Changeset 17805


Ignore:
Timestamp:
12/30/20 09:31:09 (4 months ago)
Author:
gkronber
Message:

#3075 Use the same noise levels and calculation as in our experiments for the IEEE TeC paper. Reordered instances by name first and noise level second. Removed number of samples from the name.

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

Legend:

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

    r17678 r17805  
    2828    public override string Name {
    2929      get {
    30         return string.Format("I.6.20a exp(-theta**2/2)/sqrt(2*pi) | {0} samples | {1}", trainingSamples,
     30        return string.Format("I.6.20a exp(-theta**2/2)/sqrt(2*pi) | {0}",
    3131          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3232      }
     
    6262      if (noiseRatio != null) {
    6363        var f_noise     = new List<double>();
    64         var sigma_noise = (double) noiseRatio * f.StandardDeviationPop();
     64        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * f.StandardDeviationPop();
    6565        f_noise.AddRange(f.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    6666        data.Remove(f);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman10.cs

    r17678 r17805  
    2828    public override string Name {
    2929      get {
    30         return string.Format("I.12.4 q1/(4*pi*epsilon*r**2) | {0} samples | {1}", trainingSamples,
     30        return string.Format("I.12.4 q1/(4*pi*epsilon*r**2) | {0}",
    3131          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3232      }
     
    7070      if (noiseRatio != null) {
    7171        var Ef_noise    = new List<double>();
    72         var sigma_noise = (double) noiseRatio * Ef.StandardDeviationPop();
     72        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * Ef.StandardDeviationPop();
    7373        Ef_noise.AddRange(Ef.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    7474        data.Remove(Ef);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman100.cs

    r17678 r17805  
    2828    public override string Name {
    2929      get {
    30         return string.Format("III.21.20 -rho_c_0*q*A_vec/m | {0} samples | {1}", trainingSamples,
     30        return string.Format("III.21.20 -rho_c_0*q*A_vec/m | {0}",
    3131          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3232      }
     
    7272      if (noiseRatio != null) {
    7373        var j_noise     = new List<double>();
    74         var sigma_noise = (double) noiseRatio * j.StandardDeviationPop();
     74        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * j.StandardDeviationPop();
    7575        j_noise.AddRange(j.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    7676        data.Remove(j);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman11.cs

    r17678 r17805  
    2828    public override string Name {
    2929      get {
    30         return string.Format("I.12.5 q2*Ef | {0} samples | {1}", trainingSamples,
     30        return string.Format("I.12.5 q2*Ef | {0}",
    3131          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3232      }
     
    6868      if (noiseRatio != null) {
    6969        var F_noise     = new List<double>();
    70         var sigma_noise = (double) noiseRatio * F.StandardDeviationPop();
     70        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * F.StandardDeviationPop();
    7171        F_noise.AddRange(F.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    7272        data.Remove(F);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman12.cs

    r17678 r17805  
    2828    public override string Name {
    2929      get {
    30         return string.Format("I.12.11 q*(Ef + B*v*sin(theta)) | {0} samples | {1}", trainingSamples,
     30        return string.Format("I.12.11 q*(Ef + B*v*sin(theta)) | {0}",
    3131          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3232      }
     
    7474      if (noiseRatio != null) {
    7575        var F_noise     = new List<double>();
    76         var sigma_noise = (double) noiseRatio * F.StandardDeviationPop();
     76        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * F.StandardDeviationPop();
    7777        F_noise.AddRange(F.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    7878        data.Remove(F);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman13.cs

    r17678 r17805  
    2828    public override string Name {
    2929      get {
    30         return string.Format("I.13.4 1/2*m*(v**2+u**2+w**2) | {0} samples | {1}", trainingSamples,
     30        return string.Format("I.13.4 1/2*m*(v**2+u**2+w**2) | {0}",
    3131          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3232      }
     
    7272      if (noiseRatio != null) {
    7373        var K_noise     = new List<double>();
    74         var sigma_noise = (double) noiseRatio * K.StandardDeviationPop();
     74        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * K.StandardDeviationPop();
    7575        K_noise.AddRange(K.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    7676        data.Remove(K);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman14.cs

    r17678 r17805  
    2828    public override string Name {
    2929      get {
    30         return string.Format("I.13.12 G*m1*m2*(1/r2-1/r1) | {0} samples | {1}", trainingSamples,
     30        return string.Format("I.13.12 G*m1*m2*(1/r2-1/r1) | {0}",
    3131          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3232      }
     
    7474      if (noiseRatio != null) {
    7575        var U_noise     = new List<double>();
    76         var sigma_noise = (double) noiseRatio * U.StandardDeviationPop();
     76        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * U.StandardDeviationPop();
    7777        U_noise.AddRange(U.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    7878        data.Remove(U);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman15.cs

    r17678 r17805  
    2828    public override string Name {
    2929      get {
    30         return string.Format("I.14.3 m*g*z | {0} samples | {1}", trainingSamples,
     30        return string.Format("I.14.3 m*g*z | {0}",
    3131          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3232      }
     
    7070      if (noiseRatio != null) {
    7171        var U_noise     = new List<double>();
    72         var sigma_noise = (double) noiseRatio * U.StandardDeviationPop();
     72        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * U.StandardDeviationPop();
    7373        U_noise.AddRange(U.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    7474        data.Remove(U);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman16.cs

    r17678 r17805  
    2828    public override string Name {
    2929      get {
    30         return string.Format("I.14.4 1/2*k_spring*x**2 | {0} samples | {1}", trainingSamples,
     30        return string.Format("I.14.4 1/2*k_spring*x**2 | {0}",
    3131          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3232      }
     
    6868      if (noiseRatio != null) {
    6969        var U_noise     = new List<double>();
    70         var sigma_noise = (double) noiseRatio * U.StandardDeviationPop();
     70        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * U.StandardDeviationPop();
    7171        U_noise.AddRange(U.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    7272        data.Remove(U);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman17.cs

    r17678 r17805  
    2828    public override string Name {
    2929      get {
    30         return string.Format("I.15.3x (x-u*t)/sqrt(1-u**2/c**2) | {0} samples | {1}", trainingSamples,
     30        return string.Format("I.15.3x (x-u*t)/sqrt(1-u**2/c**2) | {0}",
    3131          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3232      }
     
    7272      if (noiseRatio != null) {
    7373        var x1_noise    = new List<double>();
    74         var sigma_noise = (double) noiseRatio * x1.StandardDeviationPop();
     74        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * x1.StandardDeviationPop();
    7575        x1_noise.AddRange(x1.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    7676        data.Remove(x1);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman18.cs

    r17678 r17805  
    2828    public override string Name {
    2929      get {
    30         return string.Format("I.15.3t (t-u*x/c**2)/sqrt(1-u**2/c**2) | {0} samples | {1}",
    31           trainingSamples, noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
     30        return string.Format("I.15.3t (t-u*x/c**2)/sqrt(1-u**2/c**2) | {0}",
     31          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3232      }
    3333    }
     
    7272      if (noiseRatio != null) {
    7373        var t1_noise    = new List<double>();
    74         var sigma_noise = (double) noiseRatio * t1.StandardDeviationPop();
     74        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * t1.StandardDeviationPop();
    7575        t1_noise.AddRange(t1.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    7676        data.Remove(t1);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman19.cs

    r17678 r17805  
    2828    public override string Name {
    2929      get {
    30         return string.Format("I.15.10 m_0*v/sqrt(1-v**2/c**2) | {0} samples | {1}", trainingSamples,
     30        return string.Format("I.15.10 m_0*v/sqrt(1-v**2/c**2) | {0}",
    3131          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3232      }
     
    7070      if (noiseRatio != null) {
    7171        var p_noise     = new List<double>();
    72         var sigma_noise = (double) noiseRatio * p.StandardDeviationPop();
     72        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * p.StandardDeviationPop();
    7373        p_noise.AddRange(p.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    7474        data.Remove(p);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman2.cs

    r17678 r17805  
    2828    public override string Name {
    2929      get {
    30         return string.Format("I.6.20 exp(-(theta/sigma)**2/2)/(sqrt(2*pi)*sigma) | {0} samples | {1}",
    31           trainingSamples, noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
     30        return string.Format("I.6.20 exp(-(theta/sigma)**2/2)/(sqrt(2*pi)*sigma) | {0}",
     31          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3232      }
    3333    }
     
    6868      if (noiseRatio != null) {
    6969        var f_noise     = new List<double>();
    70         var sigma_noise = (double) noiseRatio * f.StandardDeviationPop();
     70        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * f.StandardDeviationPop();
    7171        f_noise.AddRange(f.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    7272        data.Remove(f);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman20.cs

    r17678 r17805  
    2828    public override string Name {
    2929      get {
    30         return string.Format("I.16.6 (u+v)/(1+u*v/c**2) | {0} samples | {1}", trainingSamples,
     30        return string.Format("I.16.6 (u+v)/(1+u*v/c**2) | {0}",
    3131          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3232      }
     
    7070      if (noiseRatio != null) {
    7171        var v1_noise    = new List<double>();
    72         var sigma_noise = (double) noiseRatio * v1.StandardDeviationPop();
     72        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * v1.StandardDeviationPop();
    7373        v1_noise.AddRange(v1.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    7474        data.Remove(v1);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman21.cs

    r17678 r17805  
    2828    public override string Name {
    2929      get {
    30         return string.Format("I.18.4 (m1*r1 + m2*r2)/(m1 + m2) | {0} samples | {1}", trainingSamples,
     30        return string.Format("I.18.4 (m1*r1 + m2*r2)/(m1 + m2) | {0}",
    3131          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3232      }
     
    7272      if (noiseRatio != null) {
    7373        var r_noise     = new List<double>();
    74         var sigma_noise = (double) noiseRatio * r.StandardDeviationPop();
     74        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * r.StandardDeviationPop();
    7575        r_noise.AddRange(r.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    7676        data.Remove(r);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman22.cs

    r17678 r17805  
    2828    public override string Name {
    2929      get {
    30         return string.Format("I.18.12 r*F*sin(theta) | {0} samples | {1}", trainingSamples,
     30        return string.Format("I.18.12 r*F*sin(theta) | {0}",
    3131          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3232      }
     
