public virtual void preIterate(PCFuzzySystem Classify, ILearnAlgorithmConf conf)
{
result = Classify;
Init(conf);
HeadLeader = new KnowlegeBasePCRules(result.RulesDatabaseSet[0]);
VelocityVector = new KnowlegeBasePCRules(result.RulesDatabaseSet[0]);
VelocityVectorLL = new KnowlegeBasePCRules(result.RulesDatabaseSet[0]);
VelocityVectorHL = new KnowlegeBasePCRules(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 <KnowlegeBasePCRules, KnowlegeBasePCRules>();
foreach (var Particle in Population)
{
ParticlesBest.Add(Particle, Universal);
}
LocalLeaders = new KnowlegeBasePCRules[numberOfLocalLeaders];
ExplorerParticles = new KnowlegeBasePCRules[numberOfAllParts - numberOfAimlessParts - numberOfLocalLeaders - 1];
AimlessParticles = new KnowlegeBasePCRules[numberOfAimlessParts];
iter = 0;
Population = ListPittsburgClassifierTool.SortRules(Population, result);
}
public override PCFuzzySystem TuneUpFuzzySystem(PCFuzzySystem Classify, ILearnAlgorithmConf conf)
{
preIterate(Classify, conf);
//Оптимизируем параметры
iter = 1;
while (iter <= MaxIter)
{
if (iter % changeParts == 0)
{
swapParticles();
}
oneIterate();
}
//Выводим точность классификации для лучшей частицы из каждой популяции
for (int j = 0; j < Populations.Count; j++)
{
Populations[j] = ListPittsburgClassifierTool.SortRules(Populations[j], result);
Console.WriteLine("Популяция №" + j + ":");
Console.WriteLine("Обуч: " + Math.Round(result.ClassifyLearnSamples(Populations[j][0]), 2));
Console.WriteLine("Тест: " + Math.Round(result.ClassifyTestSamples(Populations[j][0]), 2));
}
//Находим самое лучшее решение из всех популяций
List <KnowlegeBasePCRules> BestPopulation = new List <KnowlegeBasePCRules>();
for (int i = 0; i < numberOfPopulations; i++)
{
BestPopulation.Add(Populations[i][0]);
}
BestPopulation = ListPittsburgClassifierTool.SortRules(BestPopulation, result);
result.RulesDatabaseSet[0] = BestPopulation[0];
//Возвращаем результат
return(result);
}
public override PCFuzzySystem TuneUpFuzzySystem(PCFuzzySystem Classifier, ILearnAlgorithmConf conf)
{
result = Classifier;
groups = new List <int[]>();
Init(conf);
SetPopulation();
Population = SortRules(Population);
NS = new int[m];
for (int i = 0; i < m; i++)
{
NS[i] = (N - 1) / m;
}
cur_iter = 0;
while (cur_iter < iter)
{
groups = GroupStream();
if (p_one > rand.NextDouble())
{
ChooseOneCluster();
}
else
{
ChooseTwoClusters();
}
Population = ListPittsburgClassifierTool.SortRules(Population, result);
Console.WriteLine(cur_iter + " - Итерация");
Console.WriteLine("Обуч. выборка = " + result.ErrorLearnSamples(Population[0]));
Console.WriteLine("Тест. выборка = " + result.ErrorTestSamples(Population[0]));
cur_iter++;
}
Population = ListPittsburgClassifierTool.SortRules(Population, result);
result.RulesDatabaseSet[0] = Population[0];
return(result);
}
public virtual void oneIterate()
{
for (int p_i = 0; p_i < Populations.Count; p_i++)
{
Populations[p_i] = ListPittsburgClassifierTool.SortRules(Populations[p_i], result);
SetRoles(Populations[p_i]);
ChangeExplorersPositions();
ChangeAimlessPositions();
Populations[p_i] = DiscardRoles(Populations[p_i]);
}
iter++;
}
public virtual void oneIterate()
{
Task[] AlgTasks = new Task[numberOfPopulations];
Parallel.For(0, Populations.Count, i =>
{
int p_i = i;
Populations[p_i] = ListPittsburgClassifierTool.SortRules(Populations[p_i], result);
SetRoles(Populations[p_i], p_i);
ChangeExplorersPositions(p_i);
ChangeAimlessPositions(p_i);
Populations[p_i] = DiscardRoles(Populations[p_i], p_i);
});
iter++;
}
public virtual void oneIterate()
{
SetRoles();
ChangeExplorersPositions();
ChangeAimlessPositions();
DiscardRoles();
Population = ListPittsburgClassifierTool.SortRules(Population, result);
iter++;
if (iter == MaxIter)
{
Console.WriteLine("Обуч: " + Math.Round(result.ClassifyLearnSamples(Population[0]), 2));
