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Changeset 17966 for trunk/HeuristicLab.Problems.Instances.DataAnalysis

Timestamp:

04/28/21 11:31:24 (3 years ago)

Author:

mkommend

Message:

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

Location:

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

Files:

: 120 edited

Feynman1.cs (modified) (1 diff)
Feynman10.cs (modified) (1 diff)
Feynman100.cs (modified) (1 diff)
Feynman11.cs (modified) (1 diff)
Feynman12.cs (modified) (1 diff)
Feynman13.cs (modified) (1 diff)
Feynman14.cs (modified) (1 diff)
Feynman15.cs (modified) (1 diff)
Feynman16.cs (modified) (1 diff)
Feynman17.cs (modified) (5 diffs)
Feynman18.cs (modified) (1 diff)
Feynman19.cs (modified) (1 diff)
Feynman2.cs (modified) (1 diff)
Feynman20.cs (modified) (1 diff)
Feynman21.cs (modified) (1 diff)
Feynman22.cs (modified) (1 diff)
Feynman23.cs (modified) (1 diff)
Feynman24.cs (modified) (1 diff)
Feynman25.cs (modified) (1 diff)
Feynman26.cs (modified) (1 diff)
Feynman27.cs (modified) (1 diff)
Feynman28.cs (modified) (1 diff)
Feynman29.cs (modified) (1 diff)
Feynman3.cs (modified) (1 diff)
Feynman30.cs (modified) (1 diff)
Feynman31.cs (modified) (1 diff)
Feynman32.cs (modified) (1 diff)
Feynman33.cs (modified) (1 diff)
Feynman34.cs (modified) (1 diff)
Feynman35.cs (modified) (1 diff)
Feynman36.cs (modified) (1 diff)
Feynman37.cs (modified) (1 diff)
Feynman38.cs (modified) (1 diff)
Feynman39.cs (modified) (1 diff)
Feynman4.cs (modified) (1 diff)
Feynman40.cs (modified) (1 diff)
Feynman41.cs (modified) (1 diff)
Feynman42.cs (modified) (1 diff)
Feynman43.cs (modified) (1 diff)
Feynman44.cs (modified) (1 diff)
Feynman45.cs (modified) (1 diff)
Feynman46.cs (modified) (1 diff)
Feynman47.cs (modified) (1 diff)
Feynman48.cs (modified) (1 diff)
Feynman49.cs (modified) (1 diff)
Feynman5.cs (modified) (1 diff)
Feynman50.cs (modified) (1 diff)
Feynman51.cs (modified) (1 diff)
Feynman52.cs (modified) (1 diff)
Feynman53.cs (modified) (1 diff)
Feynman54.cs (modified) (1 diff)
Feynman55.cs (modified) (1 diff)
Feynman56.cs (modified) (1 diff)
Feynman57.cs (modified) (1 diff)
Feynman58.cs (modified) (1 diff)
Feynman59.cs (modified) (1 diff)
Feynman6.cs (modified) (1 diff)
Feynman60.cs (modified) (1 diff)
Feynman61.cs (modified) (1 diff)
Feynman62.cs (modified) (1 diff)
Feynman63.cs (modified) (1 diff)
Feynman64.cs (modified) (1 diff)
Feynman65.cs (modified) (1 diff)
Feynman66.cs (modified) (1 diff)
Feynman67.cs (modified) (1 diff)
Feynman68.cs (modified) (1 diff)
Feynman69.cs (modified) (1 diff)
Feynman7.cs (modified) (1 diff)
Feynman70.cs (modified) (1 diff)
Feynman71.cs (modified) (1 diff)
Feynman72.cs (modified) (1 diff)
Feynman73.cs (modified) (1 diff)
Feynman74.cs (modified) (1 diff)
Feynman75.cs (modified) (1 diff)
Feynman76.cs (modified) (1 diff)
Feynman77.cs (modified) (1 diff)
Feynman78.cs (modified) (1 diff)
Feynman79.cs (modified) (1 diff)
Feynman8.cs (modified) (1 diff)
Feynman80.cs (modified) (1 diff)
Feynman81.cs (modified) (1 diff)
Feynman82.cs (modified) (1 diff)
Feynman83.cs (modified) (1 diff)
Feynman84.cs (modified) (1 diff)
Feynman85.cs (modified) (1 diff)
Feynman86.cs (modified) (1 diff)
Feynman87.cs (modified) (1 diff)
Feynman88.cs (modified) (1 diff)
Feynman89.cs (modified) (1 diff)
Feynman9.cs (modified) (1 diff)
Feynman90.cs (modified) (1 diff)
Feynman91.cs (modified) (1 diff)
Feynman92.cs (modified) (1 diff)
Feynman93.cs (modified) (1 diff)
Feynman94.cs (modified) (1 diff)
Feynman95.cs (modified) (1 diff)
Feynman96.cs (modified) (1 diff)
Feynman97.cs (modified) (1 diff)
Feynman98.cs (modified) (1 diff)
Feynman99.cs (modified) (1 diff)
FeynmanBonus1.cs (modified) (1 diff)
FeynmanBonus10.cs (modified) (1 diff)
FeynmanBonus11.cs (modified) (1 diff)
FeynmanBonus12.cs (modified) (1 diff)
FeynmanBonus13.cs (modified) (1 diff)
FeynmanBonus14.cs (modified) (1 diff)
FeynmanBonus15.cs (modified) (1 diff)
FeynmanBonus16.cs (modified) (1 diff)
FeynmanBonus17.cs (modified) (1 diff)
FeynmanBonus18.cs (modified) (1 diff)
FeynmanBonus19.cs (modified) (1 diff)
FeynmanBonus2.cs (modified) (1 diff)
FeynmanBonus20.cs (modified) (1 diff)
FeynmanBonus3.cs (modified) (1 diff)
FeynmanBonus4.cs (modified) (1 diff)
FeynmanBonus5.cs (modified) (1 diff)
FeynmanBonus6.cs (modified) (1 diff)
FeynmanBonus7.cs (modified) (1 diff)
FeynmanBonus8.cs (modified) (1 diff)
FeynmanBonus9.cs (modified) (1 diff)