    7070      if (noiseRatio != null) {
    7171        var tau_noise   = new List<double>();
    72         var sigma_noise = (double) noiseRatio * tau.StandardDeviationPop();
     72        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * tau.StandardDeviationPop();
    7373        tau_noise.AddRange(tau.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    7474        data.Remove(tau);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman23.cs

    r17678 r17805  
    2828    public override string Name {
    2929      get {
    30         return string.Format("I.18.16 m*r*v*sin(theta) | {0} samples | {1}", trainingSamples,
     30        return string.Format("I.18.16 m*r*v*sin(theta) | {0}",
    3131          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3232      }
     
    7272      if (noiseRatio != null) {
    7373        var L_noise     = new List<double>();
    74         var sigma_noise = (double) noiseRatio * L.StandardDeviationPop();
     74        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * L.StandardDeviationPop();
    7575        L_noise.AddRange(L.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    7676        data.Remove(L);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman24.cs

    r17678 r17805  
    2828    public override string Name {
    2929      get {
    30         return string.Format("I.24.6 1/4*m*(omega**2 + omega_0**2)*x**2 | {0} samples | {1}",
    31           trainingSamples, noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
     30        return string.Format("I.24.6 1/4*m*(omega**2 + omega_0**2)*x**2 | {0}",
     31          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3232      }
    3333    }
     
    7272      if (noiseRatio != null) {
    7373        var E_n_noise   = new List<double>();
    74         var sigma_noise = (double) noiseRatio * E_n.StandardDeviationPop();
     74        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * E_n.StandardDeviationPop();
    7575        E_n_noise.AddRange(E_n.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    7676        data.Remove(E_n);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman25.cs

    r17678 r17805  
    2828    public override string Name {
    2929      get {
    30         return string.Format("I.25.13 q/C | {0} samples | {1}", trainingSamples,
     30        return string.Format("I.25.13 q/C | {0}",
    3131          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3232      }
     
    6868      if (noiseRatio != null) {
    6969        var Volt_noise  = new List<double>();
    70         var sigma_noise = (double) noiseRatio * Volt.StandardDeviationPop();
     70        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * Volt.StandardDeviationPop();
    7171        Volt_noise.AddRange(Volt.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    7272        data.Remove(Volt);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman26.cs

    r17678 r17805  
    2828    public override string Name {
    2929      get {
    30         return string.Format("I.26.2 arcsin(n*sin(theta2)) | {0} samples | {1}", trainingSamples,
     30        return string.Format("I.26.2 arcsin(n*sin(theta2)) | {0}",
    3131          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3232      }
     
    6868      if (noiseRatio != null) {
    6969        var theta1_noise = new List<double>();
    70         var sigma_noise  = (double) noiseRatio * theta1.StandardDeviationPop();
     70        var sigma_noise  = (double) Math.Sqrt(noiseRatio.Value) * theta1.StandardDeviationPop();
    7171        theta1_noise.AddRange(theta1.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    7272        data.Remove(theta1);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman27.cs

    r17678 r17805  
    2828    public override string Name {
    2929      get {
    30         return string.Format("I.27.6 1/(1/d1+n/d2) | {0} samples | {1}", trainingSamples,
     30        return string.Format("I.27.6 1/(1/d1+n/d2) | {0}",
    3131          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3232      }
     
    7070      if (noiseRatio != null) {
    7171        var foc_noise   = new List<double>();
    72         var sigma_noise = (double) noiseRatio * foc.StandardDeviationPop();
     72        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * foc.StandardDeviationPop();
    7373        foc_noise.AddRange(foc.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    7474        data.Remove(foc);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman28.cs

    r17678 r17805  
    2828    public override string Name {
    2929      get {
    30         return string.Format("I.29.4 omega/c | {0} samples | {1}", trainingSamples,
     30        return string.Format("I.29.4 omega/c | {0}",
    3131          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3232      }
     
    6868      if (noiseRatio != null) {
    6969        var k_noise     = new List<double>();
    70         var sigma_noise = (double) noiseRatio * k.StandardDeviationPop();
     70        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * k.StandardDeviationPop();
    7171        k_noise.AddRange(k.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    7272        data.Remove(k);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman29.cs

    r17678 r17805  
    2828    public override string Name {
    2929      get {
    30         return string.Format("I.29.16 sqrt(x1**2+x2**2 - 2*x1*x2*cos(theta1 - theta2)) | {0} samples | {1}",
    31           trainingSamples, noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
     30        return string.Format("I.29.16 sqrt(x1**2+x2**2 - 2*x1*x2*cos(theta1 - theta2)) | {0}",
     31          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3232      }
    3333    }
     
    7373      if (noiseRatio != null) {
    7474        var x_noise     = new List<double>();
    75         var sigma_noise = (double) noiseRatio * x.StandardDeviationPop();
     75        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * x.StandardDeviationPop();
    7676        x_noise.AddRange(x.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    7777        data.Remove(x);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman3.cs

    r17678 r17805  
    2929      get {
    3030        return string.Format(
    31           "I.6.20b exp(-((theta-theta1)/sigma)**2/2)/(sqrt(2*pi)*sigma) | {0} samples | {1}",
    32           trainingSamples, noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
     31          "I.6.20b exp(-((theta-theta1)/sigma)**2/2)/(sqrt(2*pi)*sigma) | {0}",
     32          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3333      }
    3434    }
     
    7171      if (noiseRatio != null) {
    7272        var f_noise     = new List<double>();
    73         var sigma_noise = (double) noiseRatio * f.StandardDeviationPop();
     73        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * f.StandardDeviationPop();
    7474        f_noise.AddRange(f.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    7575        data.Remove(f);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman30.cs

    r17678 r17805  
    2828    public override string Name {
    2929      get {
    30         return string.Format("I.30.3 Int_0*sin(n*theta/2)**2/sin(theta/2)**2 | {0} samples | {1}",
    31           trainingSamples, noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
     30        return string.Format("I.30.3 Int_0*sin(n*theta/2)**2/sin(theta/2)**2 | {0}",
     31          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3232      }
    3333    }
     
    7070      if (noiseRatio != null) {
    7171        var Int_noise   = new List<double>();
    72         var sigma_noise = (double) noiseRatio * Int.StandardDeviationPop();
     72        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * Int.StandardDeviationPop();
    7373        Int_noise.AddRange(Int.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    7474        data.Remove(Int);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman31.cs

    r17678 r17805  
    2828    public override string Name {
    2929      get {
    30         return string.Format("I.30.5 arcsin(lambd/(n*d)) | {0} samples | {1}", trainingSamples,
     30        return string.Format("I.30.5 arcsin(lambd/(n*d)) | {0}",
    3131          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3232      }
     
    7070      if (noiseRatio != null) {
    7171        var theta_noise = new List<double>();
    72         var sigma_noise = (double) noiseRatio * theta.StandardDeviationPop();
     72        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * theta.StandardDeviationPop();
    7373        theta_noise.AddRange(theta.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    7474        data.Remove(theta);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman32.cs

    r17678 r17805  
    2828    public override string Name {
    2929      get {
    30         return string.Format("I.32.5 q**2*a**2/(6*pi*epsilon*c**3) | {0} samples | {1}",
    31           trainingSamples, noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
     30        return string.Format("I.32.5 q**2*a**2/(6*pi*epsilon*c**3) | {0}",
     31          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3232      }
    3333    }
     
    7272      if (noiseRatio != null) {
    7373        var Pwr_noise   = new List<double>();
    74         var sigma_noise = (double) noiseRatio * Pwr.StandardDeviationPop();
     74        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * Pwr.StandardDeviationPop();
    7575        Pwr_noise.AddRange(Pwr.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    7676        data.Remove(Pwr);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman33.cs

    r17678 r17805  
    2929      get {
    3030        return string.Format(
    31           "I.32.17 (1/2*epsilon*c*Ef**2)*(8*pi*r**2/3)*(omega**4/(omega**2-omega_0**2)**2) | {0} samples | {1}",
    32           trainingSamples, noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
     31          "I.32.17 (1/2*epsilon*c*Ef**2)*(8*pi*r**2/3)*(omega**4/(omega**2-omega_0**2)**2) | {0}",
     32            noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3333      }
    3434    }
     
    8080      if (noiseRatio != null) {
    8181        var Pwr_noise   = new List<double>();
    82         var sigma_noise = (double) noiseRatio * Pwr.StandardDeviationPop();
     82        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * Pwr.StandardDeviationPop();
    8383        Pwr_noise.AddRange(Pwr.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    8484        data.Remove(Pwr);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman34.cs

    r17678 r17805  
    2828    public override string Name {
    2929      get {
    30         return string.Format("I.34.8 q*v*B/p | {0} samples | {1}", trainingSamples,
     30        return string.Format("I.34.8 q*v*B/p | {0}",
    3131          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3232      }
     
    7272      if (noiseRatio != null) {
    7373        var omega_noise = new List<double>();
    74         var sigma_noise = (double) noiseRatio * omega.StandardDeviationPop();
     74        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * omega.StandardDeviationPop();
    7575        omega_noise.AddRange(omega.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    7676        data.Remove(omega);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman35.cs

    r17678 r17805  
    2828    public override string Name {
    2929      get {
    30         return string.Format("I.34.10 omega_0/(1-v/c) | {0} samples | {1}", trainingSamples,
     30        return string.Format("I.34.10 omega_0/(1-v/c) | {0}",
    3131          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3232      }
     
    7070      if (noiseRatio != null) {
    7171        var omega_noise = new List<double>();
    72         var sigma_noise = (double) noiseRatio * omega.StandardDeviationPop();
     72        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * omega.StandardDeviationPop();
    7373        omega_noise.AddRange(omega.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    7474        data.Remove(omega);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman36.cs

    r17678 r17805  
    2828    public override string Name {
    2929      get {
    30         return string.Format("I.34.14 (1+v/c)/sqrt(1-v**2/c**2)*omega_0 | {0} samples | {1}",
    31           trainingSamples, noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
     30        return string.Format("I.34.14 (1+v/c)/sqrt(1-v**2/c**2)*omega_0 | {0}",
     31          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3232      }
    3333    }
     
    7070      if (noiseRatio != null) {
    7171        var omega_noise = new List<double>();
    72         var sigma_noise = (double) noiseRatio * omega.StandardDeviationPop();
     72        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * omega.StandardDeviationPop();
    7373        omega_noise.AddRange(omega.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    7474        data.Remove(omega);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman37.cs

    r17678 r17805  
    2828    public override string Name {
    2929      get {
    30         return string.Format("I.34.27 h*omega | {0} samples | {1}", trainingSamples,
     30        return string.Format("I.34.27 h*omega | {0}",
    3131          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3232      }
     