Console.WriteLine("Тест: " + Math.Round(result.ClassifyTestSamples(Population[0]), 2));
//Console.WriteLine();
}
}
//основные вычисления
public override PCFuzzySystem TuneUpFuzzySystem(PCFuzzySystem Classify, ILearnAlgorithmConf conf)
{
rand = new Random();
result = Classify;
Init(conf);
SetPopulation();
KnowlegeBasePCRules BEST = new KnowlegeBasePCRules(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 = ListPittsburgClassifierTool.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 KnowlegeBasePCRules(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
KnowlegeBasePCRules[] Bfood = new KnowlegeBasePCRules[Population.Length];
for (int i = 0; i < Population.Length; i++)
{
Bfood[i] = new KnowlegeBasePCRules(Population[i]);
double KRoofifood = CalcKroof(K[i], Kfood);
KnowlegeBasePCRules Xroofifood = new KnowlegeBasePCRules(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
KnowlegeBasePCRules[] Bbest = new KnowlegeBasePCRules[Population.Length];
for (int i = 0; i < Population.Length; i++)
{
Bbest[i] = new KnowlegeBasePCRules(Population[i]);
double KRoofifood = CalcKroof(K[i], K[ibest]);
KnowlegeBasePCRules Xroofifood = new KnowlegeBasePCRules(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
KnowlegeBasePCRules[] B = new KnowlegeBasePCRules[Population.Length];
for (int i = 0; i < Population.Length; i++)
{
B[i] = new KnowlegeBasePCRules(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
KnowlegeBasePCRules[] F = new KnowlegeBasePCRules[Population.Length];
for (int i = 0; i < Population.Length; i++)
{
if (i == 0)
{
F[i] = new KnowlegeBasePCRules(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 KnowlegeBasePCRules(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
KnowlegeBasePCRules[] alocal = new KnowlegeBasePCRules[Population.Length];
for (int i = 0; i < Population.Length; i++)
{
alocal[i] = new KnowlegeBasePCRules(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]]);
KnowlegeBasePCRules XRoofij = new KnowlegeBasePCRules(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
KnowlegeBasePCRules[] atarget = new KnowlegeBasePCRules[Population.Length];
for (int i = 0; i < Population.Length; i++)
{
atarget[i] = new KnowlegeBasePCRules(Population[i]);
double KRoofibest = CalcKroof(K[i], K[ibest]);
KnowlegeBasePCRules XRoofibest = new KnowlegeBasePCRules(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
KnowlegeBasePCRules[] a = new KnowlegeBasePCRules[Population.Length];
for (int i = 0; i < Population.Length; i++)
{
a[i] = new KnowlegeBasePCRules(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
KnowlegeBasePCRules[] N = new KnowlegeBasePCRules[Population.Length];
for (int i = 0; i < Population.Length; i++)
{
if (i == 0)
{
N[i] = new KnowlegeBasePCRules(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 KnowlegeBasePCRules(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
KnowlegeBasePCRules[] dX = new KnowlegeBasePCRules[Population.Length];
for (int i = 0; i < Population.Length; i++)
{
dX[i] = new KnowlegeBasePCRules(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 KnowlegeBasePCRules(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 KnowlegeBasePCRules(Population[i]);
bestError = temp;
}
}
double y = it;
if (y % 10 == 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);
}
public override PCFuzzySystem TuneUpFuzzySystem(PCFuzzySystem Classifier, ILearnAlgorithmConf conf)
{
result = Classifier;
//Узнаем название папки с данными
string path_name = "../../OLD/Data/Keel/Classifier/KEEL-10/";
string folder_name = "";
foreach (var letter in result.LearnSamplesSet.FileName)
{
if (letter != '-')
{
folder_name += letter;
}
else
{
break;
}
}
groups = new List <int[]>();
Init(conf);
//Создаем новые обучающую и тестовую выбоки и удаляем из них некоторое количество случайных элементов