Legend:

: Unmodified
: Added
: Removed

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman1.cs

r17805	r17966
63	63	var f_noise = new List<double>();
64	64	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * f.StandardDeviationPop();
65		f_noise.AddRange(f.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	65	f_noise.AddRange(f.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
66	66	data.Remove(f);
67	67	data.Add(f_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman10.cs

r17805	r17966
71	71	var Ef_noise = new List<double>();
72	72	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * Ef.StandardDeviationPop();
73		Ef_noise.AddRange(Ef.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	73	Ef_noise.AddRange(Ef.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
74	74	data.Remove(Ef);
75	75	data.Add(Ef_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman100.cs

r17805	r17966
73	73	var j_noise = new List<double>();
74	74	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * j.StandardDeviationPop();
75		j_noise.AddRange(j.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	75	j_noise.AddRange(j.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
76	76	data.Remove(j);
77	77	data.Add(j_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman11.cs

r17805	r17966
69	69	var F_noise = new List<double>();
70	70	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * F.StandardDeviationPop();
71		F_noise.AddRange(F.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	71	F_noise.AddRange(F.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
72	72	data.Remove(F);
73	73	data.Add(F_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman12.cs

r17805	r17966
75	75	var F_noise = new List<double>();
76	76	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * F.StandardDeviationPop();
77		F_noise.AddRange(F.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	77	F_noise.AddRange(F.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
78	78	data.Remove(F);
79	79	data.Add(F_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman13.cs

r17805	r17966
73	73	var K_noise = new List<double>();
74	74	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * K.StandardDeviationPop();
75		K_noise.AddRange(K.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	75	K_noise.AddRange(K.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
76	76	data.Remove(K);
77	77	data.Add(K_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman14.cs

r17805	r17966
75	75	var U_noise = new List<double>();
76	76	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * U.StandardDeviationPop();
77		U_noise.AddRange(U.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	77	U_noise.AddRange(U.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
78	78	data.Remove(U);
79	79	data.Add(U_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman15.cs

r17805	r17966
71	71	var U_noise = new List<double>();
72	72	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * U.StandardDeviationPop();
73		U_noise.AddRange(U.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	73	U_noise.AddRange(U.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
74	74	data.Remove(U);
75	75	data.Add(U_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman16.cs

r17805	r17966
69	69	var U_noise = new List<double>();
70	70	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * U.StandardDeviationPop();
71		U_noise.AddRange(U.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	71	U_noise.AddRange(U.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
72	72	data.Remove(U);
73	73	data.Add(U_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman17.cs

-                      r17805
+                      r17966
     private readonly int trainingSamples;
     public Feynman17() : this((int) DateTime.Now.Ticks, 10000, 10000, null) { }
+    public Feynman17() : this((int)DateTime.Now.Ticks, 10000, 10000, null) { }
     public Feynman17(int seed) {
       Seed            = seed;
+      Seed = seed;
       trainingSamples = 10000;
       testSamples     = 10000;
       noiseRatio      = null;
+      testSamples = 10000;
+      noiseRatio = null;
+    }
     public Feynman17(int seed, int trainingSamples, int testSamples, double? noiseRatio) {
       Seed                 = seed;
+      Seed = seed;
       this.trainingSamples = trainingSamples;
       this.testSamples     = testSamples;
       this.noiseRatio      = noiseRatio;
+      this.testSamples = testSamples;
+      this.noiseRatio = noiseRatio;
+    }
 …
       get {
         return string.Format("I.15.3x (x-u*t)/sqrt(1-u**2/c**2) | {0}",
           noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
+          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}", noiseRatio));
+      }
+    }
 …
     protected override string[] VariableNames {
       get { return new[] {"x", "u", "c", "t", noiseRatio == null ? "x1" : "x1_noise"}; }
+      get { return new[] { "x", "u", "c", "t", noiseRatio == null ? "x1" : "x1_noise" }; }
+    }
     protected override string[] AllowedInputVariables { get { return new[] {"x", "u", "c", "t"}; } }
+    protected override string[] AllowedInputVariables { get { return new[] { "x", "u", "c", "t" }; } }
     public int Seed { get; private set; }
 …
     protected override List<List<double>> GenerateValues() {
       var rand = new MersenneTwister((uint) Seed);
+      var rand = new MersenneTwister((uint)Seed);
       var data = new List<List<double>>();
       var x    = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 5, 10).ToList();
       var u    = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 2).ToList();
       var c    = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 3, 20).ToList();
       var t    = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 2).ToList();
+      var x = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 5, 10).ToList();
+      var u = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 2).ToList();
+      var c = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 3, 20).ToList();
+      var t = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 2).ToList();
       var x1 = new List<double>();
 …
       if (noiseRatio != null) {
         var x1_noise    = new List<double>();
         var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * x1.StandardDeviationPop();
         x1_noise.AddRange(x1.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
+        var x1_noise = new List<double>();
+        var sigma_noise = (double)Math.Sqrt(noiseRatio.Value) * x1.StandardDeviationPop();
+        x1_noise.AddRange(x1.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
         data.Remove(x1);
         data.Add(x1_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman18.cs