    6868      if (noiseRatio != null) {
    6969        var E_n_noise   = new List<double>();
    70         var sigma_noise = (double) noiseRatio * E_n.StandardDeviationPop();
     70        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * E_n.StandardDeviationPop();
    7171        E_n_noise.AddRange(E_n.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    7272        data.Remove(E_n);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman38.cs

    r17678 r17805  
    2828    public override string Name {
    2929      get {
    30         return string.Format("I.37.4 I1 + I2 + 2*sqrt(I1*I2)*cos(delta) | {0} samples | {1}",
    31           trainingSamples, noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
     30        return string.Format("I.37.4 I1 + I2 + 2*sqrt(I1*I2)*cos(delta) | {0}",
     31          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3232      }
    3333    }
     
    7070      if (noiseRatio != null) {
    7171        var Int_noise   = new List<double>();
    72         var sigma_noise = (double) noiseRatio * Int.StandardDeviationPop();
     72        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * Int.StandardDeviationPop();
    7373        Int_noise.AddRange(Int.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    7474        data.Remove(Int);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman39.cs

    r17678 r17805  
    2828    public override string Name {
    2929      get {
    30         return string.Format("I.38.12 4*pi*epsilon*h**2/(m*q**2) | {0} samples | {1}",
    31           trainingSamples, noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
     30        return string.Format("I.38.12 4*pi*epsilon*h**2/(m*q**2) | {0}",
     31          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3232      }
    3333    }
     
    7272      if (noiseRatio != null) {
    7373        var r_noise     = new List<double>();
    74         var sigma_noise = (double) noiseRatio * r.StandardDeviationPop();
     74        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * r.StandardDeviationPop();
    7575        r_noise.AddRange(r.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    7676        data.Remove(r);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman4.cs

    r17678 r17805  
    2828    public override string Name {
    2929      get {
    30         return string.Format("I.8.14 sqrt((x2-x1)**2+(y2-y1)**2) | {0} samples | {1}", trainingSamples,
     30        return string.Format("I.8.14 sqrt((x2-x1)**2+(y2-y1)**2) | {0}",
    3131          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3232      }
     
    7272      if (noiseRatio != null) {
    7373        var d_noise     = new List<double>();
    74         var sigma_noise = (double) noiseRatio * d.StandardDeviationPop();
     74        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * d.StandardDeviationPop();
    7575        d_noise.AddRange(d.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    7676        data.Remove(d);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman40.cs

    r17678 r17805  
    2828    public override string Name {
    2929      get {
    30         return string.Format("I.39.10 3/2*pF*V | {0} samples | {1}", trainingSamples,
     30        return string.Format("I.39.10 3/2*pF*V | {0}",
    3131          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3232      }
     
    6868      if (noiseRatio != null) {
    6969        var E_n_noise   = new List<double>();
    70         var sigma_noise = (double) noiseRatio * E_n.StandardDeviationPop();
     70        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * E_n.StandardDeviationPop();
    7171        E_n_noise.AddRange(E_n.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    7272        data.Remove(E_n);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman41.cs

    r17678 r17805  
    2828    public override string Name {
    2929      get {
    30         return string.Format("I.39.11 1/(gamma-1)*pF*V | {0} samples | {1}", trainingSamples,
     30        return string.Format("I.39.11 1/(gamma-1)*pF*V | {0}",
    3131          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3232      }
     
    7070      if (noiseRatio != null) {
    7171        var E_n_noise   = new List<double>();
    72         var sigma_noise = (double) noiseRatio * E_n.StandardDeviationPop();
     72        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * E_n.StandardDeviationPop();
    7373        E_n_noise.AddRange(E_n.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    7474        data.Remove(E_n);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman42.cs

    r17678 r17805  
    2828    public override string Name {
    2929      get {
    30         return string.Format("I.39.22 n*kb*T/V | {0} samples | {1}", trainingSamples,
     30        return string.Format("I.39.22 n*kb*T/V | {0}",
    3131          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3232      }
     
    7272      if (noiseRatio != null) {
    7373        var pr_noise    = new List<double>();
    74         var sigma_noise = (double) noiseRatio * pr.StandardDeviationPop();
     74        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * pr.StandardDeviationPop();
    7575        pr_noise.AddRange(pr.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    7676        data.Remove(pr);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman43.cs

    r17678 r17805  
    2828    public override string Name {
    2929      get {
    30         return string.Format("I.40.1 n_0*exp(-m*g*x/(kb*T)) | {0} samples | {1}", trainingSamples,
     30        return string.Format("I.40.1 n_0*exp(-m*g*x/(kb*T)) | {0}",
    3131          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3232      }
     
    7676      if (noiseRatio != null) {
    7777        var n_noise     = new List<double>();
    78         var sigma_noise = (double) noiseRatio * n.StandardDeviationPop();
     78        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * n.StandardDeviationPop();
    7979        n_noise.AddRange(n.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    8080        data.Remove(n);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman44.cs

    r17678 r17805  
    2929      get {
    3030        return string.Format(
    31           "I.41.16 h*omega**3/(pi**2 * c**2 * (exp(h*omega/(kb*T))-1)) | {0} samples | {1}",
    32           trainingSamples, noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
     31          "I.41.16 h*omega**3/(pi**2 * c**2 * (exp(h*omega/(kb*T))-1)) | {0}",
     32          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3333      }
    3434    }
     
    7777      if (noiseRatio != null) {
    7878        var L_rad_noise = new List<double>();
    79         var sigma_noise = (double) noiseRatio * L_rad.StandardDeviationPop();
     79        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * L_rad.StandardDeviationPop();
    8080        L_rad_noise.AddRange(L_rad.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    8181        data.Remove(L_rad);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman45.cs

    r17678 r17805  
    2828    public override string Name {
    2929      get {
    30         return string.Format("I.43.16 mu_drift*q*Volt/d | {0} samples | {1}", trainingSamples,
     30        return string.Format("I.43.16 mu_drift*q*Volt/d | {0}",
    3131          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3232      }
     
    7272      if (noiseRatio != null) {
    7373        var v_noise     = new List<double>();
    74         var sigma_noise = (double) noiseRatio * v.StandardDeviationPop();
     74        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * v.StandardDeviationPop();
    7575        v_noise.AddRange(v.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    7676        data.Remove(v);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman46.cs

    r17678 r17805  
    2828    public override string Name {
    2929      get {
    30         return string.Format("I.43.31 mob*kb*T | {0} samples | {1}", trainingSamples,
     30        return string.Format("I.43.31 mob*kb*T | {0}",
    3131          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3232      }
     
    7070      if (noiseRatio != null) {
    7171        var D_noise     = new List<double>();
    72         var sigma_noise = (double) noiseRatio * D.StandardDeviationPop();
     72        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * D.StandardDeviationPop();
    7373        D_noise.AddRange(D.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    7474        data.Remove(D);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman47.cs

    r17678 r17805  
    2828    public override string Name {
    2929      get {
    30         return string.Format("I.43.43 1/(gamma-1)*kb*v/A | {0} samples | {1}", trainingSamples,
     30        return string.Format("I.43.43 1/(gamma-1)*kb*v/A | {0}",
    3131          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3232      }
     
    7272      if (noiseRatio != null) {
    7373        var kappa_noise = new List<double>();
    74         var sigma_noise = (double) noiseRatio * kappa.StandardDeviationPop();
     74        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * kappa.StandardDeviationPop();
    7575        kappa_noise.AddRange(kappa.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    7676        data.Remove(kappa);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman48.cs

    r17678 r17805  
    2828    public override string Name {
    2929      get {
    30         return string.Format("I.44.4 n*kb*T*ln(V2/V1) | {0} samples | {1}", trainingSamples,
     30        return string.Format("I.44.4 n*kb*T*ln(V2/V1) | {0}",
    3131          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3232      }
     
    7474      if (noiseRatio != null) {
    7575        var E_n_noise   = new List<double>();
    76         var sigma_noise = (double) noiseRatio * E_n.StandardDeviationPop();
     76        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * E_n.StandardDeviationPop();
    7777        E_n_noise.AddRange(E_n.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    7878        data.Remove(E_n);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman49.cs

    r17678 r17805  
    2828    public override string Name {
    2929      get {
    30         return string.Format("I.47.23 sqrt(gamma*pr/rho) | {0} samples | {1}", trainingSamples,
     30        return string.Format("I.47.23 sqrt(gamma*pr/rho) | {0}",
    3131          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3232      }
     
    7070      if (noiseRatio != null) {
    7171        var c_noise     = new List<double>();
    72         var sigma_noise = (double) noiseRatio * c.StandardDeviationPop();
     72        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * c.StandardDeviationPop();
    7373        c_noise.AddRange(c.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    7474        data.Remove(c);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman5.cs

    r17678 r17805  
    2828    public override string Name {
    2929      get {
    30         return string.Format("I.9.18 G*m1*m2/((x2-x1)**2+(y2-y1)**2+(z2-z1)**2) | {0} samples | {1}",
    31           trainingSamples, noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
     30        return string.Format("I.9.18 G*m1*m2/((x2-x1)**2+(y2-y1)**2+(z2-z1)**2) | {0}",
     31          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3232      }
    3333    }
     
    8585      if (noiseRatio != null) {
    8686        var F_noise     = new List<double>();
    87         var sigma_noise = (double) noiseRatio * F.StandardDeviationPop();
     87        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * F.StandardDeviationPop();
    8888        F_noise.AddRange(F.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    8989        data.Remove(F);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman50.cs

    r17678 r17805  
    2828    public override string Name {
    2929      get {
    30         return string.Format("I.48.2 m*c**2/sqrt(1-v**2/c**2) | {0} samples | {1}", trainingSamples,
     30        return string.Format("I.48.2 m*c**2/sqrt(1-v**2/c**2) | {0}",
    3131          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3232      }
     
    7070      if (noiseRatio != null) {
    7171        var E_n_noise   = new List<double>();
    72         var sigma_noise = (double) noiseRatio * E_n.StandardDeviationPop();
     72        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * E_n.StandardDeviationPop();
    7373        E_n_noise.AddRange(E_n.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    7474        data.Remove(E_n);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman51.cs

    r17678 r17805  
    2828    public override string Name {
    2929      get {
    30         return string.Format("I.50.26 x1*(cos(omega*t)+alpha*cos(omega*t)**2) | {0} samples | {1}",
    31           trainingSamples, noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
     30        return string.Format("I.50.26 x1*(cos(omega*t)+alpha*cos(omega*t)**2) | {0}",
     31          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3232      }
    3333    }
     