List <PCFuzzySystem> results = new List <PCFuzzySystem>();
for (int i = 0; i < numberOfPopulations; i++)
{
SampleSet new_learn = new SampleSet(path_name + folder_name + "/" + result.LearnSamplesSet.FileName);
SampleSet new_test = new SampleSet(path_name + folder_name + "/" + result.TestSamplesSet.FileName);
results.Add(new PCFuzzySystem(new_learn, new_test));
int ground = (int)Math.Round(results[i].LearnSamplesSet.DataRows.Count * 0.25);
for (int j = 0; j < ground; j++)
{
results[i].LearnSamplesSet.DataRows.RemoveAt(rand.Next(0, results[i].LearnSamplesSet.DataRows.Count));
}
}
Populations = new List <List <KnowlegeBasePCRules> >();
for (int i = 0; i < numberOfPopulations; i++)
{
Populations.Add(SetPopulation(new List <KnowlegeBasePCRules>()));
Populations[i] = ListPittsburgClassifierTool.SortRules(Populations[i], result);
}
NS = new int[m];
for (int i = 0; i < m; i++)
{
NS[i] = (N - 1) / m;
}
cur_iter = 0;
while (cur_iter < iter)
{
for (int p_i = 0; p_i < Populations.Count; p_i++)
{
groups = GroupStream(Populations[p_i]);
if (p_one > rand.NextDouble())
{
ChooseOneCluster(Populations[p_i]);
}
else
{
ChooseTwoClusters(Populations[p_i]);
}
Populations[p_i] = ListPittsburgClassifierTool.SortRules(Populations[p_i], results[p_i]);
//Console.WriteLine(cur_iter + " - Итерация");
//Console.WriteLine("Обуч. выборка = " + result.ErrorLearnSamples(Populations[p_i][0]));
//Console.WriteLine("Тест. выборка = " + result.ErrorTestSamples(Populations[p_i][0]));
}
cur_iter++;
}
//Выводим точность классификации для лучшей частицы из каждой популяции
for (int j = 0; j < Populations.Count; j++)
{
Populations[j] = ListPittsburgClassifierTool.SortRules(Populations[j], results[j]);
Console.WriteLine("Популяция №" + j + ":");
Console.WriteLine("Обуч: " + Math.Round(result.ClassifyLearnSamples(Populations[j][0]), 2));
Console.WriteLine("Тест: " + Math.Round(result.ClassifyTestSamples(Populations[j][0]), 2));
}
//Допобавляем в базу правил лучшие решения
if (result.RulesDatabaseSet.Count == 1)
{
result.RulesDatabaseSet.Clear();
}
for (int i = 0; i < Populations.Count; i++)
{
result.RulesDatabaseSet.Add(Populations[i][0]);
}
//Возвращаем результат
return(result);
}
public override PCFuzzySystem TuneUpFuzzySystem(PCFuzzySystem Classify, ILearnAlgorithmConf conf)
{
result = Classify;
//Узнаем название папки с данными
string path_name = "../../OLD/Data/Keel/Classifier/KEEL-10/";
string folder_name = "";
foreach (var letter in result.LearnSamplesSet.FileName)
{
if (letter != '-')
{
folder_name += letter;
}
else
{
break;
}
}
//Инициализируем параметры
Init(conf);
//Создаем новые обучающую и тестовую выбоки и удаляем из них некоторое количество случайных элементов
List <PCFuzzySystem> results = new List <PCFuzzySystem>();
for (int i = 0; i < numberOfPopulations; i++)
{
SampleSet new_learn = new SampleSet(path_name + folder_name + "/" + result.LearnSamplesSet.FileName);
SampleSet new_test = new SampleSet(path_name + folder_name + "/" + result.TestSamplesSet.FileName);
results.Add(new PCFuzzySystem(new_learn, new_test));
int ground = (int)Math.Round(results[i].LearnSamplesSet.DataRows.Count * 0.25);
for (int j = 0; j < ground; j++)
{
results[i].LearnSamplesSet.DataRows.RemoveAt(rand.Next(0, results[i].LearnSamplesSet.DataRows.Count));
}
}
//Инициализируем и зануляем вектора алгоритма
HeadLeader = new KnowlegeBasePCRules(result.RulesDatabaseSet[0]);
VelocityVector = new KnowlegeBasePCRules(result.RulesDatabaseSet[0]);
VelocityVectorLL = new KnowlegeBasePCRules(result.RulesDatabaseSet[0]);
VelocityVectorHL = new KnowlegeBasePCRules(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;
}
}
//Создаем популяции и архив лучших положений каждой частицы
Populations = new List <List <KnowlegeBasePCRules> >();