r17805	r17966
73	73	var t1_noise = new List<double>();
74	74	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * t1.StandardDeviationPop();
75		t1_noise.AddRange(t1.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	75	t1_noise.AddRange(t1.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
76	76	data.Remove(t1);
77	77	data.Add(t1_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman19.cs

r17805	r17966
71	71	var p_noise = new List<double>();
72	72	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * p.StandardDeviationPop();
73		p_noise.AddRange(p.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	73	p_noise.AddRange(p.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
74	74	data.Remove(p);
75	75	data.Add(p_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman2.cs

r17805	r17966
69	69	var f_noise = new List<double>();
70	70	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * f.StandardDeviationPop();
71		f_noise.AddRange(f.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	71	f_noise.AddRange(f.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
72	72	data.Remove(f);
73	73	data.Add(f_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman20.cs

r17805	r17966
71	71	var v1_noise = new List<double>();
72	72	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * v1.StandardDeviationPop();
73		v1_noise.AddRange(v1.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	73	v1_noise.AddRange(v1.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
74	74	data.Remove(v1);
75	75	data.Add(v1_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman21.cs

r17805	r17966
73	73	var r_noise = new List<double>();
74	74	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * r.StandardDeviationPop();
75		r_noise.AddRange(r.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	75	r_noise.AddRange(r.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
76	76	data.Remove(r);
77	77	data.Add(r_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman22.cs

r17805	r17966
71	71	var tau_noise = new List<double>();
72	72	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * tau.StandardDeviationPop();
73		tau_noise.AddRange(tau.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	73	tau_noise.AddRange(tau.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
74	74	data.Remove(tau);
75	75	data.Add(tau_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman23.cs

r17805	r17966
73	73	var L_noise = new List<double>();
74	74	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * L.StandardDeviationPop();
75		L_noise.AddRange(L.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	75	L_noise.AddRange(L.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
76	76	data.Remove(L);
77	77	data.Add(L_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman24.cs

r17805	r17966
73	73	var E_n_noise = new List<double>();
74	74	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * E_n.StandardDeviationPop();
75		E_n_noise.AddRange(E_n.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	75	E_n_noise.AddRange(E_n.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
76	76	data.Remove(E_n);
77	77	data.Add(E_n_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman25.cs

r17805	r17966
69	69	var Volt_noise = new List<double>();
70	70	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * Volt.StandardDeviationPop();
71		Volt_noise.AddRange(Volt.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	71	Volt_noise.AddRange(Volt.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
72	72	data.Remove(Volt);
73	73	data.Add(Volt_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman26.cs

r17805	r17966
69	69	var theta1_noise = new List<double>();
70	70	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * theta1.StandardDeviationPop();
71		theta1_noise.AddRange(theta1.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	71	theta1_noise.AddRange(theta1.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
72	72	data.Remove(theta1);
73	73	data.Add(theta1_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman27.cs

r17805	r17966
71	71	var foc_noise = new List<double>();
72	72	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * foc.StandardDeviationPop();
73		foc_noise.AddRange(foc.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	73	foc_noise.AddRange(foc.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
74	74	data.Remove(foc);
75	75	data.Add(foc_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman28.cs

r17805	r17966
69	69	var k_noise = new List<double>();
70	70	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * k.StandardDeviationPop();
71		k_noise.AddRange(k.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	71	k_noise.AddRange(k.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
72	72	data.Remove(k);
73	73	data.Add(k_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman29.cs

r17805	r17966
74	74	var x_noise = new List<double>();
75	75	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * x.StandardDeviationPop();
76		x_noise.AddRange(x.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	76	x_noise.AddRange(x.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
77	77	data.Remove(x);
78	78	data.Add(x_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman3.cs

r17805	r17966
72	72	var f_noise = new List<double>();
73	73	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * f.StandardDeviationPop();
74		f_noise.AddRange(f.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	74	f_noise.AddRange(f.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
75	75	data.Remove(f);
76	76	data.Add(f_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman30.cs

r17805	r17966
71	71	var Int_noise = new List<double>();
72	72	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * Int.StandardDeviationPop();
73		Int_noise.AddRange(Int.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	73	Int_noise.AddRange(Int.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
74	74	data.Remove(Int);
75	75	data.Add(Int_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman31.cs

r17805	r17966
71	71	var theta_noise = new List<double>();
72	72	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * theta.StandardDeviationPop();
73		theta_noise.AddRange(theta.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	73	theta_noise.AddRange(theta.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
74	74	data.Remove(theta);
75	75	data.Add(theta_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman32.cs

r17805	r17966
73	73	var Pwr_noise = new List<double>();
74	74	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * Pwr.StandardDeviationPop();
75		Pwr_noise.AddRange(Pwr.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	75	Pwr_noise.AddRange(Pwr.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
76	76	data.Remove(Pwr);
77	77	data.Add(Pwr_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman33.cs

r17805	r17966
81	81	var Pwr_noise = new List<double>();
82	82	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * Pwr.StandardDeviationPop();
83		Pwr_noise.AddRange(Pwr.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	83	Pwr_noise.AddRange(Pwr.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
84	84	data.Remove(Pwr);
85	85	data.Add(Pwr_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman34.cs

