public override SAFuzzySystem TuneUpFuzzySystem(SAFuzzySystem Approx, ILearnAlgorithmConf conf)
{
result = Approx;
List <int[]> groups = new List <int[]>();
Init(conf);
SetPopulation();
Population = ListSingletonApproximateTool.SortRules(Population, result);
NS = new int[Nsr];
NS = SetNS(Population, Nsr);
groups = GroupStream();
double BestMSETest = result.RMSEtoMSEforTest(result.approxTestSamples(Population[0]));
double BestMSELearn = result.RMSEtoMSEforLearn(result.approxLearnSamples(Population[0]));
int BestIter = 0;
/*StringBuilder sb = new StringBuilder();
* sb.AppendLine("sep=.");*/
for (int i = 1; i <= MaxIter; i++)
{
Console.Clear();
Console.WriteLine((double)i * 100 / MaxIter + "%");
Population = SetNextPosition(groups, Population);
Population = Replacement(groups, Population);
if (flag)
{
Evaporation(groups.Last());//Испарение
}
if (BestMSETest > result.RMSEtoMSEforTest(result.approxTestSamples(Population[0])))
{
BestMSETest = result.RMSEtoMSEforTest(result.approxTestSamples(Population[0]));
BestMSELearn = result.RMSEtoMSEforLearn(result.approxLearnSamples(Population[0]));
BestIter = i;
}
//sb.AppendLine((result.RMSEtoMSEforLearn(result.approxLearnSamples(Population[0]))).ToString() + "." + (result.RMSEtoMSEforTest(result.approxTestSamples(Population[0]))).ToString());
}
/*FileStream file1 = new FileStream("F:\\Table.scv", FileMode.Create);
* StreamWriter writer = new StreamWriter(file1);
* writer.Write(sb);
* writer.Close();
* file1.Close();*/
Console.WriteLine(ToString(true));
Console.WriteLine("Итер - " + BestIter + " MSET - " + BestMSETest + " MSEL - " + BestMSELearn);
result.RulesDatabaseSet[0] = Population[0];
return(result);
}
public override SAFuzzySystem TuneUpFuzzySystem(SAFuzzySystem Approx, ILearnAlgorithmConf conf)
{
result = Approx;
Init(conf);
HeadLeader = new KnowlegeBaseSARules(result.RulesDatabaseSet[0]);
VelocityVector = new KnowlegeBaseSARules(result.RulesDatabaseSet[0]);
VelocityVectorLL = new KnowlegeBaseSARules(result.RulesDatabaseSet[0]);
VelocityVectorHL = new KnowlegeBaseSARules(result.RulesDatabaseSet[0]);
for (int i = 0; i < VelocityVector.TermsSet.Count; i++)
{
for (int j = 0; j < VelocityVector.TermsSet[i].Parametrs.Length; j++)
{
VelocityVector.TermsSet[i].Parametrs[j] = 0;
VelocityVectorLL.TermsSet[i].Parametrs[j] = 0;
VelocityVectorHL.TermsSet[i].Parametrs[j] = 0;
}
}
SetPopulation();
ParticlesBest = new Dictionary <KnowlegeBaseSARules, KnowlegeBaseSARules>();
foreach (var Particle in Population)
{
ParticlesBest.Add(Particle, Universal);
}
LocalLeaders = new KnowlegeBaseSARules[numberOfLocalLeaders];
Console.WriteLine(LocalLeaders.Length);
ExplorerParticles = new KnowlegeBaseSARules[numberOfAllParts - numberOfAimlessParts - numberOfLocalLeaders - 1];
Console.WriteLine(ExplorerParticles.Length);
AimlessParticles = new KnowlegeBaseSARules[numberOfAimlessParts];
Console.WriteLine(AimlessParticles.Length);
while (iter < MaxIter)
{
Population = ListSingletonApproximateTool.SortRules(Population, result);
