Changeset 17966


Ignore:
Timestamp:
04/28/21 11:31:24 (11 days ago)
Author:
mkommend
Message:

#3075: Changed Feynman problem instances to new normal distributed RNG.

Location:
trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman
Files:
120 edited

Legend:

Unmodified
Added
Removed
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman1.cs

    r17805 r17966  
    6363        var f_noise     = new List<double>();
    6464        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * f.StandardDeviationPop();
    65         f_noise.AddRange(f.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     65        f_noise.AddRange(f.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    6666        data.Remove(f);
    6767        data.Add(f_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman10.cs

    r17805 r17966  
    7171        var Ef_noise    = new List<double>();
    7272        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * Ef.StandardDeviationPop();
    73         Ef_noise.AddRange(Ef.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     73        Ef_noise.AddRange(Ef.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    7474        data.Remove(Ef);
    7575        data.Add(Ef_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman100.cs

    r17805 r17966  
    7373        var j_noise     = new List<double>();
    7474        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * j.StandardDeviationPop();
    75         j_noise.AddRange(j.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     75        j_noise.AddRange(j.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    7676        data.Remove(j);
    7777        data.Add(j_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman11.cs

    r17805 r17966  
    6969        var F_noise     = new List<double>();
    7070        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * F.StandardDeviationPop();
    71         F_noise.AddRange(F.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     71        F_noise.AddRange(F.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    7272        data.Remove(F);
    7373        data.Add(F_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman12.cs

    r17805 r17966  
    7575        var F_noise     = new List<double>();
    7676        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * F.StandardDeviationPop();
    77         F_noise.AddRange(F.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     77        F_noise.AddRange(F.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    7878        data.Remove(F);
    7979        data.Add(F_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman13.cs

    r17805 r17966  
    7373        var K_noise     = new List<double>();
    7474        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * K.StandardDeviationPop();
    75         K_noise.AddRange(K.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     75        K_noise.AddRange(K.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    7676        data.Remove(K);
    7777        data.Add(K_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman14.cs

    r17805 r17966  
    7575        var U_noise     = new List<double>();
    7676        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * U.StandardDeviationPop();
    77         U_noise.AddRange(U.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     77        U_noise.AddRange(U.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    7878        data.Remove(U);
    7979        data.Add(U_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman15.cs

    r17805 r17966  
    7171        var U_noise     = new List<double>();
    7272        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * U.StandardDeviationPop();
    73         U_noise.AddRange(U.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     73        U_noise.AddRange(U.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    7474        data.Remove(U);
    7575        data.Add(U_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman16.cs

    r17805 r17966  
    6969        var U_noise     = new List<double>();
    7070        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * U.StandardDeviationPop();
    71         U_noise.AddRange(U.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     71        U_noise.AddRange(U.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    7272        data.Remove(U);
    7373        data.Add(U_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman17.cs

    r17805 r17966  
    1010    private readonly int trainingSamples;
    1111
    12     public Feynman17() : this((int) DateTime.Now.Ticks, 10000, 10000, null) { }
     12    public Feynman17() : this((int)DateTime.Now.Ticks, 10000, 10000, null) { }
    1313
    1414    public Feynman17(int seed) {
    15       Seed            = seed;
     15      Seed = seed;
    1616      trainingSamples = 10000;
    17       testSamples     = 10000;
    18       noiseRatio      = null;
     17      testSamples = 10000;
     18      noiseRatio = null;
    1919    }
    2020
    2121    public Feynman17(int seed, int trainingSamples, int testSamples, double? noiseRatio) {
    22       Seed                 = seed;
     22      Seed = seed;
    2323      this.trainingSamples = trainingSamples;
    24       this.testSamples     = testSamples;
    25       this.noiseRatio      = noiseRatio;
     24      this.testSamples = testSamples;
     25      this.noiseRatio = noiseRatio;
    2626    }
    2727
     
    2929      get {
    3030        return string.Format("I.15.3x (x-u*t)/sqrt(1-u**2/c**2) | {0}",
    31           noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
     31          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}", noiseRatio));
    3232      }
    3333    }
     
    3636
    3737    protected override string[] VariableNames {
    38       get { return new[] {"x", "u", "c", "t", noiseRatio == null ? "x1" : "x1_noise"}; }
     38      get { return new[] { "x", "u", "c", "t", noiseRatio == null ? "x1" : "x1_noise" }; }
    3939    }
    4040
    41     protected override string[] AllowedInputVariables { get { return new[] {"x", "u", "c", "t"}; } }
     41    protected override string[] AllowedInputVariables { get { return new[] { "x", "u", "c", "t" }; } }
    4242
    4343    public int Seed { get; private set; }
     