r17805	r17966
73	73	var omega_noise = new List<double>();
74	74	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * omega.StandardDeviationPop();
75		omega_noise.AddRange(omega.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	75	omega_noise.AddRange(omega.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
76	76	data.Remove(omega);
77	77	data.Add(omega_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman35.cs

r17805	r17966
71	71	var omega_noise = new List<double>();
72	72	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * omega.StandardDeviationPop();
73		omega_noise.AddRange(omega.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	73	omega_noise.AddRange(omega.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
74	74	data.Remove(omega);
75	75	data.Add(omega_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman36.cs

r17805	r17966
71	71	var omega_noise = new List<double>();
72	72	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * omega.StandardDeviationPop();
73		omega_noise.AddRange(omega.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	73	omega_noise.AddRange(omega.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
74	74	data.Remove(omega);
75	75	data.Add(omega_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman37.cs

r17805	r17966
69	69	var E_n_noise = new List<double>();
70	70	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * E_n.StandardDeviationPop();
71		E_n_noise.AddRange(E_n.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	71	E_n_noise.AddRange(E_n.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
72	72	data.Remove(E_n);
73	73	data.Add(E_n_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman38.cs

r17805	r17966
71	71	var Int_noise = new List<double>();
72	72	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * Int.StandardDeviationPop();
73		Int_noise.AddRange(Int.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	73	Int_noise.AddRange(Int.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
74	74	data.Remove(Int);
75	75	data.Add(Int_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman39.cs

r17805	r17966
73	73	var r_noise = new List<double>();
74	74	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * r.StandardDeviationPop();
75		r_noise.AddRange(r.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	75	r_noise.AddRange(r.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
76	76	data.Remove(r);
77	77	data.Add(r_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman4.cs

r17805	r17966
73	73	var d_noise = new List<double>();
74	74	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * d.StandardDeviationPop();
75		d_noise.AddRange(d.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	75	d_noise.AddRange(d.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
76	76	data.Remove(d);
77	77	data.Add(d_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman40.cs

r17805	r17966
69	69	var E_n_noise = new List<double>();
70	70	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * E_n.StandardDeviationPop();
71		E_n_noise.AddRange(E_n.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	71	E_n_noise.AddRange(E_n.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
72	72	data.Remove(E_n);
73	73	data.Add(E_n_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman41.cs

r17805	r17966
71	71	var E_n_noise = new List<double>();
72	72	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * E_n.StandardDeviationPop();
73		E_n_noise.AddRange(E_n.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	73	E_n_noise.AddRange(E_n.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
74	74	data.Remove(E_n);
75	75	data.Add(E_n_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman42.cs

r17805	r17966
73	73	var pr_noise = new List<double>();
74	74	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * pr.StandardDeviationPop();
75		pr_noise.AddRange(pr.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	75	pr_noise.AddRange(pr.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
76	76	data.Remove(pr);
77	77	data.Add(pr_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman43.cs

r17805	r17966
77	77	var n_noise = new List<double>();
78	78	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * n.StandardDeviationPop();
79		n_noise.AddRange(n.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	79	n_noise.AddRange(n.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
80	80	data.Remove(n);
81	81	data.Add(n_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman44.cs

r17805	r17966
78	78	var L_rad_noise = new List<double>();
79	79	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * L_rad.StandardDeviationPop();
80		L_rad_noise.AddRange(L_rad.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	80	L_rad_noise.AddRange(L_rad.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
81	81	data.Remove(L_rad);
82	82	data.Add(L_rad_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman45.cs

r17805	r17966
73	73	var v_noise = new List<double>();
74	74	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * v.StandardDeviationPop();
75		v_noise.AddRange(v.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	75	v_noise.AddRange(v.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
76	76	data.Remove(v);
77	77	data.Add(v_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman46.cs

r17805	r17966
71	71	var D_noise = new List<double>();
72	72	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * D.StandardDeviationPop();
73		D_noise.AddRange(D.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	73	D_noise.AddRange(D.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
74	74	data.Remove(D);
75	75	data.Add(D_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman47.cs

r17805	r17966
73	73	var kappa_noise = new List<double>();
74	74	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * kappa.StandardDeviationPop();
75		kappa_noise.AddRange(kappa.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	75	kappa_noise.AddRange(kappa.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
76	76	data.Remove(kappa);
77	77	data.Add(kappa_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman48.cs

r17805	r17966
75	75	var E_n_noise = new List<double>();
76	76	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * E_n.StandardDeviationPop();
77		E_n_noise.AddRange(E_n.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	77	E_n_noise.AddRange(E_n.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
78	78	data.Remove(E_n);
79	79	data.Add(E_n_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman49.cs

r17805	r17966
71	71	var c_noise = new List<double>();
72	72	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * c.StandardDeviationPop();
73		c_noise.AddRange(c.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	73	c_noise.AddRange(c.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
74	74	data.Remove(c);
75	75	data.Add(c_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman5.cs

r17805	r17966
86	86	var F_noise = new List<double>();
87	87	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * F.StandardDeviationPop();
88		F_noise.AddRange(F.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	88	F_noise.AddRange(F.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
89	89	data.Remove(F);
90	90	data.Add(F_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman50.cs

r17805	r17966
71	71	var E_n_noise = new List<double>();
72	72	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * E_n.StandardDeviationPop();
73		E_n_noise.AddRange(E_n.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	73	E_n_noise.AddRange(E_n.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
74	74	data.Remove(E_n);
75	75	data.Add(E_n_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman51.cs

