrainingPartitionStart { get { return 0; } }
     protected override int TrainingPartitionEnd { get { return 100; } }
-    protected override int TestPartitionStart { get { return 1000; } }
-    protected override int TestPartitionEnd { get { return 3025; } }
+    protected override int TestPartitionStart { get { return 100; } }
+    protected override int TestPartitionEnd { get { return 2126; } }
 
     protected override List<List<double>> GenerateValues() {
@@ -53 +53 @@
       var combinations = ValueGenerator.GenerateAllCombinationsOfValuesInLists(testData).ToList<IEnumerable<double>>();
       for (int i = 0; i < AllowedInputVariables.Count(); i++) {
-        data.Add(ValueGenerator.GenerateUniformDistributedValues(1000, 0.3, 4).ToList());
+        data.Add(ValueGenerator.GenerateUniformDistributedValues(100, 0.3, 4).ToList());
         data[i].AddRange(combinations[i]);
       }

trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Vladislavleva/RationalPolynomialThreeDimensional.cs

@@ -27 +27 @@
   public class RationalPolynomialThreeDimensional : ArtificialRegressionDataDescriptor {
 
-    public override string Name { get { return "Vladislavleva RatPol3D"; } }
+    public override string Name { get { return "Vladislavleva-5 F5(X1, X2, X3) = 30 * ((X1 - 1) * (X3 -1)) / (X2² * (X1 - 10))"; } }
     public override string Description {
       get {
         return "Paper: Order of Nonlinearity as a Complexity Measure for Models Generated by Symbolic Regression via Pareto Genetic Programming " + Environment.NewLine
         + "Authors: Ekaterina J. Vladislavleva, Member, IEEE, Guido F. Smits, Member, IEEE, and Dick den Hertog" + Environment.NewLine
-        + "Function: F5(X1, X2, X3) = 30 * ((X1 - 1) * (X3 -1)) / (X2^2 * (X1 - 10))" + Environment.NewLine
+        + "Function: F5(X1, X2, X3) = 30 * ((X1 - 1) * (X3 -1)) / (X2² * (X1 - 10))" + Environment.NewLine
         + "Training Data: 300 points X1, X3 = Rand(0.05, 2), X2 = Rand(1, 2)" + Environment.NewLine
         + "Test Data: 2701 points X1, X3 = (-0.05:0.15:2.1), X2 = (0.95:0.1:2.05)" + Environment.NewLine
@@ -49 +49 @@
       List<List<double>> data = new List<List<double>>();
 
-      int amountOfPoints = 1000;
-      data.Add(ValueGenerator.GenerateUniformDistributedValues(amountOfPoints, 0.05, 2).ToList());
-      data.Add(ValueGenerator.GenerateUniformDistributedValues(amountOfPoints, 1, 2).ToList());
-      data.Add(ValueGenerator.GenerateUniformDistributedValues(amountOfPoints, 0.05, 2).ToList());
+      int n = 1000;
+      data.Add(ValueGenerator.GenerateUniformDistributedValues(n, 0.05, 2).ToList());
+      data.Add(ValueGenerator.GenerateUniformDistributedValues(n, 1, 2).ToList());
+      data.Add(ValueGenerator.GenerateUniformDistributedValues(n, 0.05, 2).ToList());
 
       List<List<double>> testData = new List<List<double>>() { 

trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Vladislavleva/RationalPolynomialTwoDimensional.cs

@@ -27 +27 @@
   public class RationalPolynomialTwoDimensional : ArtificialRegressionDataDescriptor {
 