    7272      if (noiseRatio != null) {
    7373        var x_noise     = new List<double>();
    74         var sigma_noise = (double) noiseRatio * x.StandardDeviationPop();
     74        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * x.StandardDeviationPop();
    7575        x_noise.AddRange(x.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    7676        data.Remove(x);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman52.cs

    r17678 r17805  
    2828    public override string Name {
    2929      get {
    30         return string.Format("II.2.42 kappa*(T2-T1)*A/d | {0} samples | {1}", trainingSamples,
     30        return string.Format("II.2.42 kappa*(T2-T1)*A/d | {0}",
    3131          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3232      }
     
    7474      if (noiseRatio != null) {
    7575        var Pwr_noise   = new List<double>();
    76         var sigma_noise = (double) noiseRatio * Pwr.StandardDeviationPop();
     76        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * Pwr.StandardDeviationPop();
    7777        Pwr_noise.AddRange(Pwr.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    7878        data.Remove(Pwr);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman53.cs

    r17678 r17805  
    2828    public override string Name {
    2929      get {
    30         return string.Format("II.3.24 Pwr/(4*pi*r**2) | {0} samples | {1}", trainingSamples,
     30        return string.Format("II.3.24 Pwr/(4*pi*r**2) | {0}",
    3131          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3232      }
     
    6868      if (noiseRatio != null) {
    6969        var flux_noise  = new List<double>();
    70         var sigma_noise = (double) noiseRatio * flux.StandardDeviationPop();
     70        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * flux.StandardDeviationPop();
    7171        flux_noise.AddRange(flux.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    7272        data.Remove(flux);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman54.cs

    r17678 r17805  
    2828    public override string Name {
    2929      get {
    30         return string.Format("II.4.23 q/(4*pi*epsilon*r) | {0} samples | {1}", trainingSamples,
     30        return string.Format("II.4.23 q/(4*pi*epsilon*r) | {0}",
    3131          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3232      }
     
    7070      if (noiseRatio != null) {
    7171        var Volt_noise  = new List<double>();
    72         var sigma_noise = (double) noiseRatio * Volt.StandardDeviationPop();
     72        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * Volt.StandardDeviationPop();
    7373        Volt_noise.AddRange(Volt.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    7474        data.Remove(Volt);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman55.cs

    r17678 r17805  
    2828    public override string Name {
    2929      get {
    30         return string.Format("II.6.11 1/(4*pi*epsilon)*p_d*cos(theta)/r**2 | {0} samples | {1}",
    31           trainingSamples, noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
     30        return string.Format("II.6.11 1/(4*pi*epsilon)*p_d*cos(theta)/r**2 | {0}",
     31          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3232      }
    3333    }
     
    7272      if (noiseRatio != null) {
    7373        var Volt_noise  = new List<double>();
    74         var sigma_noise = (double) noiseRatio * Volt.StandardDeviationPop();
     74        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * Volt.StandardDeviationPop();
    7575        Volt_noise.AddRange(Volt.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    7676        data.Remove(Volt);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman56.cs

    r17678 r17805  
    2828    public override string Name {
    2929      get {
    30         return string.Format("II.6.15a 3/(4*pi*epsilon)*p_d*z/r**5*sqrt(x**2+y**2) | {0} samples | {1}",
    31           trainingSamples, noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
     30        return string.Format("II.6.15a 3/(4*pi*epsilon)*p_d*z/r**5*sqrt(x**2+y**2) | {0}",
     31          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3232      }
    3333    }
     
    7777      if (noiseRatio != null) {
    7878        var Ef_noise    = new List<double>();
    79         var sigma_noise = (double) noiseRatio * Ef.StandardDeviationPop();
     79        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * Ef.StandardDeviationPop();
    8080        Ef_noise.AddRange(Ef.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    8181        data.Remove(Ef);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman57.cs

    r17678 r17805  
    2929      get {
    3030        return string.Format(
    31           "II.6.15b 3/(4*pi*epsilon)*p_d/r**3*cos(theta)*sin(theta) | {0} samples | {1}",
    32           trainingSamples, noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
     31          "II.6.15b 3/(4*pi*epsilon)*p_d/r**3*cos(theta)*sin(theta) | {0}",
     32          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3333      }
    3434    }
     
    7373      if (noiseRatio != null) {
    7474        var Ef_noise    = new List<double>();
    75         var sigma_noise = (double) noiseRatio * Ef.StandardDeviationPop();
     75        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * Ef.StandardDeviationPop();
    7676        Ef_noise.AddRange(Ef.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    7777        data.Remove(Ef);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman58.cs

    r17678 r17805  
    2828    public override string Name {
    2929      get {
    30         return string.Format("II.8.7 3/5*q**2/(4*pi*epsilon*d) | {0} samples | {1}", trainingSamples,
     30        return string.Format("II.8.7 3/5*q**2/(4*pi*epsilon*d) | {0}",
    3131          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3232      }
     
    7070      if (noiseRatio != null) {
    7171        var E_n_noise   = new List<double>();
    72         var sigma_noise = (double) noiseRatio * E_n.StandardDeviationPop();
     72        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * E_n.StandardDeviationPop();
    7373        E_n_noise.AddRange(E_n.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    7474        data.Remove(E_n);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman59.cs

    r17678 r17805  
    2828    public override string Name {
    2929      get {
    30         return string.Format("II.8.31 epsilon*Ef**2/2 | {0} samples | {1}", trainingSamples,
     30        return string.Format("II.8.31 epsilon*Ef**2/2 | {0}",
    3131          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3232      }
     
    6868      if (noiseRatio != null) {
    6969        var E_den_noise = new List<double>();
    70         var sigma_noise = (double) noiseRatio * E_den.StandardDeviationPop();
     70        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * E_den.StandardDeviationPop();
    7171        E_den_noise.AddRange(E_den.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    7272        data.Remove(E_den);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman6.cs

    r17678 r17805  
    2828    public override string Name {
    2929      get {
    30         return string.Format("I.10.7 m_0/sqrt(1-v**2/c**2) | {0} samples | {1}", trainingSamples,
     30        return string.Format("I.10.7 m_0/sqrt(1-v**2/c**2) | {0}",
    3131          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3232      }
     
    7070      if (noiseRatio != null) {
    7171        var m_noise     = new List<double>();
    72         var sigma_noise = (double) noiseRatio * m.StandardDeviationPop();
     72        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * m.StandardDeviationPop();
    7373        m_noise.AddRange(m.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    7474        data.Remove(m);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman60.cs

    r17678 r17805  
    2828    public override string Name {
    2929      get {
    30         return string.Format("II.10.9 sigma_den/epsilon*1/(1+chi) | {0} samples | {1}", trainingSamples,
     30        return string.Format("II.10.9 sigma_den/epsilon*1/(1+chi) | {0}",
    3131          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3232      }
     
    7070      if (noiseRatio != null) {
    7171        var Ef_noise    = new List<double>();
    72         var sigma_noise = (double) noiseRatio * Ef.StandardDeviationPop();
     72        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * Ef.StandardDeviationPop();
    7373        Ef_noise.AddRange(Ef.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    7474        data.Remove(Ef);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman61.cs

    r17678 r17805  
    2828    public override string Name {
    2929      get {
    30         return string.Format("II.11.3 q*Ef/(m*(omega_0**2-omega**2)) | {0} samples | {1}",
    31           trainingSamples, noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
     30        return string.Format("II.11.3 q*Ef/(m*(omega_0**2-omega**2)) | {0}",
     31          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3232      }
    3333    }
     
    7474      if (noiseRatio != null) {
    7575        var x_noise     = new List<double>();
    76         var sigma_noise = (double) noiseRatio * x.StandardDeviationPop();
     76        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * x.StandardDeviationPop();
    7777        x_noise.AddRange(x.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    7878        data.Remove(x);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman62.cs

    r17678 r17805  
    2828    public override string Name {
    2929      get {
    30         return string.Format("II.11.17 n_0*(1 + p_d*Ef*cos(theta)/(kb*T)) | {0} samples | {1}",
    31           trainingSamples, noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
     30        return string.Format("II.11.17 n_0*(1 + p_d*Ef*cos(theta)/(kb*T)) | {0}",
     31          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3232      }
    3333    }
     
    7676      if (noiseRatio != null) {
    7777        var n_noise     = new List<double>();
    78         var sigma_noise = (double) noiseRatio * n.StandardDeviationPop();
     78        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * n.StandardDeviationPop();
    7979        n_noise.AddRange(n.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    8080        data.Remove(n);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman63.cs

    r17678 r17805  
    2828    public override string Name {
    2929      get {
    30         return string.Format("II.11.20 n_rho*p_d**2*Ef/(3*kb*T) | {0} samples | {1}", trainingSamples,
     30        return string.Format("II.11.20 n_rho*p_d**2*Ef/(3*kb*T) | {0}",
    3131          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3232      }
     
    7474      if (noiseRatio != null) {
    7575        var Pol_noise   = new List<double>();
    76         var sigma_noise = (double) noiseRatio * Pol.StandardDeviationPop();
     76        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * Pol.StandardDeviationPop();
    7777        Pol_noise.AddRange(Pol.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    7878        data.Remove(Pol);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman64.cs

    r17678 r17805  
    2828    public override string Name {
    2929      get {
    30         return string.Format("II.11.27 n*alpha/(1-(n*alpha/3))*epsilon*Ef | {0} samples | {1}",
    31           trainingSamples, noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
     30        return string.Format("II.11.27 n*alpha/(1-(n*alpha/3))*epsilon*Ef | {0}",
     31          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3232      }
    3333    }
     
    7272      if (noiseRatio != null) {
    7373        var Pol_noise   = new List<double>();
    74         var sigma_noise = (double) noiseRatio * Pol.StandardDeviationPop();
     74        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * Pol.StandardDeviationPop();
    7575        Pol_noise.AddRange(Pol.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    7676        data.Remove(Pol);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman65.cs

    r17678 r17805  
    2828    public override string Name {
    2929      get {
    30         return string.Format("II.11.28 1+n*alpha/(1-(n*alpha/3)) | {0} samples | {1}", trainingSamples,
     30        return string.Format("II.11.28 1+n*alpha/(1-(n*alpha/3)) | {0}",
    3131          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3232      }
     