r17805	r17966
73	73	var x_noise = new List<double>();
74	74	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * x.StandardDeviationPop();
75		x_noise.AddRange(x.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	75	x_noise.AddRange(x.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
76	76	data.Remove(x);
77	77	data.Add(x_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman52.cs

r17805	r17966
75	75	var Pwr_noise = new List<double>();
76	76	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * Pwr.StandardDeviationPop();
77		Pwr_noise.AddRange(Pwr.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	77	Pwr_noise.AddRange(Pwr.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
78	78	data.Remove(Pwr);
79	79	data.Add(Pwr_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman53.cs

r17805	r17966
69	69	var flux_noise = new List<double>();
70	70	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * flux.StandardDeviationPop();
71		flux_noise.AddRange(flux.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	71	flux_noise.AddRange(flux.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
72	72	data.Remove(flux);
73	73	data.Add(flux_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman54.cs

r17805	r17966
71	71	var Volt_noise = new List<double>();
72	72	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * Volt.StandardDeviationPop();
73		Volt_noise.AddRange(Volt.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	73	Volt_noise.AddRange(Volt.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
74	74	data.Remove(Volt);
75	75	data.Add(Volt_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman55.cs

r17805	r17966
73	73	var Volt_noise = new List<double>();
74	74	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * Volt.StandardDeviationPop();
75		Volt_noise.AddRange(Volt.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	75	Volt_noise.AddRange(Volt.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
76	76	data.Remove(Volt);
77	77	data.Add(Volt_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman56.cs

r17805	r17966
78	78	var Ef_noise = new List<double>();
79	79	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * Ef.StandardDeviationPop();
80		Ef_noise.AddRange(Ef.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	80	Ef_noise.AddRange(Ef.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
81	81	data.Remove(Ef);
82	82	data.Add(Ef_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman57.cs

r17805	r17966
74	74	var Ef_noise = new List<double>();
75	75	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * Ef.StandardDeviationPop();
76		Ef_noise.AddRange(Ef.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	76	Ef_noise.AddRange(Ef.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
77	77	data.Remove(Ef);
78	78	data.Add(Ef_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman58.cs

r17805	r17966
71	71	var E_n_noise = new List<double>();
72	72	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * E_n.StandardDeviationPop();
73		E_n_noise.AddRange(E_n.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	73	E_n_noise.AddRange(E_n.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
74	74	data.Remove(E_n);
75	75	data.Add(E_n_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman59.cs

r17805	r17966
69	69	var E_den_noise = new List<double>();
70	70	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * E_den.StandardDeviationPop();
71		E_den_noise.AddRange(E_den.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	71	E_den_noise.AddRange(E_den.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
72	72	data.Remove(E_den);
73	73	data.Add(E_den_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman6.cs

r17805	r17966
71	71	var m_noise = new List<double>();
72	72	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * m.StandardDeviationPop();
73		m_noise.AddRange(m.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	73	m_noise.AddRange(m.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
74	74	data.Remove(m);
75	75	data.Add(m_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman60.cs

r17805	r17966
71	71	var Ef_noise = new List<double>();
72	72	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * Ef.StandardDeviationPop();
73		Ef_noise.AddRange(Ef.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	73	Ef_noise.AddRange(Ef.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
74	74	data.Remove(Ef);
75	75	data.Add(Ef_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman61.cs

r17805	r17966
75	75	var x_noise = new List<double>();
76	76	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * x.StandardDeviationPop();
77		x_noise.AddRange(x.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	77	x_noise.AddRange(x.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
78	78	data.Remove(x);
79	79	data.Add(x_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman62.cs

r17805	r17966
77	77	var n_noise = new List<double>();
78	78	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * n.StandardDeviationPop();
79		n_noise.AddRange(n.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	79	n_noise.AddRange(n.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
80	80	data.Remove(n);
81	81	data.Add(n_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman63.cs

r17805	r17966
75	75	var Pol_noise = new List<double>();
76	76	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * Pol.StandardDeviationPop();
77		Pol_noise.AddRange(Pol.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	77	Pol_noise.AddRange(Pol.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
78	78	data.Remove(Pol);
79	79	data.Add(Pol_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman64.cs

r17805	r17966
73	73	var Pol_noise = new List<double>();
74	74	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * Pol.StandardDeviationPop();
75		Pol_noise.AddRange(Pol.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	75	Pol_noise.AddRange(Pol.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
76	76	data.Remove(Pol);
77	77	data.Add(Pol_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman65.cs

r17805	r17966
69	69	var theta_noise = new List<double>();
70	70	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * theta.StandardDeviationPop();
71		theta_noise.AddRange(theta.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	71	theta_noise.AddRange(theta.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
72	72	data.Remove(theta);
73	73	data.Add(theta_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman66.cs

r17805	r17966
73	73	var B_noise = new List<double>();
74	74	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * B.StandardDeviationPop();
75		B_noise.AddRange(B.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	75	B_noise.AddRange(B.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
76	76	data.Remove(B);
77	77	data.Add(B_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman67.cs

r17805	r17966
71	71	var rho_c_noise = new List<double>();
72	72	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * rho_c.StandardDeviationPop();
73		rho_c_noise.AddRange(rho_c.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	73	rho_c_noise.AddRange(rho_c.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
74	74	data.Remove(rho_c);
75	75	data.Add(rho_c_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman68.cs