for (int i = 0; i < numberOfPopulations; i++)
{
Populations.Add(SetPopulation(new List <KnowlegeBasePCRules>()));
}
ParticlesBest = new Dictionary <KnowlegeBasePCRules, KnowlegeBasePCRules>();
foreach (var Population in Populations)
{
foreach (var Particle in Population)
{
ParticlesBest.Add(Particle, Universal);
}
}
//Инициализируем роли частиц
LocalLeaders = new KnowlegeBasePCRules[numberOfLocalLeaders];
ExplorerParticles = new KnowlegeBasePCRules[numberOfAllParts - numberOfAimlessParts - numberOfLocalLeaders - 1];
AimlessParticles = new KnowlegeBasePCRules[numberOfAimlessParts];
//Оптимизируем параметры
iter = 1;
while (iter < MaxIter)
{
for (int p_i = 0; p_i < Populations.Count; p_i++)
{
Populations[p_i] = ListPittsburgClassifierTool.SortRules(Populations[p_i], results[p_i]);
SetRoles(Populations[p_i]);
ChangeExplorersPositions();
ChangeAimlessPositions();
Populations[p_i] = DiscardRoles(Populations[p_i]);
}
iter++;
}
//Выводим точность классификации для лучшей частицы из каждой популяции
for (int j = 0; j < Populations.Count; j++)
{
Populations[j] = ListPittsburgClassifierTool.SortRules(Populations[j], results[j]);
Console.WriteLine("Популяция №" + j + ":");
Console.WriteLine("Обуч: " + Math.Round(result.ClassifyLearnSamples(Populations[j][0]), 2));
Console.WriteLine("Тест: " + Math.Round(result.ClassifyTestSamples(Populations[j][0]), 2));
}
//Допобавляем в базу правил лучшие решения
if (result.RulesDatabaseSet.Count == 1)
{
result.RulesDatabaseSet.Clear();
}
for (int i = 0; i < Populations.Count; i++)
{
result.RulesDatabaseSet.Add(Populations[i][0]);
}
//Возвращаем результат
return(result);
}
public void oneIterate()
{
for (int p_i = 0; p_i < Populations.Count; p_i++)
{
//расчитыавем значение фитнес-функции
Populations[p_i] = ListPittsburgClassifierTool.SortRules(Populations[p_i], results[p_i]);
double[] K = new double[Populations[p_i].Count];
for (int i = 0; i < Populations[p_i].Count; i++)
{
K[i] = results[p_i].ErrorLearnSamples(Populations[p_i][i]);
if (double.IsNaN(K[i]) || double.IsInfinity(K[i]) || K[i] == 100)
{
results[p_i].UnlaidProtectionFix(Populations[p_i][i]);
K[i] = results[p_i].ErrorLearnSamples(Populations[p_i][i]);
}
}
Kworst = K.Max();
if (double.IsNaN(Kworst) || double.IsInfinity(Kworst))
{
int iworst = K.ToList().IndexOf(Kworst);
}
Kbest = K.Min();
int ibest = K.ToList().IndexOf(Kbest);
//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);
//расчитыавем значение Xfood
double divide = K.Select(x => 1 / x).ToList().Sum();
var Xfood = new KnowlegeBasePCRules(Populations[p_i][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 < Populations[p_i].Count; i++)
{
Xfood.TermsSet[t].Parametrs[p] += Populations[p_i][i].TermsSet[t].Parametrs[p] / K[i];
}
Xfood.TermsSet[t].Parametrs[p] /= divide;
}
}
//расчитываем значение Kfood
double Kfood = results[p_i].ErrorLearnSamples(Xfood);
if (double.IsNaN(Kfood) || double.IsInfinity(Kfood) || Kfood == 100)
{
results[p_i].UnlaidProtectionFix(Xfood);
Kfood = results[p_i].ErrorLearnSamples(Xfood);
}
//расчитываем значение Cfood
double Cfood = 2 * (1 - (dit / diter));
//расчитываем значение Bfood
KnowlegeBasePCRules[] Bfood = new KnowlegeBasePCRules[Populations[p_i].Count];
for (int i = 0; i < Populations[p_i].Count; i++)
{
Bfood[i] = new KnowlegeBasePCRules(Populations[p_i][i]);
double KRoofifood = CalcKroof(K[i], Kfood);
KnowlegeBasePCRules Xroofifood = new KnowlegeBasePCRules(CalcXroof(Populations[p_i][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];
}
}
}
//расчитываем значение Bbest
KnowlegeBasePCRules[] Bbest = new KnowlegeBasePCRules[Populations[p_i].Count];
for (int i = 0; i < Populations[p_i].Count; i++)
{
Bbest[i] = new KnowlegeBasePCRules(Populations[p_i][i]);
double KRoofifood;
if (i != ibest)
{
KRoofifood = CalcKroof(K[i], K[ibest]);