    4949
    5050    protected override List<List<double>> GenerateValues() {
    51       var rand = new MersenneTwister((uint) Seed);
     51      var rand = new MersenneTwister((uint)Seed);
    5252
    5353      var data = new List<List<double>>();
    54       var x    = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 5, 10).ToList();
    55       var u    = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 2).ToList();
    56       var c    = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 3, 20).ToList();
    57       var t    = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 2).ToList();
     54      var x = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 5, 10).ToList();
     55      var u = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 2).ToList();
     56      var c = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 3, 20).ToList();
     57      var t = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 2).ToList();
    5858
    5959      var x1 = new List<double>();
     
    7171
    7272      if (noiseRatio != null) {
    73         var x1_noise    = new List<double>();
    74         var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * x1.StandardDeviationPop();
    75         x1_noise.AddRange(x1.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     73        var x1_noise = new List<double>();
     74        var sigma_noise = (double)Math.Sqrt(noiseRatio.Value) * x1.StandardDeviationPop();
     75        x1_noise.AddRange(x1.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    7676        data.Remove(x1);
    7777        data.Add(x1_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman18.cs

    r17805 r17966  
    7373        var t1_noise    = new List<double>();
    7474        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * t1.StandardDeviationPop();
    75         t1_noise.AddRange(t1.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     75        t1_noise.AddRange(t1.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    7676        data.Remove(t1);
    7777        data.Add(t1_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman19.cs

    r17805 r17966  
    7171        var p_noise     = new List<double>();
    7272        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * p.StandardDeviationPop();
    73         p_noise.AddRange(p.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     73        p_noise.AddRange(p.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    7474        data.Remove(p);
    7575        data.Add(p_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman2.cs

    r17805 r17966  
    6969        var f_noise     = new List<double>();
    7070        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * f.StandardDeviationPop();
    71         f_noise.AddRange(f.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     71        f_noise.AddRange(f.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    7272        data.Remove(f);
    7373        data.Add(f_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman20.cs

    r17805 r17966  
    7171        var v1_noise    = new List<double>();
    7272        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * v1.StandardDeviationPop();
    73         v1_noise.AddRange(v1.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     73        v1_noise.AddRange(v1.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    7474        data.Remove(v1);
    7575        data.Add(v1_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman21.cs

    r17805 r17966  
    7373        var r_noise     = new List<double>();
    7474        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * r.StandardDeviationPop();
    75         r_noise.AddRange(r.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     75        r_noise.AddRange(r.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    7676        data.Remove(r);
    7777        data.Add(r_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman22.cs

    r17805 r17966  
    7171        var tau_noise   = new List<double>();
    7272        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * tau.StandardDeviationPop();
    73         tau_noise.AddRange(tau.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     73        tau_noise.AddRange(tau.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    7474        data.Remove(tau);
    7575        data.Add(tau_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman23.cs

    r17805 r17966  
    7373        var L_noise     = new List<double>();
    7474        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * L.StandardDeviationPop();
    75         L_noise.AddRange(L.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     75        L_noise.AddRange(L.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    7676        data.Remove(L);
    7777        data.Add(L_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman24.cs

    r17805 r17966  
    7373        var E_n_noise   = new List<double>();
    7474        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * E_n.StandardDeviationPop();
    75         E_n_noise.AddRange(E_n.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     75        E_n_noise.AddRange(E_n.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    7676        data.Remove(E_n);
    7777        data.Add(E_n_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman25.cs

    r17805 r17966  
    6969        var Volt_noise  = new List<double>();
    7070        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * Volt.StandardDeviationPop();
    71         Volt_noise.AddRange(Volt.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     71        Volt_noise.AddRange(Volt.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    7272        data.Remove(Volt);
    7373        data.Add(Volt_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman26.cs

    r17805 r17966  
    6969        var theta1_noise = new List<double>();
    7070        var sigma_noise  = (double) Math.Sqrt(noiseRatio.Value) * theta1.StandardDeviationPop();
    71         theta1_noise.AddRange(theta1.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     71        theta1_noise.AddRange(theta1.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    7272        data.Remove(theta1);
    7373        data.Add(theta1_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman27.cs

    r17805 r17966  
    7171        var foc_noise   = new List<double>();
    7272        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * foc.StandardDeviationPop();
    73         foc_noise.AddRange(foc.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     73        foc_noise.AddRange(foc.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    7474        data.Remove(foc);
    7575        data.Add(foc_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman28.cs

    r17805 r17966  
    6969        var k_noise     = new List<double>();
    7070        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * k.StandardDeviationPop();
    71         k_noise.AddRange(k.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     71        k_noise.AddRange(k.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    7272        data.Remove(k);
    7373        data.Add(k_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman29.cs

    r17805 r17966  
    7474        var x_noise     = new List<double>();
    7575        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * x.StandardDeviationPop();
    76         x_noise.AddRange(x.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     76        x_noise.AddRange(x.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    7777        data.Remove(x);
    7878        data.Add(x_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman3.cs

