- Timestamp:
- 12/06/12 14:13:39 (12 years ago)
- Location:
- trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression
- Files:
-
- 23 edited
Legend:
- Unmodified
- Added
- Removed
-
trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Keijzer/KeijzerFunctionEleven.cs
r8825 r9007 36 36 + "range(train): 20 Training cases x,y = rnd(-3, 3)" + Environment.NewLine 37 37 + "range(test): x,y = [-3:0.01:3]" + Environment.NewLine 38 + "Function Set: x + y, x * y, 1/x, -x, sqrt(x)" + Environment.NewLine + Environment.NewLine 39 + "Note: Test partition has been adjusted to only 100 random uniformly distributed test cases in the interval [-3, 3] (not ca. 360000 as described) " 40 + ", but 5000 cases are created"; 38 + "Function Set: x + y, x * y, 1/x, -x, sqrt(x)"; 41 39 } 42 40 } … … 46 44 protected override int TrainingPartitionStart { get { return 0; } } 47 45 protected override int TrainingPartitionEnd { get { return 20; } } 48 protected override int TestPartitionStart { get { return 2 500; } }49 protected override int TestPartitionEnd { get { return 2 600; } }46 protected override int TestPartitionStart { get { return 20; } } 47 protected override int TestPartitionEnd { get { return 20 + (601 * 601); } } 50 48 51 49 protected override List<List<double>> GenerateValues() { 52 50 List<List<double>> data = new List<List<double>>(); 51 List<double> oneVariableTestData = ValueGenerator.GenerateSteps(-3, 3, 0.01).ToList(); 52 List<List<double>> testData = new List<List<double>>() { oneVariableTestData, oneVariableTestData }; 53 54 var combinations = ValueGenerator.GenerateAllCombinationsOfValuesInLists(testData).ToList(); 55 53 56 for (int i = 0; i < AllowedInputVariables.Count(); i++) { 54 data.Add(ValueGenerator.GenerateUniformDistributedValues(5020, -3, 3).ToList()); 57 data.Add(ValueGenerator.GenerateUniformDistributedValues(20, -3, 3).ToList()); 58 data[i].AddRange(combinations[i]); 55 59 } 56 60 -
trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Keijzer/KeijzerFunctionFifteen.cs
r8825 r9007 35 35 + "range(train): 20 Training cases x,y = rnd(-3, 3)" + Environment.NewLine 36 36 + "range(test): x,y = [-3:0.01:3]" + Environment.NewLine 37 + "Function Set: x + y, x * y, 1/x, -x, sqrt(x)" + Environment.NewLine + Environment.NewLine 38 + "Note: Test partition has been adjusted to only 100 random uniformly distributed test cases in the interval [-3, 3] (not ca. 360000 as described) " 39 + ", but 5000 cases are created"; 37 + "Function Set: x + y, x * y, 1/x, -x, sqrt(x)"; 40 38 } 41 39 } … … 45 43 protected override int TrainingPartitionStart { get { return 0; } } 46 44 protected override int TrainingPartitionEnd { get { return 20; } } 47 protected override int TestPartitionStart { get { return 2 500; } }48 protected override int TestPartitionEnd { get { return 2 600; } }45 protected override int TestPartitionStart { get { return 20; } } 46 protected override int TestPartitionEnd { get { return 20 + (601 * 601); } } 49 47 50 48 protected override List<List<double>> GenerateValues() { 51 49 List<List<double>> data = new List<List<double>>(); 50 List<double> oneVariableTestData = ValueGenerator.GenerateSteps(-3, 3, 0.01).ToList(); 51 List<List<double>> testData = new List<List<double>>() { oneVariableTestData, oneVariableTestData }; 52 53 var combinations = ValueGenerator.GenerateAllCombinationsOfValuesInLists(testData).ToList(); 54 52 55 for (int i = 0; i < AllowedInputVariables.Count(); i++) { 53 data.Add(ValueGenerator.GenerateUniformDistributedValues(5000, -3, 3).ToList()); 56 data.Add(ValueGenerator.GenerateUniformDistributedValues(20, -3, 3).ToList()); 57 data[i].AddRange(combinations[i]); 54 58 } 55 59 … … 59 63 x = data[0][i]; 60 64 y = data[1][i]; 61 results.Add( Math.Pow(x, 3) / 5 + Math.Pow(y, 3) / 2- y - x);65 results.Add(x * x * x / 5.0 + y * y * y / 2.0 - y - x); 62 66 } 63 67 data.Add(results); -
trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Keijzer/KeijzerFunctionFourteen.cs
r8825 r9007 35 35 + "range(train): 20 Train cases x,y = rnd(-3, 3)" + Environment.NewLine 36 36 + "range(test): x,y = [-3:0.01:3]" + Environment.NewLine 37 + "Function Set: x + y, x * y, 1/x, -x, sqrt(x)" + Environment.NewLine + Environment.NewLine 38 + "Note: Test partition has been adjusted to only 100 random uniformly distributed test cases in the interval [-3, 3] (not ca. 360000 as described) " 39 + ", but 5000 cases are created"; 37 + "Function Set: x + y, x * y, 1/x, -x, sqrt(x)"; 40 38 } 41 39 } … … 45 43 protected override int TrainingPartitionStart { get { return 0; } } 46 44 protected override int TrainingPartitionEnd { get { return 20; } } 47 protected override int TestPartitionStart { get { return 2 500; } }48 protected override int TestPartitionEnd { get { return 2 600; } }45 protected override int TestPartitionStart { get { return 20; } } 46 protected override int TestPartitionEnd { get { return 20 + (601 * 601); } } 49 47 50 48 protected override List<List<double>> GenerateValues() { 51 49 List<List<double>> data = new List<List<double>>(); 50 List<double> oneVariableTestData = ValueGenerator.GenerateSteps(-3, 3, 0.01).ToList(); 51 List<List<double>> testData = new List<List<double>>() { oneVariableTestData, oneVariableTestData }; 52 53 var combinations = ValueGenerator.GenerateAllCombinationsOfValuesInLists(testData).ToList(); 54 52 55 for (int i = 0; i < AllowedInputVariables.Count(); i++) { 53 data.Add(ValueGenerator.GenerateUniformDistributedValues(5000, -3, 3).ToList()); 56 data.Add(ValueGenerator.GenerateUniformDistributedValues(20, -3, 3).ToList()); 57 data[i].AddRange(combinations[i]); 54 58 } 55 59 … … 59 63 x = data[0][i]; 60 64 y = data[1][i]; 61 results.Add(8 / (2 + Math.Pow(x, 2) + Math.Pow(y, 2)));65 results.Add(8.0 / (2.0 + x * x + y * y)); 62 66 } 63 67 data.Add(results); -
trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Keijzer/KeijzerFunctionSeven.cs
r8825 r9007 36 36 + "range(test): x = [1:0.1:100]" + Environment.NewLine 37 37 + "Function Set: x + y, x * y, 1/x, -x, sqrt(x)" + Environment.NewLine + Environment.NewLine 38 + "Note: The problem starts with 1 to avoid log(0) , which is minus infinity!";38 + "Note: The problem starts with 1 to avoid log(0)!"; 39 39 } 40 40 } -
trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Keijzer/KeijzerFunctionTen.cs
r8900 r9007 44 44 protected override int TrainingPartitionEnd { get { return 100; } } 45 45 protected override int TestPartitionStart { get { return 100; } } 46 protected override int TestPartitionEnd { get { return 10 301; } }46 protected override int TestPartitionEnd { get { return 100 + (101 * 101); } } 47 47 48 48 protected override List<List<double>> GenerateValues() { -
trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Keijzer/KeijzerFunctionThirteen.cs