r17805	r17966
71	71	var j_noise = new List<double>();
72	72	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * j.StandardDeviationPop();
73		j_noise.AddRange(j.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	73	j_noise.AddRange(j.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
74	74	data.Remove(j);
75	75	data.Add(j_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman69.cs

r17805	r17966
71	71	var E_n_noise = new List<double>();
72	72	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * E_n.StandardDeviationPop();
73		E_n_noise.AddRange(E_n.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	73	E_n_noise.AddRange(E_n.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
74	74	data.Remove(E_n);
75	75	data.Add(E_n_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman7.cs

r17805	r17966
77	77	var A_noise = new List<double>();
78	78	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * A.StandardDeviationPop();
79		A_noise.AddRange(A.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	79	A_noise.AddRange(A.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
80	80	data.Remove(A);
81	81	data.Add(A_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman70.cs

r17805	r17966
71	71	var E_n_noise = new List<double>();
72	72	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * E_n.StandardDeviationPop();
73		E_n_noise.AddRange(E_n.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	73	E_n_noise.AddRange(E_n.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
74	74	data.Remove(E_n);
75	75	data.Add(E_n_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman71.cs

r17805	r17966
75	75	var Volt_noise = new List<double>();
76	76	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * Volt.StandardDeviationPop();
77		Volt_noise.AddRange(Volt.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	77	Volt_noise.AddRange(Volt.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
78	78	data.Remove(Volt);
79	79	data.Add(Volt_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman72.cs

r17805	r17966
71	71	var k_noise = new List<double>();
72	72	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * k.StandardDeviationPop();
73		k_noise.AddRange(k.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	73	k_noise.AddRange(k.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
74	74	data.Remove(k);
75	75	data.Add(k_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman73.cs

r17805	r17966
71	71	var flux_noise = new List<double>();
72	72	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * flux.StandardDeviationPop();
73		flux_noise.AddRange(flux.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	73	flux_noise.AddRange(flux.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
74	74	data.Remove(flux);
75	75	data.Add(flux_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman74.cs

r17805	r17966
69	69	var E_den_noise = new List<double>();
70	70	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * E_den.StandardDeviationPop();
71		E_den_noise.AddRange(E_den.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	71	E_den_noise.AddRange(E_den.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
72	72	data.Remove(E_den);
73	73	data.Add(E_den_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman75.cs

r17805	r17966
71	71	var I_noise = new List<double>();
72	72	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * I.StandardDeviationPop();
73		I_noise.AddRange(I.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	73	I_noise.AddRange(I.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
74	74	data.Remove(I);
75	75	data.Add(I_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman76.cs

r17805	r17966
71	71	var mom_noise = new List<double>();
72	72	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * mom.StandardDeviationPop();
73		mom_noise.AddRange(mom.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	73	mom_noise.AddRange(mom.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
74	74	data.Remove(mom);
75	75	data.Add(mom_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman77.cs

r17805	r17966
73	73	var omega_noise = new List<double>();
74	74	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * omega.StandardDeviationPop();
75		omega_noise.AddRange(omega.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	75	omega_noise.AddRange(omega.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
76	76	data.Remove(omega);
77	77	data.Add(omega_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman78.cs

r17805	r17966
71	71	var mom_noise = new List<double>();
72	72	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * mom.StandardDeviationPop();
73		mom_noise.AddRange(mom.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	73	mom_noise.AddRange(mom.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
74	74	data.Remove(mom);
75	75	data.Add(mom_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman79.cs

r17805	r17966
75	75	var E_n_noise = new List<double>();
76	76	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * E_n.StandardDeviationPop();
77		E_n_noise.AddRange(E_n.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	77	E_n_noise.AddRange(E_n.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
78	78	data.Remove(E_n);
79	79	data.Add(E_n_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman8.cs

r17805	r17966
69	69	var F_noise = new List<double>();
70	70	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * F.StandardDeviationPop();
71		F_noise.AddRange(F.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	71	F_noise.AddRange(F.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
72	72	data.Remove(F);
73	73	data.Add(F_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman80.cs

r17805	r17966
75	75	var n_noise = new List<double>();
76	76	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * n.StandardDeviationPop();
77		n_noise.AddRange(n.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	77	n_noise.AddRange(n.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
78	78	data.Remove(n);
79	79	data.Add(n_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman81.cs

r17805	r17966
75	75	var M_noise = new List<double>();
76	76	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * M.StandardDeviationPop();
77		M_noise.AddRange(M.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	77	M_noise.AddRange(M.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
78	78	data.Remove(M);
79	79	data.Add(M_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman82.cs

r17805	r17966
85	85	var f_noise = new List<double>();
86	86	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * f.StandardDeviationPop();
87		f_noise.AddRange(f.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	87	f_noise.AddRange(f.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
88	88	data.Remove(f);
89	89	data.Add(f_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman83.cs

r17805	r17966
71	71	var E_n_noise = new List<double>();
72	72	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * E_n.StandardDeviationPop();
73		E_n_noise.AddRange(E_n.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	73	E_n_noise.AddRange(E_n.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
74	74	data.Remove(E_n);
75	75	data.Add(E_n_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman84.cs

r17805	r17966
73	73	var F_noise = new List<double>();
74	74	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * F.StandardDeviationPop();
75		F_noise.AddRange(F.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	75	F_noise.AddRange(F.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
76	76	data.Remove(F);
77	77	data.Add(F_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman85.cs

