Changeset 8900

Timestamp:

11/12/12 13:51:49 (11 years ago)

Author:

sforsten

Message:

#1979:

• renamed Keijzer 1-3
• corrected training and test partition of Keijzer 9 and 10
• changed interval for some variables in Korn 7, 9, 15 to avoid infinity and NaN values

Location:

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

Files:

• Keijzer/KeijzerFunctionNine.cs (modified) (2 diffs)
• Keijzer/KeijzerFunctionOne.cs (modified) (1 diff)
• Keijzer/KeijzerFunctionTen.cs (modified) (1 diff)
• Keijzer/KeijzerFunctionThree.cs (modified) (1 diff)
• Keijzer/KeijzerFunctionTwo.cs (modified) (1 diff)
• Korns/KornsFunctionFiveteen.cs (modified) (2 diffs)
• Korns/KornsFunctionNine.cs (modified) (2 diffs)
• Korns/KornsFunctionSeven.cs (modified) (2 diffs)

trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Keijzer/KeijzerFunctionNine.cs

@@ -42 +42 @@
     protected override string[] AllowedInputVariables { get { return new string[] { "X" }; } }
     protected override int TrainingPartitionStart { get { return 0; } }
-    protected override int TrainingPartitionEnd { get { return 100; } }
-    protected override int TestPartitionStart { get { return 100; } }
-    protected override int TestPartitionEnd { get { return 1100; } }
+    protected override int TrainingPartitionEnd { get { return 101; } }
+    protected override int TestPartitionStart { get { return 101; } }
+    protected override int TestPartitionEnd { get { return 1102; } }
 
     protected override List<List<double>> GenerateValues() {
@@ -55 +55 @@
       for (int i = 0; i < data[0].Count; i++) {
         x = data[0][i];
-        results.Add(Math.Log(x + Math.Sqrt(x*x + 1)));
+        results.Add(Math.Log(x + Math.Sqrt(x * x + 1)));
       }
       data.Add(results);

trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Keijzer/KeijzerFunctionOne.cs

@@ -27 +27 @@
   public class KeijzerFunctionOne : ArtificialRegressionDataDescriptor {
 
-    public override string Name { get { return "Keijzer 1 f(x) = 0.3 * x *sin(2 * PI * x)"; } }
+    public override string Name { get { return "Keijzer 1 f(x) = 0.3 * x *sin(2 * PI * x); Interval [-1, 1]"; } }
     public override string Description {
       get {

trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Keijzer/KeijzerFunctionTen.cs

@@ -44 +44 @@
     protected override int TrainingPartitionEnd { get { return 100; } }
     protected override int TestPartitionStart { get { return 100; } }
-    protected override int TestPartitionEnd { get { return 10100; } }
+    protected override int TestPartitionEnd { get { return 10301; } }
 
     protected override List<List<double>> GenerateValues() {

trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Keijzer/KeijzerFunctionThree.cs

@@ -27 +27 @@
   public class KeijzerFunctionThree : ArtificialRegressionDataDescriptor {
 
-    public override string Name { get { return "Keijzer 3 f(x) = 0.3 * x *sin(2 * PI * x)"; } }
+    public override string Name { get { return "Keijzer 3 f(x) = 0.3 * x *sin(2 * PI * x); Interval [-3, 3]"; } }
     public override string Description {
       get {

trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Keijzer/KeijzerFunctionTwo.cs

@@ -27 +27 @@
   public class KeijzerFunctionTwo : ArtificialRegressionDataDescriptor {
 
-    public override string Name { get { return "Keijzer 2 f(x) = 0.3 * x *sin(2 * PI * x)"; } }
+    public override string Name { get { return "Keijzer 2 f(x) = 0.3 * x *sin(2 * PI * x); Interval [-2, 2]"; } }
     public override string Description {
       get {

trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Korns/KornsFunctionFiveteen.cs

@@ -40 +40 @@
         + "For each test a training matrix is filled with random numbers between -50 and +50. The test case "
         + "target expressions are limited to one basis function whose maximum depth is three grammar nodes.\"" + Environment.NewLine + Environment.NewLine
-        + "Note: Because of the logarithm only non-negatic values are created for the input variables!";
+        + "Note: Because of the logarithm and the exponential function as denominator only non-negatic values are created for their input variables!";
       }
     }
@@ -54 +54 @@
       List<List<double>> data = new List<List<double>>();
       data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, -50, 50).ToList());
-      data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, -50, 50).ToList());
+      data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, 0, 50).ToList()); // note: range is only [0,50] to prevent NaN values (deviates from gp benchmark paper)
       data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, 0, 50).ToList()); // note: range is only [0,50] to prevent NaN values (deviates from gp benchmark paper)
       data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, -50, 50).ToList());

trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Korns/KornsFunctionNine.cs

@@ -40 +40 @@
         + "For each test a training matrix is filled with random numbers between -50 and +50. The test case "
         + "target expressions are limited to one basis function whose maximum depth is three grammar nodes.\"" + Environment.NewLine + Environment.NewLine
-        + "Note: Because of the square root and the logarithm only non-negatic values are created for the input variables!";
+        + "Note: Because of the square root and the logarithm only non-negatic values are created for their input variables!" + Environment.NewLine
+        + "Because of the exponential function only only non-positive values are created for its input variable!";
       }
     }
@@ -55 +56 @@
       data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, 0, 50).ToList()); // note: range is only [0,50] to prevent NaN values (deviates from gp benchmark paper)
       data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, 0, 50).ToList()); // note: range is only [0,50] to prevent NaN values (deviates from gp benchmark paper)
-      data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, -50, 50).ToList()); 
-      data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, -50, 50).ToList()); 
-      data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, -50, 50).ToList()); 
+      data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, -50, 0).ToList()); // note: range is only [-50,0] to prevent NaN values (deviates from gp benchmark paper)
+      data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, -50, 50).ToList());
+      data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, -50, 50).ToList());
 
       double x0, x1, x2, x3;

trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Korns/KornsFunctionSeven.cs

@@ -39 +39 @@
         + "We test each of the test cases on matrices of 10000 rows by 1 to 5 columns with no noise. "
         + "For each test a training matrix is filled with random numbers between -50 and +50. The test case "
-        + "target expressions are limited to one basis function whose maximum depth is three grammar nodes.\"";
+        + "target expressions are limited to one basis function whose maximum depth is three grammar nodes.\"" + Environment.NewLine
+        + "Note: Because of the exponential function only non-negatic values are created for its input variable!";
       }
     }
@@ -52 +53 @@
     protected override List<List<double>> GenerateValues() {
       List<List<double>> data = new List<List<double>>();
-      for (int i = 0; i < AllowedInputVariables.Count(); i++) {
-        data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, -50, 50).ToList());
-      }
+      data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, 0, 50).ToList()); // note: range is only [0,50] to prevent NaN values (deviates from gp benchmark paper)
+      data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, -50, 50).ToList());
+      data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, -50, 50).ToList());
+      data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, -50, 50).ToList());
+      data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, -50, 50).ToList());
 
       double x0;