SetRoles();
ChangeExplorersPositions();
ChangeAimlessPositions();
DiscardRoles();
iter++;
Console.WriteLine("Iteration: " + iter.ToString());
Console.WriteLine(result.RMSEtoMSEforLearn(result.approxLearnSamples(Population[0])));
Console.WriteLine(result.RMSEtoMSEforTest(result.approxTestSamples(Population[0])));
Console.WriteLine(result.approxLearnSamples(Population[numberOfLocalLeaders + 1]));
}
result.RulesDatabaseSet[0] = Population[0];
return(result);
}
//основные вычисления
public override SAFuzzySystem TuneUpFuzzySystem(SAFuzzySystem Approx, ILearnAlgorithmConf conf)
{
result = Approx;
Init(conf);
SetPopulation();
KnowlegeBaseSARules BEST = new KnowlegeBaseSARules(result.RulesDatabaseSet[0]);
double bestError = result.ErrorLearnSamples(BEST);
//отчистка консоли
#if debug
Console.Clear();
#endif
//запуск итераций
for (int it = 0; it < iter; it++)
{
#if debug
//вывод номера итерации
Console.Write("Итерация __№__ = ");
Console.WriteLine(it);
#endif
//расчитыавем значение фитнес-функции
Population = ListSingletonApproximateTool.SortRules(Population, result);
double[] K = new double[Population.Length];
for (int i = 0; i < Population.Length; i++)
{
K[i] = result.ErrorLearnSamples(Population[i]);
#if debug
Console.Write("Значние K[i1] = ");
Console.WriteLine(K[i]);
#endif
if (double.IsNaN(K[i]) || double.IsInfinity(K[i]))
{
result.UnlaidProtectionFix(Population[i]);
K[i] = result.ErrorLearnSamples(Population[i]);
#if debug
Console.Write("Значние K[i2] = ");
Console.WriteLine(K[i]);
#endif
}
}
Kworst = K.Max();
if (double.IsNaN(Kworst) || double.IsInfinity(Kworst))
{
int iworst = K.ToList().IndexOf(Kworst);
#if debug
Console.Write("Значние iworst = ");
Console.WriteLine(iworst);
#endif
}
#if debug
//вывод Kworst
Console.Write("Значние KWorst = ");
Console.WriteLine(Kworst);
#endif
Kbest = K.Min();
#if debug
//вывод Kbest
Console.Write("Значние Kbest = ");
Console.WriteLine(Kbest);
#endif
int ibest = K.ToList().IndexOf(Kbest);
#if debug
//вывод ibest
Console.Write("Значние ibest = ");
Console.WriteLine(ibest);
#endif
//перебрать значения фитнес функции
//расчитыавем значение D
double dit;
dit = it;
double diter;
diter = iter;
double D = (dmax * (rand.NextDouble() * 2 - 1) * (dit / diter));
//расчитываем значение rand1 для D
double rand1;
rand1 = D / (dmax * (it) / iter);
#if debug
//выводим значение rand1 для D
Console.Write("Значение Drand = ");
Console.WriteLine(rand1);
//выводим значение D
Console.Write("Значение __D__ = ");
Console.WriteLine(D);
#endif
//расчитыавем значение Xfood
double divide = K.Select(x => 1 / x).ToList().Sum();
var Xfood = new KnowlegeBaseSARules(Population[0]);
for (int t = 0; t < Xfood.TermsSet.Count; t++)
{
for (int p = 0; p < Xfood.TermsSet[t].CountParams; p++)
{
Xfood.TermsSet[t].Parametrs[p] = 0;
for (int i = 0; i < Population.Length; i++)
{
Xfood.TermsSet[t].Parametrs[p] += Population[i].TermsSet[t].Parametrs[p] / K[i];
#if debug
//выводим значение Xfood