    r17805 r17966  
    7272        var f_noise     = new List<double>();
    7373        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * f.StandardDeviationPop();
    74         f_noise.AddRange(f.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     74        f_noise.AddRange(f.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    7575        data.Remove(f);
    7676        data.Add(f_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman30.cs

    r17805 r17966  
    7171        var Int_noise   = new List<double>();
    7272        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * Int.StandardDeviationPop();
    73         Int_noise.AddRange(Int.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     73        Int_noise.AddRange(Int.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    7474        data.Remove(Int);
    7575        data.Add(Int_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman31.cs

    r17805 r17966  
    7171        var theta_noise = new List<double>();
    7272        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * theta.StandardDeviationPop();
    73         theta_noise.AddRange(theta.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     73        theta_noise.AddRange(theta.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    7474        data.Remove(theta);
    7575        data.Add(theta_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman32.cs

    r17805 r17966  
    7373        var Pwr_noise   = new List<double>();
    7474        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * Pwr.StandardDeviationPop();
    75         Pwr_noise.AddRange(Pwr.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     75        Pwr_noise.AddRange(Pwr.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    7676        data.Remove(Pwr);
    7777        data.Add(Pwr_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman33.cs

    r17805 r17966  
    8181        var Pwr_noise   = new List<double>();
    8282        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * Pwr.StandardDeviationPop();
    83         Pwr_noise.AddRange(Pwr.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     83        Pwr_noise.AddRange(Pwr.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    8484        data.Remove(Pwr);
    8585        data.Add(Pwr_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman34.cs

    r17805 r17966  
    7373        var omega_noise = new List<double>();
    7474        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * omega.StandardDeviationPop();
    75         omega_noise.AddRange(omega.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     75        omega_noise.AddRange(omega.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    7676        data.Remove(omega);
    7777        data.Add(omega_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman35.cs

    r17805 r17966  
    7171        var omega_noise = new List<double>();
    7272        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * omega.StandardDeviationPop();
    73         omega_noise.AddRange(omega.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     73        omega_noise.AddRange(omega.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    7474        data.Remove(omega);
    7575        data.Add(omega_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman36.cs

    r17805 r17966  
    7171        var omega_noise = new List<double>();
    7272        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * omega.StandardDeviationPop();
    73         omega_noise.AddRange(omega.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     73        omega_noise.AddRange(omega.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    7474        data.Remove(omega);
    7575        data.Add(omega_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman37.cs

    r17805 r17966  
    6969        var E_n_noise   = new List<double>();
    7070        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * E_n.StandardDeviationPop();
    71         E_n_noise.AddRange(E_n.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     71        E_n_noise.AddRange(E_n.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    7272        data.Remove(E_n);
    7373        data.Add(E_n_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman38.cs

    r17805 r17966  
    7171        var Int_noise   = new List<double>();
    7272        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * Int.StandardDeviationPop();
    73         Int_noise.AddRange(Int.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     73        Int_noise.AddRange(Int.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    7474        data.Remove(Int);
    7575        data.Add(Int_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman39.cs

    r17805 r17966  
    7373        var r_noise     = new List<double>();
    7474        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * r.StandardDeviationPop();
    75         r_noise.AddRange(r.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     75        r_noise.AddRange(r.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    7676        data.Remove(r);
    7777        data.Add(r_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman4.cs

    r17805 r17966  
    7373        var d_noise     = new List<double>();
    7474        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * d.StandardDeviationPop();
    75         d_noise.AddRange(d.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     75        d_noise.AddRange(d.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    7676        data.Remove(d);
    7777        data.Add(d_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman40.cs

    r17805 r17966  
    6969        var E_n_noise   = new List<double>();
    7070        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * E_n.StandardDeviationPop();
    71         E_n_noise.AddRange(E_n.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     71        E_n_noise.AddRange(E_n.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    7272        data.Remove(E_n);
    7373        data.Add(E_n_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman41.cs

    r17805 r17966  
    7171        var E_n_noise   = new List<double>();
    7272        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * E_n.StandardDeviationPop();
    73         E_n_noise.AddRange(E_n.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     73        E_n_noise.AddRange(E_n.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    7474        data.Remove(E_n);
    7575        data.Add(E_n_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman42.cs

    r17805 r17966  
    7373        var pr_noise    = new List<double>();
    7474        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * pr.StandardDeviationPop();
    75         pr_noise.AddRange(pr.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     75        pr_noise.AddRange(pr.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    7676        data.Remove(pr);
    7777        data.Add(pr_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman43.cs

    r17805 r17966  
    7777        var n_noise     = new List<double>();
    7878        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * n.StandardDeviationPop();
    79         n_noise.AddRange(n.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     79        n_noise.AddRange(n.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    8080        data.Remove(n);
    8181        data.Add(n_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman44.cs

    r17805 r17966  
    7878        var L_rad_noise = new List<double>();
    7979        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * L_rad.StandardDeviationPop();
    80         L_rad_noise.AddRange(L_rad.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     80        L_rad_noise.AddRange(L_rad.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    8181        data.Remove(L_rad);
    8282        data.Add(L_rad_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman45.cs