r17805	r17966
69	69	var mu_S_noise = new List<double>();
70	70	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * mu_S.StandardDeviationPop();
71		mu_S_noise.AddRange(mu_S.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	71	mu_S_noise.AddRange(mu_S.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
72	72	data.Remove(mu_S);
73	73	data.Add(mu_S_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman86.cs

r17805	r17966
73	73	var n_noise = new List<double>();
74	74	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * n.StandardDeviationPop();
75		n_noise.AddRange(n.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	75	n_noise.AddRange(n.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
76	76	data.Remove(n);
77	77	data.Add(n_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman87.cs

r17805	r17966
74	74	var E_n_noise = new List<double>();
75	75	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * E_n.StandardDeviationPop();
76		E_n_noise.AddRange(E_n.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	76	E_n_noise.AddRange(E_n.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
77	77	data.Remove(E_n);
78	78	data.Add(E_n_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman88.cs

r17805	r17966
71	71	var omega_noise = new List<double>();
72	72	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * omega.StandardDeviationPop();
73		omega_noise.AddRange(omega.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	73	omega_noise.AddRange(omega.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
74	74	data.Remove(omega);
75	75	data.Add(omega_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman89.cs

r17805	r17966
71	71	var prob_noise = new List<double>();
72	72	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * prob.StandardDeviationPop();
73		prob_noise.AddRange(prob.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	73	prob_noise.AddRange(prob.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
74	74	data.Remove(prob);
75	75	data.Add(prob_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman9.cs

r17805	r17966
73	73	var F_noise = new List<double>();
74	74	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * F.StandardDeviationPop();
75		F_noise.AddRange(F.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	75	F_noise.AddRange(F.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
76	76	data.Remove(F);
77	77	data.Add(F_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman90.cs

r17805	r17966
82	82	var prob_noise = new List<double>();
83	83	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * prob.StandardDeviationPop();
84		prob_noise.AddRange(prob.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	84	prob_noise.AddRange(prob.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
85	85	data.Remove(prob);
86	86	data.Add(prob_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman91.cs

r17805	r17966
73	73	var E_n_noise = new List<double>();
74	74	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * E_n.StandardDeviationPop();
75		E_n_noise.AddRange(E_n.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	75	E_n_noise.AddRange(E_n.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
76	76	data.Remove(E_n);
77	77	data.Add(E_n_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman92.cs

r17805	r17966
65	65	var L_noise = new List<double>();
66	66	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * L.StandardDeviationPop();
67		L_noise.AddRange(L.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	67	L_noise.AddRange(L.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
68	68	data.Remove(L);
69	69	data.Add(L_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman93.cs

r17805	r17966
73	73	var v_noise = new List<double>();
74	74	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * v.StandardDeviationPop();
75		v_noise.AddRange(v.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	75	v_noise.AddRange(v.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
76	76	data.Remove(v);
77	77	data.Add(v_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman94.cs

r17805	r17966
75	75	var I_noise = new List<double>();
76	76	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * I.StandardDeviationPop();
77		I_noise.AddRange(I.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	77	I_noise.AddRange(I.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
78	78	data.Remove(I);
79	79	data.Add(I_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman95.cs

r17805	r17966
71	71	var E_n_noise = new List<double>();
72	72	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * E_n.StandardDeviationPop();
73		E_n_noise.AddRange(E_n.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	73	E_n_noise.AddRange(E_n.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
74	74	data.Remove(E_n);
75	75	data.Add(E_n_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman96.cs

r17805	r17966
71	71	var m_noise = new List<double>();
72	72	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * m.StandardDeviationPop();
73		m_noise.AddRange(m.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	73	m_noise.AddRange(m.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
74	74	data.Remove(m);
75	75	data.Add(m_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman97.cs

r17805	r17966
71	71	var k_noise = new List<double>();
72	72	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * k.StandardDeviationPop();
73		k_noise.AddRange(k.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	73	k_noise.AddRange(k.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
74	74	data.Remove(k);
75	75	data.Add(k_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman98.cs

r17805	r17966
71	71	var f_noise = new List<double>();
72	72	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * f.StandardDeviationPop();
73		f_noise.AddRange(f.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	73	f_noise.AddRange(f.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
74	74	data.Remove(f);
75	75	data.Add(f_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman99.cs

r17805	r17966
77	77	var E_n_noise = new List<double>();
78	78	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * E_n.StandardDeviationPop();
79		E_n_noise.AddRange(E_n.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	79	E_n_noise.AddRange(E_n.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
80	80	data.Remove(E_n);
81	81	data.Add(E_n_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/FeynmanBonus1.cs

r17805	r17966
83	83	var A_noise = new List<double>();
84	84	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * A.StandardDeviationPop();
85		A_noise.AddRange(A.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	85	A_noise.AddRange(A.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
86	86	data.Remove(A);
87	87	data.Add(A_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/FeynmanBonus10.cs

r17805	r17966
73	73	var t_noise = new List<double>();
74	74	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * t.StandardDeviationPop();
75		t_noise.AddRange(t.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	75	t_noise.AddRange(t.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
76	76	data.Remove(t);
77	77	data.Add(t_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/FeynmanBonus11.cs

r17805	r17966
85	85	var alpha_noise = new List<double>();
86	86	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * alpha.StandardDeviationPop();
87		alpha_noise.AddRange(alpha.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	87	alpha_noise.AddRange(alpha.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
88	88	data.Remove(alpha);
89	89	data.Add(alpha_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/FeynmanBonus12.cs

