Changeset 7025 for branches/RegressionBenchmarks/HeuristicLab.Problems.DataAnalysis.Benchmarks/3.4/Generator/RegressionBenchmark.cs

Timestamp:

11/21/11 16:23:48 (12 years ago)

Author:

sforsten

Message:

#1669: benchmark problems of Nguyen, Korns and Keijzer from ​http://groups.csail.mit.edu/EVO-DesignOpt/GPBenchmarks/ have been added. The benchmark problems from ​http://www.vanillamodeling.com/ have been adapted to the ones from Vladislavleva.

Not all benchmarks are working correctly so far, but they will be tested soon.

File:

• branches/RegressionBenchmarks/HeuristicLab.Problems.DataAnalysis.Benchmarks/3.4/Generator/RegressionBenchmark.cs (modified) (7 diffs)

branches/RegressionBenchmarks/HeuristicLab.Problems.DataAnalysis.Benchmarks/3.4/Generator/RegressionBenchmark.cs

@@ -20 +20 @@
 #endregion
 
-using System.Collections;
 using System.Collections.Generic;
 using System.Linq;
@@ -31 +30 @@
 
     #region properties
-    public abstract List<string> InputVariable { get; }
-    public abstract string TargetVariable { get; }
-    public abstract IntRange TrainingPartition { get; }
-    public abstract IntRange TestPartition { get; }
+    protected string targetVariable;
+    protected List<string> inputVariables;
+    protected IntRange trainingPartition;
+    protected IntRange testPartition;
+
+    public List<string> InputVariable {
+      get { return inputVariables; }
+    }
+
+    public string TargetVariable {
+      get { return targetVariable; }
+    }
+
+    public IntRange TrainingPartition {
+      get { return trainingPartition; }
+    }
+
+    public IntRange TestPartition {
+      get { return testPartition; }
+    }
     #endregion
 
@@ -42 +57 @@
     }
 
-    protected abstract List<double> CalculateFunction(Dictionary<string, IList<double>> data);
+    protected abstract List<double> CalculateFunction(List<List<double>> data);
 
-    protected abstract Dictionary<string, IList<double>> GenerateInput(Dictionary<string, IList<double>> data);
+    protected abstract List<List<double>> GenerateInput(List<List<double>> dataList);
 
     public IDataAnalysisProblemData GenerateProblemData() {
-      Dictionary<string, IList<double>> data = new Dictionary<string, IList<double>>();
-      data.Add(this.TargetVariable, new List<double>());
-      foreach (var variable in this.InputVariable) {
-        data.Add(variable, new List<double>());
-      }
+      List<string> varNames = new List<string>();
+      varNames.Add(this.TargetVariable);
+      varNames.AddRange(InputVariable);
 
-      data = GenerateInput(data);
+      List<List<double>> dataList = GenerateInput(new List<List<double>>());
 
-      List<IList> values = new List<IList>();
-      foreach (var valueList in data.Values) {
-        values.Add((IList)valueList);
-      }
+      dataList.Insert(0, CalculateFunction(dataList));
 
-      Dataset dataset = new Dataset(data.Keys, values);
-      dataset.Name = this.Name;
+      Dataset dataset = new Dataset(varNames, dataList);
 
       RegressionProblemData problemData = new RegressionProblemData(dataset, dataset.DoubleVariables.Skip(1), dataset.DoubleVariables.First());
 
       problemData.Name = "Data generated for benchmark problem \"" + this.Name + "\"";
-
+      problemData.Description = this.Description;
 
       problemData.TestPartition.Start = this.TestPartition.Start;
@@ -77 +86 @@
     }
 
-    //private Dictionary<string, IList<double>> CalculateValues(Dictionary<string, IList<double>> data, DatasetDefinition dataDef) {
-    //  Random rand = new Random();
-    //  var combinationDataSet = AllCombinationsOf(dataDef.RangeVariables.Values.Select(range => range.Values).ToList());
-    //  int index = 0;
-    //  var help = dataDef.RangeVariables.Keys;
-    //  foreach (var dataSet in combinationDataSet) {
-    //    data[help.ElementAt(index)] = dataSet;
-    //    index++;
-    //  }
-    //  List<string> vars = new List<string>(dataDef.RandomVariables.Keys);
-    //  for (int i = 0; i < dataDef.AmountOfPoints; i++) {
-    //    foreach (var variable in vars) {
-    //      data[variable].Add(dataDef.RandomVariables[variable].Next());
-    //    }
-    //    //  data[TargetVariable].Add(CalculateFunction(data, vars));
-    //  }
-    //  int bla = 0;
-    //  var test = data.Values.Select((ind) => (ind.ElementAt(bla)));
-
-    //  return data;
-    //}
-
-    public static List<double> generateSteps(DoubleRange range, double stepWidth) {
+    public static List<double> GenerateSteps(DoubleRange range, double stepWidth) {
       return Enumerable.Range(0, (int)((range.End - range.Start) / stepWidth) + 1)
                                       .Select(i => (range.Start + i * stepWidth))
@@ -105 +92 @@
     }
 
-    public static List<double> generateUniformDistributedValues(int amount, DoubleRange range) {
+    public static List<double> GenerateUniformDistributedValues(int amount, DoubleRange range) {
       List<double> values = new List<double>();
       System.Random rand = new System.Random();
@@ -114 +101 @@
     }
 
-    public static List<double> generateNormalDistributedValues(int amount, double mu, double sigma) {
+    public static List<double> GenerateNormalDistributedValues(int amount, double mu, double sigma) {
       List<double> values = new List<double>();
       FastRandom rand = new FastRandom();
@@ -133 +120 @@
         combinations = AddExtraSet(combinations, set);
 
+      combinations = (from i in Enumerable.Range(0, sets.Count)
+                      select (from list in combinations
+                              select list.ElementAt(i)).ToList<double>()).ToList<List<double>>();
+
       return combinations;
     }