Console.Write("Значение Xfood = ");
Console.WriteLine(Xfood.TermsSet[t].Parametrs[p]);
#endif
}
Xfood.TermsSet[t].Parametrs[p] /= divide;
}
}
#if debug
//вывод divide
Console.Write("Значние divide = ");
Console.WriteLine(divide);
#endif
//расчитываем значение Kfood
double Kfood = result.ErrorLearnSamples(Xfood);
if (double.IsNaN(Kfood) || double.IsInfinity(Kfood))
{
result.UnlaidProtectionFix(Xfood);
Kfood = result.ErrorLearnSamples(Xfood);
}
#if debug
//выводим значение Kfood
Console.Write("Значение Kfood = ");
Console.WriteLine(Kfood);
#endif
//расчитываем значение Cfood
double Cfood = 2 * (1 - (dit / diter));
#if debug
//выводим значение Cfood
Console.Write("Значение Cfood = ");
Console.WriteLine(Cfood);
#endif
//расчитываем значение Bfood
KnowlegeBaseSARules[] Bfood = new KnowlegeBaseSARules[Population.Length];
for (int i = 0; i < Population.Length; i++)
{
Bfood[i] = new KnowlegeBaseSARules(Population[i]);
double KRoofifood = CalcKroof(K[i], Kfood);
KnowlegeBaseSARules Xroofifood = new KnowlegeBaseSARules(CalcXroof(Population[i], Xfood));
for (int t = 0; t < Bfood[i].TermsSet.Count; t++)
{
for (int p = 0; p < Bfood[i].TermsSet[t].CountParams; p++)
{
Bfood[i].TermsSet[t].Parametrs[p] = Cfood * KRoofifood * Xroofifood.TermsSet[t].Parametrs[p];
#if debug
//выводим значение Bfood
Console.Write("Значение Bfood = ");
Console.WriteLine(Bfood[i].TermsSet[t].Parametrs[p]);
#endif
}
}
}
//расчитываем значение Bbest
KnowlegeBaseSARules[] Bbest = new KnowlegeBaseSARules[Population.Length];
for (int i = 0; i < Population.Length; i++)
{
Bbest[i] = new KnowlegeBaseSARules(Population[i]);
double KRoofifood = CalcKroof(K[i], K[ibest]);
KnowlegeBaseSARules Xroofifood = new KnowlegeBaseSARules(CalcXroof(Population[i], Population[ibest]));
for (int t = 0; t < Bbest[i].TermsSet.Count; t++)
{
for (int p = 0; p < Bbest[i].TermsSet[t].CountParams; p++)
{
Bbest[i].TermsSet[t].Parametrs[p] = KRoofifood * Xroofifood.TermsSet[t].Parametrs[p];
#if debug
//выводим значение Bbest
Console.Write("Значение Bbest = ");
Console.WriteLine(Bbest[i].TermsSet[t].Parametrs[p]);
#endif
}
}
}
//расчитываем значение B
KnowlegeBaseSARules[] B = new KnowlegeBaseSARules[Population.Length];
for (int i = 0; i < Population.Length; i++)
{
B[i] = new KnowlegeBaseSARules(Population[i]);
for (int t = 0; t < B[i].TermsSet.Count; t++)
{
for (int p = 0; p < B[i].TermsSet[t].CountParams; p++)
{
B[i].TermsSet[t].Parametrs[p] = Bfood[i].TermsSet[t].Parametrs[p] + Bbest[i].TermsSet[t].Parametrs[p];
#if debug
//выводим значение B
Console.Write("Значение __B__ = ");
Console.WriteLine(B[i].TermsSet[t].Parametrs[p]);
#endif
}
}
}
//расчитываем значение F
KnowlegeBaseSARules[] F = new KnowlegeBaseSARules[Population.Length];
for (int i = 0; i < Population.Length; i++)
{
if (i == 0)
{
F[i] = new KnowlegeBaseSARules(Population[i]);
for (int t = 0; t < F[i].TermsSet.Count; t++)
{
for (int p = 0; p < F[i].TermsSet[t].CountParams; p++)
{
F[i].TermsSet[t].Parametrs[p] = Vf * B[i].TermsSet[t].Parametrs[p];