-    public override string Name { get { return "Vladislavleva RatPol2D"; } }
+    public override string Name { get { return "Vladislavleva-8 F8(X1, X2) = ((X1 - 3)^4 + (X2 - 3)³ - (X2 -3)) / ((X2 - 2)^4 + 10)"; } }
     public override string Description {
       get {
         return "Paper: Order of Nonlinearity as a Complexity Measure for Models Generated by Symbolic Regression via Pareto Genetic Programming " + Environment.NewLine
         + "Authors: Ekaterina J. Vladislavleva, Member, IEEE, Guido F. Smits, Member, IEEE, and Dick den Hertog" + Environment.NewLine
-        + "Function: F8(X1, X2) = ((X1 - 3)^4 + (X2 - 3)^3 - (X2 -3)) / ((X2 - 2)^4 + 10)" + Environment.NewLine
+        + "Function: F8(X1, X2) = ((X1 - 3)^4 + (X2 - 3)³ - (X2 -3)) / ((X2 - 2)^4 + 10)" + Environment.NewLine
         + "Training Data: 50 points X1, X2 = Rand(0.05, 6.05)" + Environment.NewLine
         + "Test Data: 1157 points X1, X2 = (-0.25:0.2:6.35)" + Environment.NewLine

trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Vladislavleva/RippleFunction.cs

@@ -27 +27 @@
   public class RippleFunction : ArtificialRegressionDataDescriptor {
 
-    public override string Name { get { return "Vladislavleva Ripple"; } }
+    public override string Name { get { return "Vladislavleva-7  F7(X1, X2) = (X1 - 3)(X2 - 3) + 2 * sin((X1 - 4)(X2 - 4))"; } }
     public override string Description {
       get {

trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Vladislavleva/SalutowiczFunctionOneDimensional.cs

@@ -27 +27 @@
   public class SalutowiczFunctionOneDimensional : ArtificialRegressionDataDescriptor {
 
-    public override string Name { get { return "Vladislavleva Salutowicz"; } }
+    public override string Name { get { return "Vladislavleva-2 F2(X) = exp(-X) * X³ * cos(X) * sin(X) * (cos(X)sin(X)² - 1)"; } }
     public override string Description {
       get {
         return "Paper: Order of Nonlinearity as a Complexity Measure for Models Generated by Symbolic Regression via Pareto Genetic Programming " + Environment.NewLine
         + "Authors: Ekaterina J. Vladislavleva, Member, IEEE, Guido F. Smits, Member, IEEE, and Dick den Hertog" + Environment.NewLine
-        + "Function: F2(X) = e^-X * X^3 * cos(X) * sin(X) * (cos(X)sin(X)^2 - 1)" + Environment.NewLine
+        + "Function: F2(X) = exp(-X) * X³ * cos(X) * sin(X) * (cos(X)sin(X)² - 1)" + Environment.NewLine
         + "Training Data: 100 points X = (0.05:0.1:10)" + Environment.NewLine
         + "Test Data: 221 points X = (-0.5:0.05:10.5)" + Environment.NewLine
-        + "Function Set: +, -, *, /, sqaure, x^real, x + real, x + real, e^x, e^-x, sin(x), cos(x)";
+        + "Function Set: +, -, *, /, square, x^real, x + real, x + real, e^x, e^-x, sin(x), cos(x)";
       }
     }

trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Vladislavleva/SalutowiczFunctionTwoDimensional.cs

@@ -27 +27 @@
   public class SalutowiczFunctionTwoDimensional : ArtificialRegressionDataDescriptor {
 