}
else
{
KRoofifood = 1.0;
}
KnowlegeBasePCRules Xroofifood = new KnowlegeBasePCRules(CalcXroof(Populations[p_i][i], Populations[p_i][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];
}
}
}
//расчитываем значение B
KnowlegeBasePCRules[] B = new KnowlegeBasePCRules[Populations[p_i].Count];
for (int i = 0; i < Populations[p_i].Count; i++)
{
B[i] = new KnowlegeBasePCRules(Populations[p_i][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];
}
}
}
//расчитываем значение F
KnowlegeBasePCRules[] F = new KnowlegeBasePCRules[Populations[p_i].Count];
for (int i = 0; i < Populations[p_i].Count; i++)
{
if (i == 0)
{
F[i] = new KnowlegeBasePCRules(Populations[p_i][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];
}
}
}
else
{
F[i] = new KnowlegeBasePCRules(Populations[p_i][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];
}
}
}
}
List <int>[] neihbors = new List <int> [Populations[p_i].Count];
//расчитываем значение alocal
KnowlegeBasePCRules[] alocal = new KnowlegeBasePCRules[Populations[p_i].Count];
for (int i = 0; i < Populations[p_i].Count; i++)
{
alocal[i] = new KnowlegeBasePCRules(Populations[p_i][i]);
neihbors[i] = countneihbors(Populations[p_i][i], Populations[p_i]);
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]]);
KnowlegeBasePCRules XRoofij = new KnowlegeBasePCRules(CalcXroof(Populations[p_i][i], Populations[p_i][neihbors[i][j]]));
alocal[i].TermsSet[t].Parametrs[p] += KRoofij * XRoofij.TermsSet[t].Parametrs[p];
}
}
}
}
//расчитываем значение Cbest
double Cbest = 2 * (rand.NextDouble() - (dit / diter));
//расчитываем значение rand для Cbest
double rand2;
rand2 = it / iter - Cbest / 2;
//расчитываем значение atarget
KnowlegeBasePCRules[] atarget = new KnowlegeBasePCRules[Populations[p_i].Count];
for (int i = 0; i < Populations[p_i].Count; i++)
{
atarget[i] = new KnowlegeBasePCRules(Populations[p_i][i]);
double KRoofibest = CalcKroof(K[i], K[ibest]);
KnowlegeBasePCRules XRoofibest = new KnowlegeBasePCRules(CalcXroof(Populations[p_i][i], Populations[p_i][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];
}
}
}
//расчитываем значение a
KnowlegeBasePCRules[] a = new KnowlegeBasePCRules[Populations[p_i].Count];
for (int i = 0; i < Populations[p_i].Count; i++)
{
a[i] = new KnowlegeBasePCRules(Populations[p_i][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];
}
}
}
//расчитываем значение N
KnowlegeBasePCRules[] N = new KnowlegeBasePCRules[Populations[p_i].Count];
for (int i = 0; i < Populations[p_i].Count; i++)
{
if (i == 0)
{
N[i] = new KnowlegeBasePCRules(Populations[p_i][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];
}
}
}
else
{
N[i] = new KnowlegeBasePCRules(Populations[p_i][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];
}
}
}
}
//расчитываем значение dX
KnowlegeBasePCRules[] dX = new KnowlegeBasePCRules[Populations[p_i].Count];
for (int i = 0; i < Populations[p_i].Count; i++)
{
dX[i] = new KnowlegeBasePCRules(Populations[p_i][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;
}
}
}
for (int i = 0; i < Populations[p_i].Count; i++)
{
Populations[p_i][i] = new KnowlegeBasePCRules(Populations[p_i][i]);
for (int t = 0; t < Populations[p_i][i].TermsSet.Count; t++)
{
for (int p = 0; p < F[i].TermsSet[t].CountParams; p++)
{
Populations[p_i][i].TermsSet[t].Parametrs[p] = Populations[p_i][i].TermsSet[t].Parametrs[p] + calcdeltat(ct) * dX[i].TermsSet[t].Parametrs[p];
}
}
}
for (int i = 0; i < Populations[p_i].Count; i++)
{
double temp = result.ErrorLearnSamples(Populations[p_i][i]);
if (double.IsNaN(temp) || double.IsInfinity(temp) || temp == 100)
{
result.UnlaidProtectionFix(Xfood);
temp = result.ErrorLearnSamples(Populations[p_i][i]);
}
if (temp < bestError[p_i])
{
BEST[p_i] = new KnowlegeBasePCRules(Populations[p_i][i]);
bestError[p_i] = temp;
}
}
}
}