r8825 r9007 35 35 + "range(train): 20 Train cases x,y = rnd(-3, 3)" + Environment.NewLine 36 36 + "range(test): x,y = [-3:0.01:3]" + Environment.NewLine 37 + "Function Set: x + y, x * y, 1/x, -x, sqrt(x)" + Environment.NewLine + Environment.NewLine 38 + "Note: Test partition has been adjusted to only 100 random uniformly distributed test cases in the interval [-3, 3] (not ca. 360000 as described) " 39 + ", but 5000 cases are created"; 37 + "Function Set: x + y, x * y, 1/x, -x, sqrt(x)"; 40 38 } 41 39 } … … 45 43 protected override int TrainingPartitionStart { get { return 0; } } 46 44 protected override int TrainingPartitionEnd { get { return 20; } } 47 protected override int TestPartitionStart { get { return 2 500; } }48 protected override int TestPartitionEnd { get { return 2 600; } }45 protected override int TestPartitionStart { get { return 20; } } 46 protected override int TestPartitionEnd { get { return 20 + (601 * 601); } } 49 47 50 48 protected override List<List<double>> GenerateValues() { 51 49 List<List<double>> data = new List<List<double>>(); 50 List<double> oneVariableTestData = ValueGenerator.GenerateSteps(-3, 3, 0.01).ToList(); 51 List<List<double>> testData = new List<List<double>>() { oneVariableTestData, oneVariableTestData }; 52 53 var combinations = ValueGenerator.GenerateAllCombinationsOfValuesInLists(testData).ToList(); 54 52 55 for (int i = 0; i < AllowedInputVariables.Count(); i++) { 53 data.Add(ValueGenerator.GenerateUniformDistributedValues(5000, -3, 3).ToList()); 56 data.Add(ValueGenerator.GenerateUniformDistributedValues(20, -3, 3).ToList()); 57 data[i].AddRange(combinations[i]); 54 58 } 55 59 -
trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Keijzer/KeijzerFunctionTwelve.cs
r8825 r9007 31 31 get { 32 32 return "Paper: Improving Symbolic Regression with Interval Arithmetic and Linear Scaling" + Environment.NewLine 33 + "Authors: Maarten Keijzer" + Environment.NewLine 34 + "Function: f(x, y) = x^4 - x³ + y² / 2 - y" + Environment.NewLine 35 + "range(train): 20 Training cases x,y = rnd(-3, 3)" + Environment.NewLine 36 + "range(test): x,y = [-3:0.01:3]" + Environment.NewLine 37 + "Function Set: x + y, x * y, 1/x, -x, sqrt(x)" + Environment.NewLine + Environment.NewLine 38 + "Note: Test partition has been adjusted to only 100 random uniformly distributed test cases in the interval [-3, 3] (not ca. 360000 as described) " 39 + ", but 5000 cases are created"; 33 + "Authors: Maarten Keijzer" + Environment.NewLine 34 + "Function: f(x, y) = x^4 - x³ + y² / 2 - y" + Environment.NewLine 35 + "range(train): 20 Training cases x,y = rnd(-3, 3)" + Environment.NewLine 36 + "range(test): x,y = [-3:0.01:3]" + Environment.NewLine 37 + "Function Set: x + y, x * y, 1/x, -x, sqrt(x)"; 40 38 } 41 39 } … … 45 43 protected override int TrainingPartitionStart { get { return 0; } } 46 44 protected override int TrainingPartitionEnd { get { return 20; } } 47 protected override int TestPartitionStart { get { return 2 500; } }48 protected override int TestPartitionEnd { get { return 2 600; } }45 protected override int TestPartitionStart { get { return 20; } } 46 protected override int TestPartitionEnd { get { return 20 + (601 * 601); } } 49 47 50 48 protected override List<List<double>> GenerateValues() { 51 49 List<List<double>> data = new List<List<double>>(); 50 List<double> oneVariableTestData = ValueGenerator.GenerateSteps(-3, 3, 0.01).ToList(); 51 List<List<double>> testData = new List<List<double>>() { oneVariableTestData, oneVariableTestData }; 52 53 var combinations = ValueGenerator.GenerateAllCombinationsOfValuesInLists(testData).ToList(); 54 52 55 for (int i = 0; i < AllowedInputVariables.Count(); i++) { 53 data.Add(ValueGenerator.GenerateUniformDistributedValues(5000, -3, 3).ToList()); 56 data.Add(ValueGenerator.GenerateUniformDistributedValues(20, -3, 3).ToList()); 57 data[i].AddRange(combinations[i]); 54 58 } 55 59 -
trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Korns/KornsFunctionEleven.cs