#if debug
//выводим значение F
Console.Write("Значение __F__ = ");
Console.WriteLine(F[i].TermsSet[t].Parametrs[p]);
#endif
}
}
}
else
{
F[i] = new KnowlegeBaseSARules(Population[i]);
for (int t = 0; t < F[i].TermsSet.Count; t++)
{
for (int p = 0; p < F[i].TermsSet[t].CountParams; p++)
{
F[i].TermsSet[t].Parametrs[p] = Vf * B[i].TermsSet[t].Parametrs[p] + wf * F[i - 1].TermsSet[t].Parametrs[p];
#if debug
//выводим значение F
Console.Write("Значение __F__ = ");
Console.WriteLine(F[i].TermsSet[t].Parametrs[p]);
#endif
}
}
}
}
List <int> [] neihbors = new List <int> [Population.Length];
//расчитываем значение alocal
KnowlegeBaseSARules[] alocal = new KnowlegeBaseSARules[Population.Length];
for (int i = 0; i < Population.Length; i++)
{
alocal[i] = new KnowlegeBaseSARules(Population[i]);
neihbors[i] = countneihbors(Population[i]);
/*
#if debug
* //вывод значений количества соседей
* for (int g = 0; g < Population.Length; g++)
* {
* Console.Write("Знаение countneihbors = ");
* Console.WriteLine(countneihbors(Population[g]));
* }
#endif
*/
for (int t = 0; t < alocal[i].TermsSet.Count; t++)
{
for (int p = 0; p < alocal[i].TermsSet[t].CountParams; p++)
{
alocal[i].TermsSet[t].Parametrs[p] = 0;
for (int j = 0; j < neihbors[i].Count; j++)
{
double KRoofij = CalcKroof(K[i], K[neihbors[i][j]]);
KnowlegeBaseSARules XRoofij = new KnowlegeBaseSARules(CalcXroof(Population[i], Population[neihbors[i][j]]));
alocal[i].TermsSet[t].Parametrs[p] += KRoofij * XRoofij.TermsSet[t].Parametrs[p];
#if debug
//выводим значение alocal
Console.Write("Знаение alocal = ");
Console.WriteLine(alocal[i].TermsSet[t].Parametrs[p]);
#endif
}
}
}
}
//расчитываем значение Cbest
double Cbest = 2 * (rand.NextDouble() - (dit / diter));
#if debug
//выводим значение Cbest
Console.Write("Значение Сbest = ");
Console.WriteLine(Cbest);
#endif
//расчитываем значение rand для Cbest
double rand2;
rand2 = it / iter - Cbest / 2;
#if debug
//выводим значение rand2 для Cbest
Console.Write("Значение Crand = ");
Console.WriteLine(rand2);
#endif
//расчитываем значение atarget
KnowlegeBaseSARules[] atarget = new KnowlegeBaseSARules[Population.Length];
for (int i = 0; i < Population.Length; i++)
{
atarget[i] = new KnowlegeBaseSARules(Population[i]);
double KRoofibest = CalcKroof(K[i], K[ibest]);
KnowlegeBaseSARules XRoofibest = new KnowlegeBaseSARules(CalcXroof(Population[i], Population[ibest]));
for (int t = 0; t < alocal[i].TermsSet.Count; t++)
{
for (int p = 0; p < atarget[i].TermsSet[t].CountParams; p++)
{
atarget[i].TermsSet[t].Parametrs[p] = Cbest * KRoofibest * XRoofibest.TermsSet[t].Parametrs[p];
#if debug
//выводим значение atarget
Console.Write("Знание atarget = ");
Console.WriteLine(atarget[i].TermsSet[t].Parametrs[p]);
#endif
}
}
}
//расчитываем значение a
KnowlegeBaseSARules[] a = new KnowlegeBaseSARules[Population.Length];
for (int i = 0; i < Population.Length; i++)
{
a[i] = new KnowlegeBaseSARules(Population[i]);
for (int t = 0; t < a[i].TermsSet.Count; t++)
{