    6868      if (noiseRatio != null) {
    6969        var theta_noise = new List<double>();
    70         var sigma_noise = (double) noiseRatio * theta.StandardDeviationPop();
     70        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * theta.StandardDeviationPop();
    7171        theta_noise.AddRange(theta.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    7272        data.Remove(theta);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman66.cs

    r17678 r17805  
    2828    public override string Name {
    2929      get {
    30         return string.Format("II.13.17 1/(4*pi*epsilon*c**2)*2*I/r | {0} samples | {1}",
    31           trainingSamples, noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
     30        return string.Format("II.13.17 1/(4*pi*epsilon*c**2)*2*I/r | {0}",
     31          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3232      }
    3333    }
     
    7272      if (noiseRatio != null) {
    7373        var B_noise     = new List<double>();
    74         var sigma_noise = (double) noiseRatio * B.StandardDeviationPop();
     74        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * B.StandardDeviationPop();
    7575        B_noise.AddRange(B.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    7676        data.Remove(B);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman67.cs

    r17678 r17805  
    2828    public override string Name {
    2929      get {
    30         return string.Format("II.13.23 rho_c_0/sqrt(1-v**2/c**2) | {0} samples | {1}", trainingSamples,
     30        return string.Format("II.13.23 rho_c_0/sqrt(1-v**2/c**2) | {0}",
    3131          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3232      }
     
    7070      if (noiseRatio != null) {
    7171        var rho_c_noise = new List<double>();
    72         var sigma_noise = (double) noiseRatio * rho_c.StandardDeviationPop();
     72        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * rho_c.StandardDeviationPop();
    7373        rho_c_noise.AddRange(rho_c.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    7474        data.Remove(rho_c);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman68.cs

    r17678 r17805  
    2828    public override string Name {
    2929      get {
    30         return string.Format("II.13.34 rho_c_0*v/sqrt(1-v**2/c**2) | {0} samples | {1}",
    31           trainingSamples, noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
     30        return string.Format("II.13.34 rho_c_0*v/sqrt(1-v**2/c**2) | {0}",
     31          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3232      }
    3333    }
     
    7070      if (noiseRatio != null) {
    7171        var j_noise     = new List<double>();
    72         var sigma_noise = (double) noiseRatio * j.StandardDeviationPop();
     72        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * j.StandardDeviationPop();
    7373        j_noise.AddRange(j.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    7474        data.Remove(j);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman69.cs

    r17678 r17805  
    2828    public override string Name {
    2929      get {
    30         return string.Format("II.15.4 -mom*B*cos(theta) | {0} samples | {1}", trainingSamples,
     30        return string.Format("II.15.4 -mom*B*cos(theta) | {0}",
    3131          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3232      }
     
    7070      if (noiseRatio != null) {
    7171        var E_n_noise   = new List<double>();
    72         var sigma_noise = (double) noiseRatio * E_n.StandardDeviationPop();
     72        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * E_n.StandardDeviationPop();
    7373        E_n_noise.AddRange(E_n.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    7474        data.Remove(E_n);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman7.cs

    r17678 r17805  
    2828    public override string Name {
    2929      get {
    30         return string.Format("I.11.19 x1*y1+x2*y2+x3*y3 | {0} samples | {1}", trainingSamples,
     30        return string.Format("I.11.19 x1*y1+x2*y2+x3*y3 | {0}",
    3131          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3232      }
     
    7676      if (noiseRatio != null) {
    7777        var A_noise     = new List<double>();
    78         var sigma_noise = (double) noiseRatio * A.StandardDeviationPop();
     78        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * A.StandardDeviationPop();
    7979        A_noise.AddRange(A.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    8080        data.Remove(A);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman70.cs

    r17678 r17805  
    2828    public override string Name {
    2929      get {
    30         return string.Format("II.15.5 -p_d*Ef*cos(theta) | {0} samples | {1}", trainingSamples,
     30        return string.Format("II.15.5 -p_d*Ef*cos(theta) | {0}",
    3131          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3232      }
     
    7070      if (noiseRatio != null) {
    7171        var E_n_noise   = new List<double>();
    72         var sigma_noise = (double) noiseRatio * E_n.StandardDeviationPop();
     72        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * E_n.StandardDeviationPop();
    7373        E_n_noise.AddRange(E_n.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    7474        data.Remove(E_n);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman71.cs

    r17678 r17805  
    2828    public override string Name {
    2929      get {
    30         return string.Format("II.21.32 q/(4*pi*epsilon*r*(1-v/c)) | {0} samples | {1}", trainingSamples,
     30        return string.Format("II.21.32 q/(4*pi*epsilon*r*(1-v/c)) | {0}",
    3131          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3232      }
     
    7474      if (noiseRatio != null) {
    7575        var Volt_noise  = new List<double>();
    76         var sigma_noise = (double) noiseRatio * Volt.StandardDeviationPop();
     76        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * Volt.StandardDeviationPop();
    7777        Volt_noise.AddRange(Volt.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    7878        data.Remove(Volt);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman72.cs

    r17678 r17805  
    2828    public override string Name {
    2929      get {
    30         return string.Format("II.24.17 sqrt(omega**2/c**2-pi**2/d**2) | {0} samples | {1}",
    31           trainingSamples, noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
     30        return string.Format("II.24.17 sqrt(omega**2/c**2-pi**2/d**2) | {0}",
     31          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3232      }
    3333    }
     
    7070      if (noiseRatio != null) {
    7171        var k_noise     = new List<double>();
    72         var sigma_noise = (double) noiseRatio * k.StandardDeviationPop();
     72        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * k.StandardDeviationPop();
    7373        k_noise.AddRange(k.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    7474        data.Remove(k);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman73.cs

    r17678 r17805  
    2828    public override string Name {
    2929      get {
    30         return string.Format("II.27.16 epsilon*c*Ef**2 | {0} samples | {1}", trainingSamples,
     30        return string.Format("II.27.16 epsilon*c*Ef**2 | {0}",
    3131          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3232      }
     
    7070      if (noiseRatio != null) {
    7171        var flux_noise  = new List<double>();
    72         var sigma_noise = (double) noiseRatio * flux.StandardDeviationPop();
     72        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * flux.StandardDeviationPop();
    7373        flux_noise.AddRange(flux.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    7474        data.Remove(flux);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman74.cs

    r17678 r17805  
    2828    public override string Name {
    2929      get {
    30         return string.Format("II.27.18 epsilon*Ef**2 | {0} samples | {1}", trainingSamples,
     30        return string.Format("II.27.18 epsilon*Ef**2 | {0}",
    3131          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3232      }
     
    6868      if (noiseRatio != null) {
    6969        var E_den_noise = new List<double>();
    70         var sigma_noise = (double) noiseRatio * E_den.StandardDeviationPop();
     70        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * E_den.StandardDeviationPop();
    7171        E_den_noise.AddRange(E_den.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    7272        data.Remove(E_den);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman75.cs

    r17678 r17805  
    2828    public override string Name {
    2929      get {
    30         return string.Format("II.34.2a q*v/(2*pi*r) | {0} samples | {1}", trainingSamples,
     30        return string.Format("II.34.2a q*v/(2*pi*r) | {0}",
    3131          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3232      }
     
    7070      if (noiseRatio != null) {
    7171        var I_noise     = new List<double>();
    72         var sigma_noise = (double) noiseRatio * I.StandardDeviationPop();
     72        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * I.StandardDeviationPop();
    7373        I_noise.AddRange(I.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    7474        data.Remove(I);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman76.cs

    r17678 r17805  
    2828    public override string Name {
    2929      get {
    30         return string.Format("II.34.2 q*v*r/2 | {0} samples | {1}", trainingSamples,
     30        return string.Format("II.34.2 q*v*r/2 | {0}",
    3131          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3232      }
     
    7070      if (noiseRatio != null) {
    7171        var mom_noise   = new List<double>();
    72         var sigma_noise = (double) noiseRatio * mom.StandardDeviationPop();
     72        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * mom.StandardDeviationPop();
    7373        mom_noise.AddRange(mom.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    7474        data.Remove(mom);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman77.cs

    r17678 r17805  
    2828    public override string Name {
    2929      get {
    30         return string.Format("II.34.11 g_*q*B/(2*m) | {0} samples | {1}", trainingSamples,
     30        return string.Format("II.34.11 g_*q*B/(2*m) | {0}",
    3131          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3232      }
     
    7272      if (noiseRatio != null) {
    7373        var omega_noise = new List<double>();
    74         var sigma_noise = (double) noiseRatio * omega.StandardDeviationPop();
     74        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * omega.StandardDeviationPop();
    7575        omega_noise.AddRange(omega.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    7676        data.Remove(omega);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman78.cs

    r17678 r17805  
    2828    public override string Name {
    2929      get {
    30         return string.Format("II.34.29a q*h/(4*pi*m) | {0} samples | {1}", trainingSamples,
     30        return string.Format("II.34.29a q*h/(4*pi*m) | {0}",
    3131          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3232      }
     
    7070      if (noiseRatio != null) {
    7171        var mom_noise   = new List<double>();
    72         var sigma_noise = (double) noiseRatio * mom.StandardDeviationPop();
     72        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * mom.StandardDeviationPop();
    7373        mom_noise.AddRange(mom.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    7474        data.Remove(mom);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman79.cs

    r17678 r17805  
    2828    public override string Name {
    2929      get {
    30         return string.Format("II.34.29b g_*mom*B*Jz/h | {0} samples | {1}", trainingSamples,
     30        return string.Format("II.34.29b g_*mom*B*Jz/h | {0}",
    3131          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3232      }
     
    7474      if (noiseRatio != null) {
    7575        var E_n_noise   = new List<double>();
    76         var sigma_noise = (double) noiseRatio * E_n.StandardDeviationPop();
     76        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * E_n.StandardDeviationPop();
    7777        E_n_noise.AddRange(E_n.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    7878        data.Remove(E_n);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman8.cs

    r17678 r17805  
    2828    public override string Name {
    2929      get {
    30         return string.Format("I.12.1 mu*Nn | {0} samples | {1}", trainingSamples,
     30        return string.Format("I.12.1 mu*Nn | {0}",
    3131          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3232      }
     