    r17805 r17966  
    7373        var v_noise     = new List<double>();
    7474        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * v.StandardDeviationPop();
    75         v_noise.AddRange(v.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     75        v_noise.AddRange(v.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    7676        data.Remove(v);
    7777        data.Add(v_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman46.cs

    r17805 r17966  
    7171        var D_noise     = new List<double>();
    7272        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * D.StandardDeviationPop();
    73         D_noise.AddRange(D.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     73        D_noise.AddRange(D.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    7474        data.Remove(D);
    7575        data.Add(D_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman47.cs

    r17805 r17966  
    7373        var kappa_noise = new List<double>();
    7474        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * kappa.StandardDeviationPop();
    75         kappa_noise.AddRange(kappa.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     75        kappa_noise.AddRange(kappa.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    7676        data.Remove(kappa);
    7777        data.Add(kappa_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman48.cs

    r17805 r17966  
    7575        var E_n_noise   = new List<double>();
    7676        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * E_n.StandardDeviationPop();
    77         E_n_noise.AddRange(E_n.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     77        E_n_noise.AddRange(E_n.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    7878        data.Remove(E_n);
    7979        data.Add(E_n_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman49.cs

    r17805 r17966  
    7171        var c_noise     = new List<double>();
    7272        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * c.StandardDeviationPop();
    73         c_noise.AddRange(c.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     73        c_noise.AddRange(c.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    7474        data.Remove(c);
    7575        data.Add(c_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman5.cs

    r17805 r17966  
    8686        var F_noise     = new List<double>();
    8787        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * F.StandardDeviationPop();
    88         F_noise.AddRange(F.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     88        F_noise.AddRange(F.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    8989        data.Remove(F);
    9090        data.Add(F_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman50.cs

    r17805 r17966  
    7171        var E_n_noise   = new List<double>();
    7272        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * E_n.StandardDeviationPop();
    73         E_n_noise.AddRange(E_n.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     73        E_n_noise.AddRange(E_n.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    7474        data.Remove(E_n);
    7575        data.Add(E_n_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman51.cs

    r17805 r17966  
    7373        var x_noise     = new List<double>();
    7474        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * x.StandardDeviationPop();
    75         x_noise.AddRange(x.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     75        x_noise.AddRange(x.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    7676        data.Remove(x);
    7777        data.Add(x_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman52.cs

    r17805 r17966  
    7575        var Pwr_noise   = new List<double>();
    7676        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * Pwr.StandardDeviationPop();
    77         Pwr_noise.AddRange(Pwr.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     77        Pwr_noise.AddRange(Pwr.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    7878        data.Remove(Pwr);
    7979        data.Add(Pwr_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman53.cs

    r17805 r17966  
    6969        var flux_noise  = new List<double>();
    7070        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * flux.StandardDeviationPop();
    71         flux_noise.AddRange(flux.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     71        flux_noise.AddRange(flux.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    7272        data.Remove(flux);
    7373        data.Add(flux_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman54.cs

    r17805 r17966  
    7171        var Volt_noise  = new List<double>();
    7272        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * Volt.StandardDeviationPop();
    73         Volt_noise.AddRange(Volt.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     73        Volt_noise.AddRange(Volt.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    7474        data.Remove(Volt);
    7575        data.Add(Volt_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman55.cs

    r17805 r17966  
    7373        var Volt_noise  = new List<double>();
    7474        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * Volt.StandardDeviationPop();
    75         Volt_noise.AddRange(Volt.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     75        Volt_noise.AddRange(Volt.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    7676        data.Remove(Volt);
    7777        data.Add(Volt_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman56.cs

    r17805 r17966  
    7878        var Ef_noise    = new List<double>();
    7979        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * Ef.StandardDeviationPop();
    80         Ef_noise.AddRange(Ef.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     80        Ef_noise.AddRange(Ef.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    8181        data.Remove(Ef);
    8282        data.Add(Ef_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman57.cs

    r17805 r17966  
    7474        var Ef_noise    = new List<double>();
    7575        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * Ef.StandardDeviationPop();
    76         Ef_noise.AddRange(Ef.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     76        Ef_noise.AddRange(Ef.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    7777        data.Remove(Ef);
    7878        data.Add(Ef_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman58.cs

    r17805 r17966  
    7171        var E_n_noise   = new List<double>();
    7272        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * E_n.StandardDeviationPop();
    73         E_n_noise.AddRange(E_n.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     73        E_n_noise.AddRange(E_n.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    7474        data.Remove(E_n);
    7575        data.Add(E_n_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman59.cs

    r17805 r17966  
    6969        var E_den_noise = new List<double>();
    7070        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * E_den.StandardDeviationPop();
    71         E_den_noise.AddRange(E_den.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     71        E_den_noise.AddRange(E_den.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    7272        data.Remove(E_den);
    7373        data.Add(E_den_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman6.cs