r8825 r9007 27 27 public class KornsFunctionEleven : ArtificialRegressionDataDescriptor { 28 28 29 public override string Name { get { return "Korns 11 y = 6.87 + (11 * cos(7.23 * X0 * X0 * X0))"; } }29 public override string Name { get { return "Korns 11 y = 6.87 + (11 * cos(7.23 * X0³))"; } } 30 30 public override string Description { 31 31 get { … … 46 46 protected override string[] AllowedInputVariables { get { return new string[] { "X0", "X1", "X2", "X3", "X4" }; } } 47 47 protected override int TrainingPartitionStart { get { return 0; } } 48 protected override int TrainingPartitionEnd { get { return 5000; } }49 protected override int TestPartitionStart { get { return 5000; } }50 protected override int TestPartitionEnd { get { return 10000; } }48 protected override int TrainingPartitionEnd { get { return 10000; } } 49 protected override int TestPartitionStart { get { return 10000; } } 50 protected override int TestPartitionEnd { get { return 20000; } } 51 51 52 52 protected override List<List<double>> GenerateValues() { -
trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Korns/KornsFunctionFiveteen.cs
r8900 r9007 54 54 List<List<double>> data = new List<List<double>>(); 55 55 data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, -50, 50).ToList()); 56 data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, 0, 50).ToList()); // note: range is only [0,50] to prevent NaN values (deviates from gp benchmark paper)56 data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, -50, 50).ToList()); 57 57 data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, 0, 50).ToList()); // note: range is only [0,50] to prevent NaN values (deviates from gp benchmark paper) 58 58 data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, -50, 50).ToList()); -
trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Korns/KornsFunctionNine.cs
r8900 r9007 56 56 data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, 0, 50).ToList()); // note: range is only [0,50] to prevent NaN values (deviates from gp benchmark paper) 57 57 data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, 0, 50).ToList()); // note: range is only [0,50] to prevent NaN values (deviates from gp benchmark paper) 58 data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, -50, 0).ToList()); // note: range is only [-50,0] to prevent NaN values (deviates from gp benchmark paper)58 data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, -50, 50).ToList()); 59 59 data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, -50, 50).ToList()); 60 60 data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, -50, 50).ToList()); -
trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Korns/KornsFunctionSeven.cs
r8900 r9007 53 53 protected override List<List<double>> GenerateValues() { 54 54 List<List<double>> data = new List<List<double>>(); 55 data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, 0, 50).ToList()); // note: range is only [0,50] to prevent NaN values (deviates from gp benchmark paper)55 data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, -50, 50).ToList()); 56 56 data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, -50, 50).ToList()); 57 57 data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, -50, 50).ToList()); -
trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Nguyen/NguyenFunctionEight.cs
r8825 r9007 43 43 protected override int TrainingPartitionStart { get { return 0; } } 44 44 protected override int TrainingPartitionEnd { get { return 20; } } 45 protected override int TestPartitionStart { get { return 2 50; } }46 protected override int TestPartitionEnd { get { return 350; } }45 protected override int TestPartitionStart { get { return 20; } } 46 protected override int TestPartitionEnd { get { return 520; } } 47 47 48 48 protected override List<List<double>> GenerateValues() { 49 49 List<List<double>> data = new List<List<double>>(); 50 data.Add(ValueGenerator.GenerateUniformDistributedValues(5 00, 0, 4).ToList());50 data.Add(ValueGenerator.GenerateUniformDistributedValues(520, 0, 4).ToList()); 51 51 52 52 double x; -
trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Nguyen/NguyenFunctionEleven.cs
r8825 r9007 43 43 protected override int TrainingPartitionStart { get { return 0; } } 44 44 protected override int TrainingPartitionEnd { get { return 20; } } 45 protected override int TestPartitionStart { get { return 500; } }46 protected override int TestPartitionEnd { get { return 10 00; } }45 protected override int TestPartitionStart { get { return 20; } } 46 protected override int TestPartitionEnd { get { return 1020; } } 47 47 48 48 protected override List<List<double>> GenerateValues() { -
trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Nguyen/NguyenFunctionFive.cs
r8825 r9007 43 43 protected override int TrainingPartitionStart { get { return 0; } } 44 44 protected override int TrainingPartitionEnd { get { return 20; } } 45 protected override int TestPartitionStart { get { return 2 50; } }46 protected override int TestPartitionEnd { get { return 350; } }45 protected override int TestPartitionStart { get { return 20; } } 46 protected override int TestPartitionEnd { get { return 520; } } 47 47 48 48 protected override List<List<double>> GenerateValues() { 49 49 List<List<double>> data = new List<List<double>>(); 50 data.Add(ValueGenerator.GenerateUniformDistributedValues(5 00, -1, 1).ToList());50 data.Add(ValueGenerator.GenerateUniformDistributedValues(520, -1, 1).ToList()); 51 51 52 52 double x; … … 54 54 for (int i = 0; i < data[0].Count; i++) { 55 55 x = data[0][i]; 56 results.Add(Math.Sin( Math.Pow(x, 2)) * Math.Cos(x) - 1);56 results.Add(Math.Sin(x * x) * Math.Cos(x) - 1); 57 57 } 58 58 data.Add(results); -
trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Nguyen/NguyenFunctionFour.cs
r8825 r9007 43 43 protected override int TrainingPartitionStart { get { return 0; } } 44 44 protected override int TrainingPartitionEnd { get { return 20; } } 45 protected override int TestPartitionStart { get { return 2 50; } }46 protected override int TestPartitionEnd { get { return 350; } }45 protected override int TestPartitionStart { get { return 20; } } 46 protected override int TestPartitionEnd { get { return 520; } } 47 47 48 48 protected override List<List<double>> GenerateValues() { 49 49 List<List<double>> data = new List<List<double>>(); 50 data.Add(ValueGenerator.GenerateUniformDistributedValues(5 00, -1, 1).ToList());50 data.Add(ValueGenerator.GenerateUniformDistributedValues(520, -1, 1).ToList()); 51 51 52 52 double x; … … 54 54 for (int i = 0; i < data[0].Count; i++) { 55 55 x = data[0][i]; 56 results.Add(Math.Pow(x, 6) + Math.Pow(x, 5) + Math.Pow(x, 4) + Math.Pow(x, 3) + Math.Pow(x, 2)+ x);56 results.Add(Math.Pow(x, 6) + Math.Pow(x, 5) + Math.Pow(x, 4) + Math.Pow(x, 3) + x * x + x); 57 57 } 58 58 data.Add(results); -
trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Nguyen/NguyenFunctionNine.cs
r8825 r9007 43 43 protected override int TrainingPartitionStart { get { return 0; } } 44 44 protected override int TrainingPartitionEnd { get { return 20; } } 45 protected override int TestPartitionStart { get { return 500; } }46 protected override int TestPartitionEnd { get { return 10 00; } }45 protected override int TestPartitionStart { get { return 20; } } 46 protected override int TestPartitionEnd { get { return 1020; } } 47 47 48 48 protected override List<List<double>> GenerateValues() { … … 56 56 x = data[0][i]; 57 57 y = data[1][i]; 58 results.Add(Math.Sin(x) + Math.Sin( Math.Pow(y, 2)));58 results.Add(Math.Sin(x) + Math.Sin(y * y)); 59 59 } 60 60 data.Add(results); -
trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Nguyen/NguyenFunctionOne.cs
r8825 r9007 43 43 protected override int TrainingPartitionStart { get { return 0; } } 44 44 protected override int TrainingPartitionEnd { get { return 20; } } 45 protected override int TestPartitionStart { get { return 2 50; } }46 protected override int TestPartitionEnd { get { return 350; } }45 protected override int TestPartitionStart { get { return 20; } } 46 protected override int TestPartitionEnd { get { return 520; } } 47 47 48 48 protected override List<List<double>> GenerateValues() { 49 49 List<List<double>> data = new List<List<double>>(); 50 data.Add(ValueGenerator.GenerateUniformDistributedValues(5 00, -1, 1).ToList());50 data.Add(ValueGenerator.GenerateUniformDistributedValues(520, -1, 1).ToList()); 51 51 52 52 double x; -
trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Nguyen/NguyenFunctionSeven.cs
r8825 r9007 43 43 protected override int TrainingPartitionStart { get { return 0; } } 44 44 protected override int TrainingPartitionEnd { get { return 20; } } 45 protected override int TestPartitionStart { get { return 2 50; } }46 protected override int TestPartitionEnd { get { return 350; } }45 protected override int TestPartitionStart { get { return 20; } } 46 protected override int TestPartitionEnd { get { return 520; } } 47 47 48 48 protected override List<List<double>> GenerateValues() { 49 49 List<List<double>> data = new List<List<double>>(); 50 data.Add(ValueGenerator.GenerateUniformDistributedValues(5 00, 0, 2).ToList());50 data.Add(ValueGenerator.GenerateUniformDistributedValues(520, 0, 2).ToList()); 51 51 52 52 double x; … … 54 54 for (int i = 0; i < data[0].Count; i++) { 55 55 x = data[0][i]; 56 results.Add(Math.Log(x + 1) + Math.Log( Math.Pow(x, 2)+ 1));56 results.Add(Math.Log(x + 1) + Math.Log(x * x + 1)); 57 57 } 58 58 data.Add(results); -
trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Nguyen/NguyenFunctionSix.cs
r8825 r9007 43 43 protected override int TrainingPartitionStart { get { return 0; } } 44 44 protected override int TrainingPartitionEnd { get { return 20; } } 45 protected override int TestPartitionStart { get { return 2 50; } }46 protected override int TestPartitionEnd { get { return 350; } }45 protected override int TestPartitionStart { get { return 20; } } 46 protected override int TestPartitionEnd { get { return 520; } } 47 47 48 48 protected override List<List<double>> GenerateValues() { 49 49 List<List<double>> data = new List<List<double>>(); 50 data.Add(ValueGenerator.GenerateUniformDistributedValues(5 00, -1, 1).ToList());50 data.Add(ValueGenerator.GenerateUniformDistributedValues(520, -1, 1).ToList()); 51 51 52 52 double x; … … 54 54 for (int i = 0; i < data[0].Count; i++) { 55 55 x = data[0][i]; 56 results.Add(Math.Sin(x) + Math.Sin(x + Math.Pow(x, 2)));56 results.Add(Math.Sin(x) + Math.Sin(x + x*x)); 57 57 } 58 58 data.Add(results); -
trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Nguyen/NguyenFunctionTen.cs