-    public override string Name { get { return "Vladislavleva Salutowicz2D"; } }
+    public override string Name { get { return "Vladislavleva-3 F3(X1, X2) = exp(-X1) * X1³ * cos(X1) * sin(X1) * (cos(X1)sin(X1)² - 1)(X2 - 5)"; } }
     public override string Description {
       get {
         return "Paper: Order of Nonlinearity as a Complexity Measure for Models Generated by Symbolic Regression via Pareto Genetic Programming " + Environment.NewLine
         + "Authors: Ekaterina J. Vladislavleva, Member, IEEE, Guido F. Smits, Member, IEEE, and Dick den Hertog" + Environment.NewLine
-        + "Function: F3(X1, X2) = e^-X1 * X1^3 * cos(X1) * sin(X1) * (cos(X1)sin(X1)^2 - 1)(X2 - 5)" + Environment.NewLine
+        + "Function: F3(X1, X2) = exp(-X1) * X1³ * cos(X1) * sin(X1) * (cos(X1)sin(X1)² - 1)(X2 - 5)" + Environment.NewLine
         + "Training Data: 601 points X1 = (0.05:0.1:10), X2 = (0.05:2:10.05)" + Environment.NewLine
-        + "Test Data: 2554 points X1 = (-0.5:0.05:10.5), X2 = (-0.5:0.5:10.5)" + Environment.NewLine
-        + "Function Set: +, -, *, /, sqaure, x^real, x + real, x + real, e^x, e^-x, sin(x), cos(x)" + Environment.NewLine + Environment.NewLine
-        + "Important: The stepwidth of the variable X1 in the test partition has been set to 0.1, to fit the amount of data points.";
+        + "Test Data: 4840 points X1 = (-0.5:0.05:10.5), X2 = (-0.5:0.5:10.5)" + Environment.NewLine
+        + "Function Set: +, -, *, /, square, x^real, x + real, x + real, e^x, e^-x, sin(x), cos(x)";
       }
     }
@@ -45 +44 @@
     protected override int TrainingPartitionEnd { get { return 601; } }
     protected override int TestPartitionStart { get { return 601; } }
-    protected override int TestPartitionEnd { get { return 3155; } }
+    protected override int TestPartitionEnd { get { return 5441; } }
 
     protected override List<List<double>> GenerateValues() {
@@ -55 +54 @@
 
       List<List<double>> testData = new List<List<double>>() {
-        ValueGenerator.GenerateSteps(-0.5, 10.5, 0.1).ToList(),
+        ValueGenerator.GenerateSteps(-0.5, 10.5, 0.05).ToList(),
         ValueGenerator.GenerateSteps(-0.5, 10.5, 0.5).ToList()
       };

trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Vladislavleva/SineCosineFunction.cs

@@ -27 +27 @@
   public class SineCosineFunction : ArtificialRegressionDataDescriptor {
 
-    public override string Name { get { return "Vladislavleva SineCosine"; } }
+    public override string Name { get { return "Vladislavleva-6 F6(X1, X2) = 6 * sin(X1) * cos(X2)"; } }
     public override string Description {
       get {

trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Vladislavleva/UnwrappedBallFunctionFiveDimensional.cs

@@ -27 +27 @@
   public class UnwrappedBallFunctionFiveDimensional : ArtificialRegressionDataDescriptor {
 
-    public override string Name { get { return "Vladislavleva UBall5D"; } }
+    public override string Name { get { return "Vladislavleva-4 F4(X1, X2, X3, X4, X5) = 10 / (5 + Sum(Xi - 3)^2)"; } }
     public override string Description {
       get {

trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Vladislavleva/VladislavlevaInstanceProvider.cs

@@ -41 +41 @@
       List<IDataDescriptor> descriptorList = new List<IDataDescriptor>();
       descriptorList.Add(new KotanchekFunction());
-      descriptorList.Add(new RationalPolynomialTwoDimensional());
-      descriptorList.Add(new RationalPolynomialThreeDimensional());
-      descriptorList.Add(new RippleFunction());
       descriptorList.Add(new SalutowiczFunctionOneDimensional());
       descriptorList.Add(new SalutowiczFunctionTwoDimensional());
       descriptorList.Add(new UnwrappedBallFunctionFiveDimensional());
+      descriptorList.Add(new RationalPolynomialThreeDimensional());
+      descriptorList.Add(new SineCosineFunction());
+      descriptorList.Add(new RippleFunction());
+      descriptorList.Add(new RationalPolynomialTwoDimensional());
       return descriptorList;
     }