    r17805 r17966  
    7171        var m_noise     = new List<double>();
    7272        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * m.StandardDeviationPop();
    73         m_noise.AddRange(m.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     73        m_noise.AddRange(m.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    7474        data.Remove(m);
    7575        data.Add(m_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman60.cs

    r17805 r17966  
    7171        var Ef_noise    = new List<double>();
    7272        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * Ef.StandardDeviationPop();
    73         Ef_noise.AddRange(Ef.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     73        Ef_noise.AddRange(Ef.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    7474        data.Remove(Ef);
    7575        data.Add(Ef_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman61.cs

    r17805 r17966  
    7575        var x_noise     = new List<double>();
    7676        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * x.StandardDeviationPop();
    77         x_noise.AddRange(x.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     77        x_noise.AddRange(x.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    7878        data.Remove(x);
    7979        data.Add(x_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman62.cs

    r17805 r17966  
    7777        var n_noise     = new List<double>();
    7878        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * n.StandardDeviationPop();
    79         n_noise.AddRange(n.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     79        n_noise.AddRange(n.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    8080        data.Remove(n);
    8181        data.Add(n_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman63.cs

    r17805 r17966  
    7575        var Pol_noise   = new List<double>();
    7676        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * Pol.StandardDeviationPop();
    77         Pol_noise.AddRange(Pol.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     77        Pol_noise.AddRange(Pol.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    7878        data.Remove(Pol);
    7979        data.Add(Pol_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman64.cs

    r17805 r17966  
    7373        var Pol_noise   = new List<double>();
    7474        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * Pol.StandardDeviationPop();
    75         Pol_noise.AddRange(Pol.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     75        Pol_noise.AddRange(Pol.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    7676        data.Remove(Pol);
    7777        data.Add(Pol_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman65.cs

    r17805 r17966  
    6969        var theta_noise = new List<double>();
    7070        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * theta.StandardDeviationPop();
    71         theta_noise.AddRange(theta.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     71        theta_noise.AddRange(theta.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    7272        data.Remove(theta);
    7373        data.Add(theta_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman66.cs

    r17805 r17966  
    7373        var B_noise     = new List<double>();
    7474        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * B.StandardDeviationPop();
    75         B_noise.AddRange(B.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     75        B_noise.AddRange(B.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    7676        data.Remove(B);
    7777        data.Add(B_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman67.cs

    r17805 r17966  
    7171        var rho_c_noise = new List<double>();
    7272        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * rho_c.StandardDeviationPop();
    73         rho_c_noise.AddRange(rho_c.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     73        rho_c_noise.AddRange(rho_c.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    7474        data.Remove(rho_c);
    7575        data.Add(rho_c_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman68.cs

    r17805 r17966  
    7171        var j_noise     = new List<double>();
    7272        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * j.StandardDeviationPop();
    73         j_noise.AddRange(j.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     73        j_noise.AddRange(j.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    7474        data.Remove(j);
    7575        data.Add(j_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman69.cs

    r17805 r17966  
    7171        var E_n_noise   = new List<double>();
    7272        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * E_n.StandardDeviationPop();
    73         E_n_noise.AddRange(E_n.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     73        E_n_noise.AddRange(E_n.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    7474        data.Remove(E_n);
    7575        data.Add(E_n_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman7.cs

    r17805 r17966  
    7777        var A_noise     = new List<double>();
    7878        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * A.StandardDeviationPop();
    79         A_noise.AddRange(A.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     79        A_noise.AddRange(A.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    8080        data.Remove(A);
    8181        data.Add(A_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman70.cs

    r17805 r17966  
    7171        var E_n_noise   = new List<double>();
    7272        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * E_n.StandardDeviationPop();
    73         E_n_noise.AddRange(E_n.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     73        E_n_noise.AddRange(E_n.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    7474        data.Remove(E_n);
    7575        data.Add(E_n_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman71.cs

    r17805 r17966  
    7575        var Volt_noise  = new List<double>();
    7676        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * Volt.StandardDeviationPop();
    77         Volt_noise.AddRange(Volt.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     77        Volt_noise.AddRange(Volt.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    7878        data.Remove(Volt);
    7979        data.Add(Volt_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman72.cs

    r17805 r17966  
    7171        var k_noise     = new List<double>();
    7272        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * k.StandardDeviationPop();
    73         k_noise.AddRange(k.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     73        k_noise.AddRange(k.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    7474        data.Remove(k);
    7575        data.Add(k_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman73.cs

    r17805 r17966  
    7171        var flux_noise  = new List<double>();
    7272        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * flux.StandardDeviationPop();
    73         flux_noise.AddRange(flux.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     73        flux_noise.AddRange(flux.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    7474        data.Remove(flux);
    7575        data.Add(flux_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman74.cs