    6868      if (noiseRatio != null) {
    6969        var F_noise     = new List<double>();
    70         var sigma_noise = (double) noiseRatio * F.StandardDeviationPop();
     70        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * F.StandardDeviationPop();
    7171        F_noise.AddRange(F.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    7272        data.Remove(F);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman80.cs

    r17678 r17805  
    2828    public override string Name {
    2929      get {
    30         return string.Format("II.35.18 n_0/(exp(mom*B/(kb*T))+exp(-mom*B/(kb*T))) | {0} samples | {1}",
    31           trainingSamples, noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
     30        return string.Format("II.35.18 n_0/(exp(mom*B/(kb*T))+exp(-mom*B/(kb*T))) | {0}",
     31          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3232      }
    3333    }
     
    7474      if (noiseRatio != null) {
    7575        var n_noise     = new List<double>();
    76         var sigma_noise = (double) noiseRatio * n.StandardDeviationPop();
     76        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * n.StandardDeviationPop();
    7777        n_noise.AddRange(n.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    7878        data.Remove(n);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman81.cs

    r17678 r17805  
    2828    public override string Name {
    2929      get {
    30         return string.Format("II.35.21 n_rho*mom*tanh(mom*B/(kb*T)) | {0} samples | {1}",
    31           trainingSamples, noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
     30        return string.Format("II.35.21 n_rho*mom*tanh(mom*B/(kb*T)) | {0}",
     31          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3232      }
    3333    }
     
    7474      if (noiseRatio != null) {
    7575        var M_noise     = new List<double>();
    76         var sigma_noise = (double) noiseRatio * M.StandardDeviationPop();
     76        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * M.StandardDeviationPop();
    7777        M_noise.AddRange(M.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    7878        data.Remove(M);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman82.cs

    r17678 r17805  
    2929      get {
    3030        return string.Format(
    31           "II.36.38 mom*B/(kb*T)+(mom*alpha*M)/(epsilon*c**2*kb*T) | {0} samples | {1}",
    32           trainingSamples, noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
     31          "II.36.38 mom*B/(kb*T)+(mom*alpha*M)/(epsilon*c**2*kb*T) | {0}",
     32          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3333      }
    3434    }
     
    8484      if (noiseRatio != null) {
    8585        var f_noise     = new List<double>();
    86         var sigma_noise = (double) noiseRatio * f.StandardDeviationPop();
     86        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * f.StandardDeviationPop();
    8787        f_noise.AddRange(f.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    8888        data.Remove(f);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman83.cs

    r17678 r17805  
    2828    public override string Name {
    2929      get {
    30         return string.Format("II.37.1 mom*(1+chi)*B | {0} samples | {1}", trainingSamples,
     30        return string.Format("II.37.1 mom*(1+chi)*B | {0}",
    3131          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3232      }
     
    7070      if (noiseRatio != null) {
    7171        var E_n_noise   = new List<double>();
    72         var sigma_noise = (double) noiseRatio * E_n.StandardDeviationPop();
     72        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * E_n.StandardDeviationPop();
    7373        E_n_noise.AddRange(E_n.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    7474        data.Remove(E_n);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman84.cs

    r17678 r17805  
    2828    public override string Name {
    2929      get {
    30         return string.Format("II.38.3 Y*A*x/d | {0} samples | {1}", trainingSamples,
     30        return string.Format("II.38.3 Y*A*x/d | {0}",
    3131          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3232      }
     
    7272      if (noiseRatio != null) {
    7373        var F_noise     = new List<double>();
    74         var sigma_noise = (double) noiseRatio * F.StandardDeviationPop();
     74        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * F.StandardDeviationPop();
    7575        F_noise.AddRange(F.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    7676        data.Remove(F);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman85.cs

    r17678 r17805  
    2828    public override string Name {
    2929      get {
    30         return string.Format("II.38.14 Y/(2*(1+sigma)) | {0} samples | {1}", trainingSamples,
     30        return string.Format("II.38.14 Y/(2*(1+sigma)) | {0}",
    3131          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3232      }
     
    6868      if (noiseRatio != null) {
    6969        var mu_S_noise  = new List<double>();
    70         var sigma_noise = (double) noiseRatio * mu_S.StandardDeviationPop();
     70        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * mu_S.StandardDeviationPop();
    7171        mu_S_noise.AddRange(mu_S.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    7272        data.Remove(mu_S);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman86.cs

    r17678 r17805  
    2828    public override string Name {
    2929      get {
    30         return string.Format("III.4.32 1/(exp(h*omega/(kb*T))-1) | {0} samples | {1}",
    31           trainingSamples, noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
     30        return string.Format("III.4.32 1/(exp(h*omega/(kb*T))-1) | {0}",
     31          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3232      }
    3333    }
     
    7272      if (noiseRatio != null) {
    7373        var n_noise     = new List<double>();
    74         var sigma_noise = (double) noiseRatio * n.StandardDeviationPop();
     74        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * n.StandardDeviationPop();
    7575        n_noise.AddRange(n.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    7676        data.Remove(n);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman87.cs

    r17678 r17805  
    2929      get {
    3030        return string.Format(
    31           "III.4.33 h*omega/(exp(h*omega/(kb*T))-1) | {0} samples | {1}",
    32           trainingSamples, noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
     31          "III.4.33 h*omega/(exp(h*omega/(kb*T))-1) | {0}",
     32          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3333      }
    3434    }
     
    7373      if (noiseRatio != null) {
    7474        var E_n_noise   = new List<double>();
    75         var sigma_noise = (double) noiseRatio * E_n.StandardDeviationPop();
     75        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * E_n.StandardDeviationPop();
    7676        E_n_noise.AddRange(E_n.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    7777        data.Remove(E_n);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman88.cs

    r17678 r17805  
    2828    public override string Name {
    2929      get {
    30         return string.Format("III.7.38 2*mom*B/h | {0} samples | {1}", trainingSamples,
     30        return string.Format("III.7.38 2*mom*B/h | {0}",
    3131          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3232      }
     
    7070      if (noiseRatio != null) {
    7171        var omega_noise = new List<double>();
    72         var sigma_noise = (double) noiseRatio * omega.StandardDeviationPop();
     72        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * omega.StandardDeviationPop();
    7373        omega_noise.AddRange(omega.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    7474        data.Remove(omega);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman89.cs

    r17678 r17805  
    2828    public override string Name {
    2929      get {
    30         return string.Format("III.8.54 sin(E_n*t/h)**2 | {0} samples | {1}", trainingSamples,
     30        return string.Format("III.8.54 sin(E_n*t/h)**2 | {0}",
    3131          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3232      }
     
    7070      if (noiseRatio != null) {
    7171        var prob_noise  = new List<double>();
    72         var sigma_noise = (double) noiseRatio * prob.StandardDeviationPop();
     72        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * prob.StandardDeviationPop();
    7373        prob_noise.AddRange(prob.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    7474        data.Remove(prob);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman9.cs

    r17678 r17805  
    2828    public override string Name {
    2929      get {
    30         return string.Format("I.12.2 q1*q2/(4*pi*epsilon*r**2) | {0} samples | {1}", trainingSamples,
     30        return string.Format("I.12.2 q1*q2/(4*pi*epsilon*r**2) | {0}",
    3131          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3232      }
     
    7272      if (noiseRatio != null) {
    7373        var F_noise     = new List<double>();
    74         var sigma_noise = (double) noiseRatio * F.StandardDeviationPop();
     74        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * F.StandardDeviationPop();
    7575        F_noise.AddRange(F.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    7676        data.Remove(F);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman90.cs

    r17678 r17805  
    2929      get {
    3030        return string.Format(
    31           "III.9.52 (p_d*Ef*t/h*sin((omega-omega_0)*t/2)**2/((omega-omega_0)*t/2)**2 | {0} samples | {1}",
    32           trainingSamples, noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
     31          "III.9.52 (p_d*Ef*t/h*sin((omega-omega_0)*t/2)**2/((omega-omega_0)*t/2)**2 | {0}",
     32          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3333      }
    3434    }
     
    8181      if (noiseRatio != null) {
    8282        var prob_noise  = new List<double>();
    83         var sigma_noise = (double) noiseRatio * prob.StandardDeviationPop();
     83        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * prob.StandardDeviationPop();
    8484        prob_noise.AddRange(prob.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    8585        data.Remove(prob);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman91.cs

    r17678 r17805  
    2828    public override string Name {
    2929      get {
    30         return string.Format("III.10.19 mom*sqrt(Bx**2+By**2+Bz**2) | {0} samples | {1}",
    31           trainingSamples, noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
     30        return string.Format("III.10.19 mom*sqrt(Bx**2+By**2+Bz**2) | {0}",
     31          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3232      }
    3333    }
     
    7272      if (noiseRatio != null) {
    7373        var E_n_noise   = new List<double>();
    74         var sigma_noise = (double) noiseRatio * E_n.StandardDeviationPop();
     74        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * E_n.StandardDeviationPop();
    7575        E_n_noise.AddRange(E_n.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    7676        data.Remove(E_n);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman92.cs

    r17678 r17805  
    2828    public override string Name {
    2929      get {
    30         return string.Format("III.12.43 n*h | {0} samples | {1}", trainingSamples,
     30        return string.Format("III.12.43 n*h | {0}",
    3131          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3232      }
     
    6464      if (noiseRatio != null) {
    6565        var L_noise     = new List<double>();
    66         var sigma_noise = (double) noiseRatio * L.StandardDeviationPop();
     66        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * L.StandardDeviationPop();
    6767        L_noise.AddRange(L.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    6868        data.Remove(L);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman93.cs

    r17678 r17805  
    2828    public override string Name {
    2929      get {
    30         return string.Format("III.13.18 2*E_n*d**2*k/h | {0} samples | {1}", trainingSamples,
     30        return string.Format("III.13.18 2*E_n*d**2*k/h | {0}",
    3131          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3232      }
     
    7272      if (noiseRatio != null) {
    7373        var v_noise     = new List<double>();
    74         var sigma_noise = (double) noiseRatio * v.StandardDeviationPop();
     74        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * v.StandardDeviationPop();
    7575        v_noise.AddRange(v.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    7676        data.Remove(v);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman94.cs

    r17678 r17805  
    2828    public override string Name {
    2929      get {
    30         return string.Format("III.14.14 I_0*(exp(q*Volt/(kb*T))-1) | {0} samples | {1}",
    31           trainingSamples, noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
     30        return string.Format("III.14.14 I_0*(exp(q*Volt/(kb*T))-1) | {0}",
     31          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3232      }
    3333    }
     