for (int p = 0; p < a[i].TermsSet[t].CountParams; p++)
{
a[i].TermsSet[t].Parametrs[p] = atarget[i].TermsSet[t].Parametrs[p] + alocal[i].TermsSet[t].Parametrs[p];
#if debug
//выводим значение a
Console.Write("Значение __a__ = ");
Console.WriteLine(a[i].TermsSet[t].Parametrs[p]);
#endif
}
}
}
//расчитываем значение N
KnowlegeBaseSARules[] N = new KnowlegeBaseSARules[Population.Length];
for (int i = 0; i < Population.Length; i++)
{
if (i == 0)
{
N[i] = new KnowlegeBaseSARules(Population[i]);
for (int t = 0; t < N[i].TermsSet.Count; t++)
{
for (int p = 0; p < F[i].TermsSet[t].CountParams; p++)
{
N[i].TermsSet[t].Parametrs[p] = Vf * a[i].TermsSet[t].Parametrs[p];
#if debug
//выводим значение N
Console.Write("Значение __N__ = ");
Console.WriteLine(N[i].TermsSet[t].Parametrs[p]);
#endif
}
}
}
else
{
N[i] = new KnowlegeBaseSARules(Population[i]);
for (int t = 0; t < F[i].TermsSet.Count; t++)
{
for (int p = 0; p < N[i].TermsSet[t].CountParams; p++)
{
N[i].TermsSet[t].Parametrs[p] = nmax * a[i].TermsSet[t].Parametrs[p] + wn * N[i - 1].TermsSet[t].Parametrs[p];
#if debug
//выводим значение N
Console.Write("Значение __N__ = ");
Console.WriteLine(N[i].TermsSet[t].Parametrs[p]);
#endif
}
}
}
}
//расчитываем значение dX
KnowlegeBaseSARules[] dX = new KnowlegeBaseSARules[Population.Length];
for (int i = 0; i < Population.Length; i++)
{
dX[i] = new KnowlegeBaseSARules(Population[i]);
for (int t = 0; t < a[i].TermsSet.Count; t++)
{
for (int p = 0; p < a[i].TermsSet[t].CountParams; p++)
{
dX[i].TermsSet[t].Parametrs[p] = F[i].TermsSet[t].Parametrs[p] + N[i].TermsSet[t].Parametrs[p] + D;
#if debug
//выводим значение dX
Console.Write("Значение _dX__ = ");
Console.WriteLine(dX[i].TermsSet[t].Parametrs[p]);
#endif
}
}
}
//выводим значение BEST
// Console.Write("Значение BEST_ = ");
// Console.WriteLine(BEST);
//расчитываем значение X(t+dt)
for (int i = 0; i < Population.Length; i++)
{
Population[i] = new KnowlegeBaseSARules(Population[i]);
for (int t = 0; t < Population[i].TermsSet.Count; t++)
{
for (int p = 0; p < F[i].TermsSet[t].CountParams; p++)
{
Population[i].TermsSet[t].Parametrs[p] = Population[i].TermsSet[t].Parametrs[p] + calcdeltat(ct) * dX[i].TermsSet[t].Parametrs[p];
#if debug
//выводим значение Xnew
Console.Write("Знание X(t+dt) = ");
Console.WriteLine(Population[i].TermsSet[t].Parametrs[p]);
#endif
}
}
}
for (int i = 0; i < Population.Length; i++)
{
double temp = result.ErrorLearnSamples(Population[i]);
if (double.IsNaN(temp) || double.IsInfinity(temp))
{
result.UnlaidProtectionFix(Xfood);
temp = result.ErrorLearnSamples(Population[i]);
}
if (temp < bestError)
{
BEST = new KnowlegeBaseSARules(Population[i]);
bestError = temp;
}
}
double y = it;
if (y % 50 == 0 & y != 0)
{
Console.WriteLine(it);
Console.WriteLine(bestError);
}
#if debug
// выводим значение лучшей ошибки Kbest
Console.Write("Значние BestError = ");
Console.WriteLine(bestError);
Console.WriteLine(".");
#endif
}
result.RulesDatabaseSet[0] = BEST;
return(result);
}