    r17805 r17966  
    6969        var E_den_noise = new List<double>();
    7070        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * E_den.StandardDeviationPop();
    71         E_den_noise.AddRange(E_den.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     71        E_den_noise.AddRange(E_den.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    7272        data.Remove(E_den);
    7373        data.Add(E_den_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman75.cs

    r17805 r17966  
    7171        var I_noise     = new List<double>();
    7272        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * I.StandardDeviationPop();
    73         I_noise.AddRange(I.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     73        I_noise.AddRange(I.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    7474        data.Remove(I);
    7575        data.Add(I_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman76.cs

    r17805 r17966  
    7171        var mom_noise   = new List<double>();
    7272        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * mom.StandardDeviationPop();
    73         mom_noise.AddRange(mom.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     73        mom_noise.AddRange(mom.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    7474        data.Remove(mom);
    7575        data.Add(mom_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman77.cs

    r17805 r17966  
    7373        var omega_noise = new List<double>();
    7474        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * omega.StandardDeviationPop();
    75         omega_noise.AddRange(omega.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     75        omega_noise.AddRange(omega.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    7676        data.Remove(omega);
    7777        data.Add(omega_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman78.cs

    r17805 r17966  
    7171        var mom_noise   = new List<double>();
    7272        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * mom.StandardDeviationPop();
    73         mom_noise.AddRange(mom.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     73        mom_noise.AddRange(mom.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    7474        data.Remove(mom);
    7575        data.Add(mom_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman79.cs

    r17805 r17966  
    7575        var E_n_noise   = new List<double>();
    7676        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * E_n.StandardDeviationPop();
    77         E_n_noise.AddRange(E_n.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     77        E_n_noise.AddRange(E_n.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    7878        data.Remove(E_n);
    7979        data.Add(E_n_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman8.cs

    r17805 r17966  
    6969        var F_noise     = new List<double>();
    7070        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * F.StandardDeviationPop();
    71         F_noise.AddRange(F.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     71        F_noise.AddRange(F.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    7272        data.Remove(F);
    7373        data.Add(F_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman80.cs

    r17805 r17966  
    7575        var n_noise     = new List<double>();
    7676        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * n.StandardDeviationPop();
    77         n_noise.AddRange(n.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     77        n_noise.AddRange(n.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    7878        data.Remove(n);
    7979        data.Add(n_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman81.cs

    r17805 r17966  
    7575        var M_noise     = new List<double>();
    7676        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * M.StandardDeviationPop();
    77         M_noise.AddRange(M.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     77        M_noise.AddRange(M.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    7878        data.Remove(M);
    7979        data.Add(M_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman82.cs

    r17805 r17966  
    8585        var f_noise     = new List<double>();
    8686        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * f.StandardDeviationPop();
    87         f_noise.AddRange(f.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     87        f_noise.AddRange(f.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    8888        data.Remove(f);
    8989        data.Add(f_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman83.cs

    r17805 r17966  
    7171        var E_n_noise   = new List<double>();
    7272        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * E_n.StandardDeviationPop();
    73         E_n_noise.AddRange(E_n.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     73        E_n_noise.AddRange(E_n.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    7474        data.Remove(E_n);
    7575        data.Add(E_n_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman84.cs

    r17805 r17966  
    7373        var F_noise     = new List<double>();
    7474        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * F.StandardDeviationPop();
    75         F_noise.AddRange(F.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     75        F_noise.AddRange(F.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    7676        data.Remove(F);
    7777        data.Add(F_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman85.cs

    r17805 r17966  
    6969        var mu_S_noise  = new List<double>();
    7070        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * mu_S.StandardDeviationPop();
    71         mu_S_noise.AddRange(mu_S.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     71        mu_S_noise.AddRange(mu_S.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    7272        data.Remove(mu_S);
    7373        data.Add(mu_S_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman86.cs

    r17805 r17966  
    7373        var n_noise     = new List<double>();
    7474        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * n.StandardDeviationPop();
    75         n_noise.AddRange(n.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     75        n_noise.AddRange(n.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    7676        data.Remove(n);
    7777        data.Add(n_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman87.cs

    r17805 r17966  
    7474        var E_n_noise   = new List<double>();
    7575        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * E_n.StandardDeviationPop();
    76         E_n_noise.AddRange(E_n.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     76        E_n_noise.AddRange(E_n.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    7777        data.Remove(E_n);
    7878        data.Add(E_n_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman88.cs

    r17805 r17966  
    7171        var omega_noise = new List<double>();
    7272        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * omega.StandardDeviationPop();
    73         omega_noise.AddRange(omega.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     73        omega_noise.AddRange(omega.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    7474        data.Remove(omega);
    7575        data.Add(omega_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman89.cs

    r17805 r17966  
    7171        var prob_noise  = new List<double>();
    7272        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * prob.StandardDeviationPop();
    73         prob_noise.AddRange(prob.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     73        prob_noise.AddRange(prob.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    7474        data.Remove(prob);
    7575        data.Add(prob_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman9.cs