    7474      if (noiseRatio != null) {
    7575        var I_noise     = new List<double>();
    76         var sigma_noise = (double) noiseRatio * I.StandardDeviationPop();
     76        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * I.StandardDeviationPop();
    7777        I_noise.AddRange(I.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    7878        data.Remove(I);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman95.cs

    r17678 r17805  
    2828    public override string Name {
    2929      get {
    30         return string.Format("III.15.12 2*U*(1-cos(k*d)) | {0} samples | {1}", trainingSamples,
     30        return string.Format("III.15.12 2*U*(1-cos(k*d)) | {0}",
    3131          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3232      }
     
    7070      if (noiseRatio != null) {
    7171        var E_n_noise   = new List<double>();
    72         var sigma_noise = (double) noiseRatio * E_n.StandardDeviationPop();
     72        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * E_n.StandardDeviationPop();
    7373        E_n_noise.AddRange(E_n.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    7474        data.Remove(E_n);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman96.cs

    r17678 r17805  
    2828    public override string Name {
    2929      get {
    30         return string.Format("III.15.14 h**2/(2*E_n*d**2) | {0} samples | {1}",
    31           trainingSamples, noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
     30        return string.Format("III.15.14 h**2/(2*E_n*d**2) | {0}",
     31          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3232      }
    3333    }
     
    7070      if (noiseRatio != null) {
    7171        var m_noise     = new List<double>();
    72         var sigma_noise = (double) noiseRatio * m.StandardDeviationPop();
     72        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * m.StandardDeviationPop();
    7373        m_noise.AddRange(m.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    7474        data.Remove(m);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman97.cs

    r17678 r17805  
    2828    public override string Name {
    2929      get {
    30         return string.Format("III.15.27 2*pi*alpha/(n*d) | {0} samples | {1}", trainingSamples,
     30        return string.Format("III.15.27 2*pi*alpha/(n*d) | {0}",
    3131          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3232      }
     
    7070      if (noiseRatio != null) {
    7171        var k_noise     = new List<double>();
    72         var sigma_noise = (double) noiseRatio * k.StandardDeviationPop();
     72        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * k.StandardDeviationPop();
    7373        k_noise.AddRange(k.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    7474        data.Remove(k);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman98.cs

    r17678 r17805  
    2828    public override string Name {
    2929      get {
    30         return string.Format("III.17.37 beta*(1+alpha*cos(theta)) | {0} samples | {1}", trainingSamples,
     30        return string.Format("III.17.37 beta*(1+alpha*cos(theta)) | {0}",
    3131          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3232      }
     
    7070      if (noiseRatio != null) {
    7171        var f_noise     = new List<double>();
    72         var sigma_noise = (double) noiseRatio * f.StandardDeviationPop();
     72        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * f.StandardDeviationPop();
    7373        f_noise.AddRange(f.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    7474        data.Remove(f);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman99.cs

    r17678 r17805  
    2929      get {
    3030        return string.Format(
    31           "III.19.51 -m*q**4/(2*(4*pi*epsilon)**2*h**2)*(1/n**2) | {0} samples | {1}",
    32           trainingSamples, noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
     31          "III.19.51 -m*q**4/(2*(4*pi*epsilon)**2*h**2)*(1/n**2) | {0}",
     32          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3333      }
    3434    }
     
    7676      if (noiseRatio != null) {
    7777        var E_n_noise   = new List<double>();
    78         var sigma_noise = (double) noiseRatio * E_n.StandardDeviationPop();
     78        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * E_n.StandardDeviationPop();
    7979        E_n_noise.AddRange(E_n.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    8080        data.Remove(E_n);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/FeynmanBonus1.cs

    r17678 r17805  
    2929      get {
    3030        return string.Format(
    31           "Rutherford scattering: (Z_1*Z_2*alpha*hbar*c/(4*E_n*sin(theta/2)**2))**2 | {0} samples | {1}",
    32           trainingSamples, noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
     31          "Rutherford scattering: (Z_1*Z_2*alpha*hbar*c/(4*E_n*sin(theta/2)**2))**2 | {0}",
     32          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3333      }
    3434    }
     
    8282      if (noiseRatio != null) {
    8383        var A_noise     = new List<double>();
    84         var sigma_noise = (double) noiseRatio * A.StandardDeviationPop();
     84        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * A.StandardDeviationPop();
    8585        A_noise.AddRange(A.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    8686        data.Remove(A);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/FeynmanBonus10.cs

    r17678 r17805  
    2828    public override string Name {
    2929      get {
    30         return string.Format("Goldstein 3.74: 2*pi*d**(3/2)/sqrt(G*(m1+m2)) | {0} samples | {1}",
    31           trainingSamples, noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
     30        return string.Format("Goldstein 3.74: 2*pi*d**(3/2)/sqrt(G*(m1+m2)) | {0}",
     31          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3232      }
    3333    }
     
    7272      if (noiseRatio != null) {
    7373        var t_noise     = new List<double>();
    74         var sigma_noise = (double) noiseRatio * t.StandardDeviationPop();
     74        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * t.StandardDeviationPop();
    7575        t_noise.AddRange(t.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    7676        data.Remove(t);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/FeynmanBonus11.cs

    r17678 r17805  
    2929      get {
    3030        return string.Format(
    31           "Goldstein 3.99: sqrt(1+2*epsilon**2*E_n*L**2/(m*(Z_1*Z_2*q**2)**2)) | {0} samples | {1}",
    32           trainingSamples, noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
     31          "Goldstein 3.99: sqrt(1+2*epsilon**2*E_n*L**2/(m*(Z_1*Z_2*q**2)**2)) | {0}",
     32          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3333      }
    3434    }
     
    8484      if (noiseRatio != null) {
    8585        var alpha_noise = new List<double>();
    86         var sigma_noise = (double) noiseRatio * alpha.StandardDeviationPop();
     86        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * alpha.StandardDeviationPop();
    8787        alpha_noise.AddRange(alpha.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    8888        data.Remove(alpha);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/FeynmanBonus12.cs

    r17678 r17805  
    2929      get {
    3030        return string.Format(
    31           "Goldstein 8.56: sqrt((p-q*A_vec)**2*c**2+m**2*c**4)+q*Volt | {0} samples | {1}",
    32           trainingSamples, noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
     31          "Goldstein 8.56: sqrt((p-q*A_vec)**2*c**2+m**2*c**4)+q*Volt | {0}",
     32          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3333      }
    3434    }
     
    7878      if (noiseRatio != null) {
    7979        var E_n_noise   = new List<double>();
    80         var sigma_noise = (double) noiseRatio * E_n.StandardDeviationPop();
     80        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * E_n.StandardDeviationPop();
    8181        E_n_noise.AddRange(E_n.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    8282        data.Remove(E_n);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/FeynmanBonus13.cs

    r17678 r17805  
    2929      get {
    3030        return string.Format(
    31           "Goldstein 12.80: 1/(2*m)*(p**2+m**2*omega**2*x**2*(1+alpha*x/y)) | {0} samples | {1}",
    32           trainingSamples, noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
     31          "Goldstein 12.80: 1/(2*m)*(p**2+m**2*omega**2*x**2*(1+alpha*x/y)) | {0}",
     32          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3333      }
    3434    }
     
    7979      if (noiseRatio != null) {
    8080        var E_n_noise   = new List<double>();
    81         var sigma_noise = (double) noiseRatio * E_n.StandardDeviationPop();
     81        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * E_n.StandardDeviationPop();
    8282        E_n_noise.AddRange(E_n.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    8383        data.Remove(E_n);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/FeynmanBonus14.cs

    r17678 r17805  
    2929      get {
    3030        return string.Format(
    31           "Jackson 2.11: q/(4*pi*epsilon*y**2)*(4*pi*epsilon*Volt*d-q*d*y**3/(y**2-d**2)**2) | {0} samples | {1}",
    32           trainingSamples, noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
     31          "Jackson 2.11: q/(4*pi*epsilon*y**2)*(4*pi*epsilon*Volt*d-q*d*y**3/(y**2-d**2)**2) | {0}",
     32          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3333      }
    3434    }
     
    7777      if (noiseRatio != null) {
    7878        var F_noise     = new List<double>();
    79         var sigma_noise = (double) noiseRatio * F.StandardDeviationPop();
     79        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * F.StandardDeviationPop();
    8080        F_noise.AddRange(F.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    8181        data.Remove(F);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/FeynmanBonus15.cs

    r17678 r17805  
    2929      get {
    3030        return string.Format(
    31           "Jackson 3.45: q/sqrt(r**2+d**2-2*r*d*cos(alpha)) | {0} samples | {1}",
    32           trainingSamples, noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
     31          "Jackson 3.45: q/sqrt(r**2+d**2-2*r*d*cos(alpha)) | {0}",
     32          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3333      }
    3434    }
     
    7474      if (noiseRatio != null) {
    7575        var Volt_noise  = new List<double>();
    76         var sigma_noise = (double) noiseRatio * Volt.StandardDeviationPop();
     76        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * Volt.StandardDeviationPop();
    7777        Volt_noise.AddRange(Volt.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    7878        data.Remove(Volt);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/FeynmanBonus16.cs

    r17678 r17805  
    2929      get {
    3030        return string.Format(
    31           "Jackson 4.60: Ef*cos(theta)*((alpha-1)/(alpha+2)*d**3/r**2-r) | {0} samples | {1}",
    32           trainingSamples, noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
     31          "Jackson 4.60: Ef*cos(theta)*((alpha-1)/(alpha+2)*d**3/r**2-r) | {0}",
     32          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3333      }
    3434    }
     
    7676      if (noiseRatio != null) {
    7777        var Volt_noise  = new List<double>();
    78         var sigma_noise = (double) noiseRatio * Volt.StandardDeviationPop();
     78        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * Volt.StandardDeviationPop();
    7979        Volt_noise.AddRange(Volt.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    8080        data.Remove(Volt);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/FeynmanBonus17.cs

    r17678 r17805  
    2929      get {
    3030        return string.Format(
    31           "Jackson 11.38: sqrt(1-v**2/c**2)*omega/(1+v/c*cos(theta)) | {0} samples | {1}",
    32           trainingSamples, noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
     31          "Jackson 11.38: sqrt(1-v**2/c**2)*omega/(1+v/c*cos(theta)) | {0}",
     32          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3333      }
    3434    }
     