r8825 r9007 43 43 protected override int TrainingPartitionStart { get { return 0; } } 44 44 protected override int TrainingPartitionEnd { get { return 20; } } 45 protected override int TestPartitionStart { get { return 500; } }46 protected override int TestPartitionEnd { get { return 10 00; } }45 protected override int TestPartitionStart { get { return 20; } } 46 protected override int TestPartitionEnd { get { return 1020; } } 47 47 48 48 protected override List<List<double>> GenerateValues() { 49 49 List<List<double>> data = new List<List<double>>(); 50 data.Add(ValueGenerator.GenerateUniformDistributedValues(10 00, 0, 1).ToList());51 data.Add(ValueGenerator.GenerateUniformDistributedValues(10 00, 0, 1).ToList());50 data.Add(ValueGenerator.GenerateUniformDistributedValues(1020, 0, 1).ToList()); 51 data.Add(ValueGenerator.GenerateUniformDistributedValues(1020, 0, 1).ToList()); 52 52 53 53 double x, y; -
trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Nguyen/NguyenFunctionThree.cs
r8825 r9007 43 43 protected override int TrainingPartitionStart { get { return 0; } } 44 44 protected override int TrainingPartitionEnd { get { return 20; } } 45 protected override int TestPartitionStart { get { return 2 50; } }46 protected override int TestPartitionEnd { get { return 350; } }45 protected override int TestPartitionStart { get { return 20; } } 46 protected override int TestPartitionEnd { get { return 520; } } 47 47 48 48 protected override List<List<double>> GenerateValues() { 49 49 List<List<double>> data = new List<List<double>>(); 50 data.Add(ValueGenerator.GenerateUniformDistributedValues(5 00, -1, 1).ToList());50 data.Add(ValueGenerator.GenerateUniformDistributedValues(520, -1, 1).ToList()); 51 51 52 52 double x; -
trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Nguyen/NguyenFunctionTwelve.cs
r8825 r9007 43 43 protected override int TrainingPartitionStart { get { return 0; } } 44 44 protected override int TrainingPartitionEnd { get { return 20; } } 45 protected override int TestPartitionStart { get { return 500; } }46 protected override int TestPartitionEnd { get { return 10 00; } }45 protected override int TestPartitionStart { get { return 20; } } 46 protected override int TestPartitionEnd { get { return 1020; } } 47 47 48 48 protected override List<List<double>> GenerateValues() { 49 49 List<List<double>> data = new List<List<double>>(); 50 data.Add(ValueGenerator.GenerateUniformDistributedValues(10 00, 0, 1).ToList());51 data.Add(ValueGenerator.GenerateUniformDistributedValues(10 00, 0, 1).ToList());50 data.Add(ValueGenerator.GenerateUniformDistributedValues(1020, 0, 1).ToList()); 51 data.Add(ValueGenerator.GenerateUniformDistributedValues(1020, 0, 1).ToList()); 52 52 53 53 double x, y; … … 56 56 x = data[0][i]; 57 57 y = data[1][i]; 58 results.Add(Math.Pow(x, 4) - Math.Pow(x, 3) + Math.Pow(y, 2)/ 2 - y);58 results.Add(Math.Pow(x, 4) - Math.Pow(x, 3) + y * y / 2 - y); 59 59 } 60 60 data.Add(results); -
trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Nguyen/NguyenFunctionTwo.cs
r8825 r9007 43 43 protected override int TrainingPartitionStart { get { return 0; } } 44 44 protected override int TrainingPartitionEnd { get { return 20; } } 45 protected override int TestPartitionStart { get { return 2 50; } }46 protected override int TestPartitionEnd { get { return 350; } }45 protected override int TestPartitionStart { get { return 20; } } 46 protected override int TestPartitionEnd { get { return 520; } } 47 47 48 48 protected override List<List<double>> GenerateValues() { 49 49 List<List<double>> data = new List<List<double>>(); 50 data.Add(ValueGenerator.GenerateUniformDistributedValues(5 00, -1, 1).ToList());50 data.Add(ValueGenerator.GenerateUniformDistributedValues(520, -1, 1).ToList()); 51 51 52 52 double x; … … 54 54 for (int i = 0; i < data[0].Count; i++) { 55 55 x = data[0][i]; 56 results.Add(Math.Pow(x, 4) + Math.Pow(x, 3) + Math.Pow(x, 2)+ x);56 results.Add(Math.Pow(x, 4) + Math.Pow(x, 3) + x*x + x); 57 57 } 58 58 data.Add(results);
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