    r17805 r17966  
    7373        var F_noise     = new List<double>();
    7474        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * F.StandardDeviationPop();
    75         F_noise.AddRange(F.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     75        F_noise.AddRange(F.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    7676        data.Remove(F);
    7777        data.Add(F_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman90.cs

    r17805 r17966  
    8282        var prob_noise  = new List<double>();
    8383        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * prob.StandardDeviationPop();
    84         prob_noise.AddRange(prob.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     84        prob_noise.AddRange(prob.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    8585        data.Remove(prob);
    8686        data.Add(prob_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman91.cs

    r17805 r17966  
    7373        var E_n_noise   = new List<double>();
    7474        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * E_n.StandardDeviationPop();
    75         E_n_noise.AddRange(E_n.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     75        E_n_noise.AddRange(E_n.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    7676        data.Remove(E_n);
    7777        data.Add(E_n_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman92.cs

    r17805 r17966  
    6565        var L_noise     = new List<double>();
    6666        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * L.StandardDeviationPop();
    67         L_noise.AddRange(L.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     67        L_noise.AddRange(L.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    6868        data.Remove(L);
    6969        data.Add(L_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman93.cs

    r17805 r17966  
    7373        var v_noise     = new List<double>();
    7474        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * v.StandardDeviationPop();
    75         v_noise.AddRange(v.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     75        v_noise.AddRange(v.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    7676        data.Remove(v);
    7777        data.Add(v_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman94.cs

    r17805 r17966  
    7575        var I_noise     = new List<double>();
    7676        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * I.StandardDeviationPop();
    77         I_noise.AddRange(I.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     77        I_noise.AddRange(I.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    7878        data.Remove(I);
    7979        data.Add(I_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman95.cs

    r17805 r17966  
    7171        var E_n_noise   = new List<double>();
    7272        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * E_n.StandardDeviationPop();
    73         E_n_noise.AddRange(E_n.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     73        E_n_noise.AddRange(E_n.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    7474        data.Remove(E_n);
    7575        data.Add(E_n_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman96.cs

    r17805 r17966  
    7171        var m_noise     = new List<double>();
    7272        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * m.StandardDeviationPop();
    73         m_noise.AddRange(m.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     73        m_noise.AddRange(m.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    7474        data.Remove(m);
    7575        data.Add(m_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman97.cs

    r17805 r17966  
    7171        var k_noise     = new List<double>();
    7272        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * k.StandardDeviationPop();
    73         k_noise.AddRange(k.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     73        k_noise.AddRange(k.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    7474        data.Remove(k);
    7575        data.Add(k_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman98.cs

    r17805 r17966  
    7171        var f_noise     = new List<double>();
    7272        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * f.StandardDeviationPop();
    73         f_noise.AddRange(f.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     73        f_noise.AddRange(f.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    7474        data.Remove(f);
    7575        data.Add(f_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman99.cs

    r17805 r17966  
    7777        var E_n_noise   = new List<double>();
    7878        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * E_n.StandardDeviationPop();
    79         E_n_noise.AddRange(E_n.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     79        E_n_noise.AddRange(E_n.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    8080        data.Remove(E_n);
    8181        data.Add(E_n_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/FeynmanBonus1.cs

    r17805 r17966  
    8383        var A_noise     = new List<double>();
    8484        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * A.StandardDeviationPop();
    85         A_noise.AddRange(A.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     85        A_noise.AddRange(A.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    8686        data.Remove(A);
    8787        data.Add(A_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/FeynmanBonus10.cs

    r17805 r17966  
    7373        var t_noise     = new List<double>();
    7474        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * t.StandardDeviationPop();
    75         t_noise.AddRange(t.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     75        t_noise.AddRange(t.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    7676        data.Remove(t);
    7777        data.Add(t_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/FeynmanBonus11.cs

    r17805 r17966  
    8585        var alpha_noise = new List<double>();
    8686        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * alpha.StandardDeviationPop();
    87         alpha_noise.AddRange(alpha.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     87        alpha_noise.AddRange(alpha.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    8888        data.Remove(alpha);
    8989        data.Add(alpha_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/FeynmanBonus12.cs

    r17805 r17966  
    7979        var E_n_noise   = new List<double>();
    8080        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * E_n.StandardDeviationPop();
    81         E_n_noise.AddRange(E_n.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     81        E_n_noise.AddRange(E_n.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    8282        data.Remove(E_n);
    8383        data.Add(E_n_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/FeynmanBonus13.cs

    r17805 r17966  
    8080        var E_n_noise   = new List<double>();
    8181        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * E_n.StandardDeviationPop();
    82         E_n_noise.AddRange(E_n.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     82        E_n_noise.AddRange(E_n.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    8383        data.Remove(E_n);
    8484        data.Add(E_n_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/FeynmanBonus14.cs