    7474      if (noiseRatio != null) {
    7575        var omega_0_noise = new List<double>();
    76         var sigma_noise   = (double) noiseRatio * omega_0.StandardDeviationPop();
     76        var sigma_noise   = (double) Math.Sqrt(noiseRatio.Value) * omega_0.StandardDeviationPop();
    7777        omega_0_noise.AddRange(omega_0.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    7878        data.Remove(omega_0);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/FeynmanBonus18.cs

    r17678 r17805  
    2828    public override string Name {
    2929      get {
    30         return string.Format("Weinberg 15.2.1: 3/(8*pi*G)*(c**2*k_f/r**2+H_G**2) | {0} samples | {1}",
    31           trainingSamples, noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
     30        return string.Format("Weinberg 15.2.1: 3/(8*pi*G)*(c**2*k_f/r**2+H_G**2) | {0}",
     31          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3232      }
    3333    }
     
    7474      if (noiseRatio != null) {
    7575        var rho_0_noise = new List<double>();
    76         var sigma_noise = (double) noiseRatio * rho_0.StandardDeviationPop();
     76        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * rho_0.StandardDeviationPop();
    7777        rho_0_noise.AddRange(rho_0.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    7878        data.Remove(rho_0);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/FeynmanBonus19.cs

    r17678 r17805  
    2929      get {
    3030        return string.Format(
    31           "Weinberg 15.2.2: -1/(8*pi*G)*(c**4*k_f/r**2 + c**2*H_G**2*(1-2*alpha)) | {0} samples | {1}",
    32           trainingSamples, noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
     31          "Weinberg 15.2.2: -1/(8*pi*G)*(c**4*k_f/r**2 + c**2*H_G**2*(1-2*alpha)) | {0}",
     32          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3333      }
    3434    }
     
    7878      if (noiseRatio != null) {
    7979        var pr_noise    = new List<double>();
    80         var sigma_noise = (double) noiseRatio * pr.StandardDeviationPop();
     80        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * pr.StandardDeviationPop();
    8181        pr_noise.AddRange(pr.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    8282        data.Remove(pr);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/FeynmanBonus2.cs

    r17678 r17805  
    2828    public override string Name {
    2929      get {
    30         return string.Format("Friedman Equation: sqrt(8*pi*G*rho/3-alpha*c**2/d**2) | {0} samples | {1}",
    31           trainingSamples, noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
     30        return string.Format("Friedman Equation: sqrt(8*pi*G*rho/3-alpha*c**2/d**2) | {0}",
     31          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3232      }
    3333    }
     
    7474      if (noiseRatio != null) {
    7575        var H_G_noise   = new List<double>();
    76         var sigma_noise = (double) noiseRatio * H_G.StandardDeviationPop();
     76        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * H_G.StandardDeviationPop();
    7777        H_G_noise.AddRange(H_G.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    7878        data.Remove(H_G);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/FeynmanBonus20.cs

    r17678 r17805  
    2929      get {
    3030        return string.Format(
    31           "Schwarz 13.132 (Klein-Nishina): pi*alpha**2*h**2/(m**2*c**2)*(omega_0/omega)**2*(omega_0/omega+omega/omega_0-sin(beta)**2) | {0} samples | {1}",
    32           trainingSamples, noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
     31          "Schwarz 13.132 (Klein-Nishina): pi*alpha**2*h**2/(m**2*c**2)*(omega_0/omega)**2*(omega_0/omega+omega/omega_0-sin(beta)**2) | {0}",
     32          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3333      }
    3434    }
     
    8383      if (noiseRatio != null) {
    8484        var A_noise     = new List<double>();
    85         var sigma_noise = (double) noiseRatio * A.StandardDeviationPop();
     85        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * A.StandardDeviationPop();
    8686        A_noise.AddRange(A.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    8787        data.Remove(A);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/FeynmanBonus3.cs

    r17678 r17805  
    2929      get {
    3030        return string.Format(
    31           "Compton Scattering: E_n/(1+E_n/(m*c**2)*(1-cos(theta))) | {0} samples | {1}", trainingSamples,
     31          "Compton Scattering: E_n/(1+E_n/(m*c**2)*(1-cos(theta))) | {0}",
    3232          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3333      }
     
    7373      if (noiseRatio != null) {
    7474        var K_noise     = new List<double>();
    75         var sigma_noise = (double) noiseRatio * K.StandardDeviationPop();
     75        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * K.StandardDeviationPop();
    7676        K_noise.AddRange(K.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    7777        data.Remove(K);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/FeynmanBonus4.cs

    r17678 r17805  
    2929      get {
    3030        return string.Format(
    31           "Radiated gravitational wave power: -32/5*G**4/c**5*(m1*m2)**2*(m1+m2)/r**5 | {0} samples | {1}",
    32           trainingSamples, noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
     31          "Radiated gravitational wave power: -32/5*G**4/c**5*(m1*m2)**2*(m1+m2)/r**5 | {0}",
     32          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3333      }
    3434    }
     
    7676      if (noiseRatio != null) {
    7777        var Pwr_noise   = new List<double>();
    78         var sigma_noise = (double) noiseRatio * Pwr.StandardDeviationPop();
     78        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * Pwr.StandardDeviationPop();
    7979        Pwr_noise.AddRange(Pwr.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    8080        data.Remove(Pwr);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/FeynmanBonus5.cs

    r17678 r17805  
    2929      get {
    3030        return string.Format(
    31           "Relativistic aberation: arccos((cos(theta2)-v/c)/(1-v/c*cos(theta2))) | {0} samples | {1}",
    32           trainingSamples, noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
     31          "Relativistic aberation: arccos((cos(theta2)-v/c)/(1-v/c*cos(theta2))) | {0}",
     32          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3333      }
    3434    }
     
    7171      if (noiseRatio != null) {
    7272        var theta1_noise = new List<double>();
    73         var sigma_noise  = (double) noiseRatio * theta1.StandardDeviationPop();
     73        var sigma_noise  = (double) Math.Sqrt(noiseRatio.Value) * theta1.StandardDeviationPop();
    7474        theta1_noise.AddRange(theta1.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    7575        data.Remove(theta1);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/FeynmanBonus6.cs

    r17678 r17805  
    2929      get {
    3030        return string.Format(
    31           "N-slit diffraction: I_0*(sin(alpha/2)*sin(n*delta/2)/(alpha/2*sin(delta/2)))**2 | {0} samples | {1}",
    32           trainingSamples, noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
     31          "N-slit diffraction: I_0*(sin(alpha/2)*sin(n*delta/2)/(alpha/2*sin(delta/2)))**2 | {0}",
     32          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3333      }
    3434    }
     
    7575      if (noiseRatio != null) {
    7676        var I_noise     = new List<double>();
    77         var sigma_noise = (double) noiseRatio * I.StandardDeviationPop();
     77        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * I.StandardDeviationPop();
    7878        I_noise.AddRange(I.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    7979        data.Remove(I);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/FeynmanBonus7.cs

    r17678 r17805  
    2828    public override string Name {
    2929      get {
    30         return string.Format("Goldstein 3.16: sqrt(2/m*(E_n-U-L**2/(2*m*r**2))) | {0} samples | {1}",
    31           trainingSamples, noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
     30        return string.Format("Goldstein 3.16: sqrt(2/m*(E_n-U-L**2/(2*m*r**2))) | {0}",
     31          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3232      }
    3333    }
     
    7474      if (noiseRatio != null) {
    7575        var v_noise     = new List<double>();
    76         var sigma_noise = (double) noiseRatio * v.StandardDeviationPop();
     76        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * v.StandardDeviationPop();
    7777        v_noise.AddRange(v.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    7878        data.Remove(v);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/FeynmanBonus8.cs

    r17678 r17805  
    2929      get {
    3030        return string.Format(
    31           "Goldstein 3.55: m*k_G/L**2*(1+sqrt(1+2*E_n*L**2/(m*k_G**2))*cos(theta1-theta2)) | {0} samples | {1}",
    32           trainingSamples, noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
     31          "Goldstein 3.55: m*k_G/L**2*(1+sqrt(1+2*E_n*L**2/(m*k_G**2))*cos(theta1-theta2)) | {0}",
     32          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3333      }
    3434    }
     
    8181      if (noiseRatio != null) {
    8282        var k_noise     = new List<double>();
    83         var sigma_noise = (double) noiseRatio * k.StandardDeviationPop();
     83        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * k.StandardDeviationPop();
    8484        k_noise.AddRange(k.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    8585        data.Remove(k);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/FeynmanBonus9.cs

    r17678 r17805  
    2929      get {
    3030        return string.Format(
    31           "Goldstein 3.64: d*(1-alpha**2)/(1+alpha*cos(theta1-theta2)) | {0} samples | {1}",
    32           trainingSamples, noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
     31          "Goldstein 3.64: d*(1-alpha**2)/(1+alpha*cos(theta1-theta2)) | {0}",
     32          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
    3333      }
    3434    }
     
    7373      if (noiseRatio != null) {
    7474        var r_noise     = new List<double>();
    75         var sigma_noise = (double) noiseRatio * r.StandardDeviationPop();
     75        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * r.StandardDeviationPop();
    7676        r_noise.AddRange(r.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
    7777        data.Remove(r);
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/FeynmanLargeInstanceProvider.cs

    r17677 r17805  
    3535
    3636
    37       var noiseRatio = new double?[] { null, 1, 10E-2, 10E-4 };
     37      var noiseRatio = new double?[] { null, 0.1, 0.3, 1 };
    3838
    3939      #region types
     
    163163
    164164
    165       foreach (var n in noiseRatio) {
    166         foreach (var type in descriptorTypes) {
     165      foreach (var type in descriptorTypes) {
     166        foreach (var n in noiseRatio) {
    167167          descriptorList.Add((IDataDescriptor)Activator.CreateInstance(type, rand.Next(), 10000, 10000, n));
    168168        }
  • trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/FeynmanSmallInstanceProvider.cs

    r17677 r17805  
    3535
    3636
    37       var noiseRatio = new double?[] { null, 1, 10E-2, 10E-4 };
     37      var noiseRatio = new double?[] { null, 0.1, 0.3, 1 };
    3838
    3939      #region types
     
    163163
    164164
    165       foreach (var n in noiseRatio) {
    166         foreach (var type in descriptorTypes) {
     165      foreach (var type in descriptorTypes) {
     166        foreach (var n in noiseRatio) {
    167167          descriptorList.Add((IDataDescriptor)Activator.CreateInstance(type, rand.Next(), 100, 100, n));
    168168        }
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