r17805	r17966
79	79	var E_n_noise = new List<double>();
80	80	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * E_n.StandardDeviationPop();
81		E_n_noise.AddRange(E_n.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	81	E_n_noise.AddRange(E_n.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
82	82	data.Remove(E_n);
83	83	data.Add(E_n_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/FeynmanBonus13.cs

r17805	r17966
80	80	var E_n_noise = new List<double>();
81	81	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * E_n.StandardDeviationPop();
82		E_n_noise.AddRange(E_n.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	82	E_n_noise.AddRange(E_n.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
83	83	data.Remove(E_n);
84	84	data.Add(E_n_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/FeynmanBonus14.cs

r17805	r17966
78	78	var F_noise = new List<double>();
79	79	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * F.StandardDeviationPop();
80		F_noise.AddRange(F.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	80	F_noise.AddRange(F.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
81	81	data.Remove(F);
82	82	data.Add(F_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/FeynmanBonus15.cs

r17805	r17966
75	75	var Volt_noise = new List<double>();
76	76	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * Volt.StandardDeviationPop();
77		Volt_noise.AddRange(Volt.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	77	Volt_noise.AddRange(Volt.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
78	78	data.Remove(Volt);
79	79	data.Add(Volt_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/FeynmanBonus16.cs

r17805	r17966
77	77	var Volt_noise = new List<double>();
78	78	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * Volt.StandardDeviationPop();
79		Volt_noise.AddRange(Volt.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	79	Volt_noise.AddRange(Volt.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
80	80	data.Remove(Volt);
81	81	data.Add(Volt_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/FeynmanBonus17.cs

r17805	r17966
75	75	var omega_0_noise = new List<double>();
76	76	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * omega_0.StandardDeviationPop();
77		omega_0_noise.AddRange(omega_0.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	77	omega_0_noise.AddRange(omega_0.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
78	78	data.Remove(omega_0);
79	79	data.Add(omega_0_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/FeynmanBonus18.cs

r17805	r17966
75	75	var rho_0_noise = new List<double>();
76	76	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * rho_0.StandardDeviationPop();
77		rho_0_noise.AddRange(rho_0.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	77	rho_0_noise.AddRange(rho_0.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
78	78	data.Remove(rho_0);
79	79	data.Add(rho_0_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/FeynmanBonus19.cs

r17805	r17966
79	79	var pr_noise = new List<double>();
80	80	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * pr.StandardDeviationPop();
81		pr_noise.AddRange(pr.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	81	pr_noise.AddRange(pr.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
82	82	data.Remove(pr);
83	83	data.Add(pr_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/FeynmanBonus2.cs

r17805	r17966
75	75	var H_G_noise = new List<double>();
76	76	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * H_G.StandardDeviationPop();
77		H_G_noise.AddRange(H_G.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	77	H_G_noise.AddRange(H_G.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
78	78	data.Remove(H_G);
79	79	data.Add(H_G_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/FeynmanBonus20.cs

r17805	r17966
84	84	var A_noise = new List<double>();
85	85	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * A.StandardDeviationPop();
86		A_noise.AddRange(A.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	86	A_noise.AddRange(A.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
87	87	data.Remove(A);
88	88	data.Add(A_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/FeynmanBonus3.cs

r17805	r17966
74	74	var K_noise = new List<double>();
75	75	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * K.StandardDeviationPop();
76		K_noise.AddRange(K.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	76	K_noise.AddRange(K.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
77	77	data.Remove(K);
78	78	data.Add(K_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/FeynmanBonus4.cs

r17805	r17966
77	77	var Pwr_noise = new List<double>();
78	78	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * Pwr.StandardDeviationPop();
79		Pwr_noise.AddRange(Pwr.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	79	Pwr_noise.AddRange(Pwr.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
80	80	data.Remove(Pwr);
81	81	data.Add(Pwr_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/FeynmanBonus5.cs

r17805	r17966
72	72	var theta1_noise = new List<double>();
73	73	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * theta1.StandardDeviationPop();
74		theta1_noise.AddRange(theta1.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	74	theta1_noise.AddRange(theta1.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
75	75	data.Remove(theta1);
76	76	data.Add(theta1_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/FeynmanBonus6.cs

r17805	r17966
76	76	var I_noise = new List<double>();
77	77	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * I.StandardDeviationPop();
78		I_noise.AddRange(I.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	78	I_noise.AddRange(I.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
79	79	data.Remove(I);
80	80	data.Add(I_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/FeynmanBonus7.cs

r17805	r17966
75	75	var v_noise = new List<double>();
76	76	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * v.StandardDeviationPop();
77		v_noise.AddRange(v.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	77	v_noise.AddRange(v.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
78	78	data.Remove(v);
79	79	data.Add(v_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/FeynmanBonus8.cs

r17805	r17966
82	82	var k_noise = new List<double>();
83	83	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * k.StandardDeviationPop();
84		k_noise.AddRange(k.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	84	k_noise.AddRange(k.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
85	85	data.Remove(k);
86	86	data.Add(k_noise);

trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/FeynmanBonus9.cs

r17805	r17966
74	74	var r_noise = new List<double>();
75	75	var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * r.StandardDeviationPop();
76		r_noise.AddRange(r.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
	76	r_noise.AddRange(r.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
77	77	data.Remove(r);
78	78	data.Add(r_noise);

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Changeset 17966 for trunk/HeuristicLab.Problems.Instances.DataAnalysis

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