    r17805 r17966  
    7878        var F_noise     = new List<double>();
    7979        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * F.StandardDeviationPop();
    80         F_noise.AddRange(F.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     80        F_noise.AddRange(F.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    8181        data.Remove(F);
    8282        data.Add(F_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/FeynmanBonus15.cs

    r17805 r17966  
    7575        var Volt_noise  = new List<double>();
    7676        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * Volt.StandardDeviationPop();
    77         Volt_noise.AddRange(Volt.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     77        Volt_noise.AddRange(Volt.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    7878        data.Remove(Volt);
    7979        data.Add(Volt_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/FeynmanBonus16.cs

    r17805 r17966  
    7777        var Volt_noise  = new List<double>();
    7878        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * Volt.StandardDeviationPop();
    79         Volt_noise.AddRange(Volt.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     79        Volt_noise.AddRange(Volt.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    8080        data.Remove(Volt);
    8181        data.Add(Volt_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/FeynmanBonus17.cs

    r17805 r17966  
    7575        var omega_0_noise = new List<double>();
    7676        var sigma_noise   = (double) Math.Sqrt(noiseRatio.Value) * omega_0.StandardDeviationPop();
    77         omega_0_noise.AddRange(omega_0.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     77        omega_0_noise.AddRange(omega_0.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    7878        data.Remove(omega_0);
    7979        data.Add(omega_0_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/FeynmanBonus18.cs

    r17805 r17966  
    7575        var rho_0_noise = new List<double>();
    7676        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * rho_0.StandardDeviationPop();
    77         rho_0_noise.AddRange(rho_0.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     77        rho_0_noise.AddRange(rho_0.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    7878        data.Remove(rho_0);
    7979        data.Add(rho_0_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/FeynmanBonus19.cs

    r17805 r17966  
    7979        var pr_noise    = new List<double>();
    8080        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * pr.StandardDeviationPop();
    81         pr_noise.AddRange(pr.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     81        pr_noise.AddRange(pr.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    8282        data.Remove(pr);
    8383        data.Add(pr_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/FeynmanBonus2.cs

    r17805 r17966  
    7575        var H_G_noise   = new List<double>();
    7676        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * H_G.StandardDeviationPop();
    77         H_G_noise.AddRange(H_G.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     77        H_G_noise.AddRange(H_G.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    7878        data.Remove(H_G);
    7979        data.Add(H_G_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/FeynmanBonus20.cs

    r17805 r17966  
    8484        var A_noise     = new List<double>();
    8585        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * A.StandardDeviationPop();
    86         A_noise.AddRange(A.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     86        A_noise.AddRange(A.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    8787        data.Remove(A);
    8888        data.Add(A_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/FeynmanBonus3.cs

    r17805 r17966  
    7474        var K_noise     = new List<double>();
    7575        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * K.StandardDeviationPop();
    76         K_noise.AddRange(K.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     76        K_noise.AddRange(K.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    7777        data.Remove(K);
    7878        data.Add(K_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/FeynmanBonus4.cs

    r17805 r17966  
    7777        var Pwr_noise   = new List<double>();
    7878        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * Pwr.StandardDeviationPop();
    79         Pwr_noise.AddRange(Pwr.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     79        Pwr_noise.AddRange(Pwr.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    8080        data.Remove(Pwr);
    8181        data.Add(Pwr_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/FeynmanBonus5.cs

    r17805 r17966  
    7272        var theta1_noise = new List<double>();
    7373        var sigma_noise  = (double) Math.Sqrt(noiseRatio.Value) * theta1.StandardDeviationPop();
    74         theta1_noise.AddRange(theta1.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     74        theta1_noise.AddRange(theta1.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    7575        data.Remove(theta1);
    7676        data.Add(theta1_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/FeynmanBonus6.cs

    r17805 r17966  
    7676        var I_noise     = new List<double>();
    7777        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * I.StandardDeviationPop();
    78         I_noise.AddRange(I.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     78        I_noise.AddRange(I.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    7979        data.Remove(I);
    8080        data.Add(I_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/FeynmanBonus7.cs

    r17805 r17966  
    7575        var v_noise     = new List<double>();
    7676        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * v.StandardDeviationPop();
    77         v_noise.AddRange(v.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     77        v_noise.AddRange(v.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    7878        data.Remove(v);
    7979        data.Add(v_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/FeynmanBonus8.cs

    r17805 r17966  
    8282        var k_noise     = new List<double>();
    8383        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * k.StandardDeviationPop();
    84         k_noise.AddRange(k.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     84        k_noise.AddRange(k.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    8585        data.Remove(k);
    8686        data.Add(k_noise);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/FeynmanBonus9.cs

    r17805 r17966  
    7474        var r_noise     = new List<double>();
    7575        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * r.StandardDeviationPop();
    76         r_noise.AddRange(r.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
     76        r_noise.AddRange(r.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
    7777        data.Remove(r);
    7878        data.Add(r_noise);
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