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);
}
private void addApproxValue(SAFuzzySystem Approx)
{
double Value = Approx.approxLearnSamples(Approx.RulesDatabaseSet[0]);
ValueLGoodsRMSE.Add(Value);
ValueLGoodsMSE.Add(Approx.RMSEtoMSEforLearn(Value));
Value = Approx.approxTestSamples(Approx.RulesDatabaseSet[0]);
ValueTGoodsRMSE.Add(Value);
ValueTGoodsMSE.Add(Approx.RMSEtoMSEforTest(Value));
Value = Approx.getComplexit();
ValueComplexityFull.Add(Value);
Value = Approx.getRulesCount();
ValueComplexityRules.Add(Value);
Value = Approx.getNormalIndex();
ValueInterpretyNominal.Add(Value);
Value = Approx.getIndexReal();
ValueInterpretyReal.Add(Value);
}
private string ErrorInfoSA(IFuzzySystem FS)
{
SAFuzzySystem IFS = FS as SAFuzzySystem;
if (IFS.RulesDatabaseSet.Count < 1)
{
return("Точность нечеткой системы недоступна");
}
approxLearnResult.Add(IFS.approxLearnSamples(IFS.RulesDatabaseSet[0]));
approxTestResult.Add(IFS.approxTestSamples(IFS.RulesDatabaseSet[0]));
approxLearnResultMSE.Add(IFS.RMSEtoMSEforLearn(approxLearnResult[approxLearnResult.Count - 1]));
approxTestResultMSE.Add(IFS.RMSEtoMSEforTest(approxTestResult[approxTestResult.Count - 1]));
approxLearnResultMSEdiv2.Add(IFS.RMSEtoMSEdiv2forLearn(approxLearnResult[approxLearnResult.Count - 1]));
approxTestResultMSEdiv2.Add(IFS.RMSEtoMSEdiv2forTest(approxTestResult[approxTestResult.Count - 1]));
return("Точностью на обучающей выборке(RSME) " + approxLearnResult [approxLearnResult.Count - 1].ToString() + " , Точность на тестовой выборке(RMSE) " + approxTestResult[approxTestResult.Count - 1].ToString() + " " + Environment.NewLine +
"Точностью на обучающей выборке(MSE) " + approxLearnResultMSE[approxLearnResultMSE.Count - 1].ToString() + " , Точность на тестовой выборке(MSE) " + approxTestResultMSE[approxTestResultMSE.Count - 1].ToString() + " " + Environment.NewLine +
"Точностью на обучающей выборке(MSE/2) " + approxLearnResultMSEdiv2[approxLearnResultMSEdiv2.Count - 1].ToString() + " , Точность на тестовой выборке(MSE/2) " + approxTestResultMSEdiv2[approxTestResultMSEdiv2.Count - 1].ToString() + " " + Environment.NewLine);
}
public override SAFuzzySystem TuneUpFuzzySystem(SAFuzzySystem Approx, ILearnAlgorithmConf conf) // Здесь ведется оптимизация вашим алгоритмом
{
theFuzzySystem = Approx;
iterMax = ((gsa_conf)conf).Количество_итераций;
MCount = ((gsa_conf)conf).Количество_частиц;
G0 = ((gsa_conf)conf).Гравитационная_постоянная;
alpha = ((gsa_conf)conf).Коэффициент_уменьшения;
epsilon = ((gsa_conf)conf).Малая_константа;
X = new KnowlegeBaseSARules[MCount];
Errors = new double[MCount];
mass = new double[MCount];
double ErrorBest;
KnowlegeBaseSARules BestSolution;
double minValue;
int iminIndex;
KnowlegeBaseSARules temp_c_Rule = new KnowlegeBaseSARules(theFuzzySystem.RulesDatabaseSet[0]);
X[0] = temp_c_Rule;
Errors[0] = theFuzzySystem.RMSEtoMSEforLearn(theFuzzySystem.approxLearnSamples(X[0]));
double ErrorZero = Errors[0];
ErrorBest = ErrorZero;
BestSolution = temp_c_Rule;
//number = X[0].TermsSet.Count * X[0].TermsSet[0].Parametrs.Count();
R = new double[MCount][, , ];
speed = new double[MCount, X[0].TermsSet.Count, X[0].TermsSet[0].Parametrs.Count()];
for (int i = 0; i < MCount; i++)
{
R[i] = new double[MCount, X[0].TermsSet.Count, X[0].TermsSet[0].Parametrs.Count()];
}
RR = new double[MCount, MCount];
a = new double[MCount, X[0].TermsSet.Count, X[0].TermsSet[0].Parametrs.Count()];
for (int i = 1; i < MCount; i++)
{
temp_c_Rule = new KnowlegeBaseSARules(theFuzzySystem.RulesDatabaseSet[0]);
X[i] = temp_c_Rule;
for (int j = 0; j < X[i].TermsSet.Count; j++)
{
for (int k = 0; k < X[i].TermsSet[j].Parametrs.Count(); k++)
{
X[i].TermsSet[j].Parametrs[k] = GaussRandom.Random_gaussian(rand, X[i].TermsSet[j].Parametrs[k], 0.1 * (X[i].TermsSet[j].Parametrs[k])) + theFuzzySystem.LearnSamplesSet.InputAttributes[X[i].TermsSet[j].NumVar].Scatter * 0.05;
}
}
theFuzzySystem.RulesDatabaseSet.Add(X[i]);
theFuzzySystem.UnlaidProtectionFix(theFuzzySystem.RulesDatabaseSet[theFuzzySystem.RulesDatabaseSet.Count - 1]);
Errors[i] = theFuzzySystem.RMSEtoMSEforLearn(theFuzzySystem.approxLearnSamples(theFuzzySystem.RulesDatabaseSet[theFuzzySystem.RulesDatabaseSet.Count - 1]));
X[i] = theFuzzySystem.RulesDatabaseSet[theFuzzySystem.RulesDatabaseSet.Count - 1];
theFuzzySystem.RulesDatabaseSet.Remove(X[i]);
}
for (int iter = 0; iter < iterMax; iter++)
{
//g(t) = G(0)*e^(-a*t/T);
G = G0 * Math.Pow(Math.E, ((-1) * alpha * iter / iterMax));
/* if (iter >= 100) {
* Console.WriteLine("Wait");
* }*/
algorithm();
for (int r = 0; r < MCount; r++)
{
theFuzzySystem.RulesDatabaseSet.Add(X[r]);
theFuzzySystem.UnlaidProtectionFix(theFuzzySystem.RulesDatabaseSet[theFuzzySystem.RulesDatabaseSet.Count - 1]);
Errors[r] = theFuzzySystem.RMSEtoMSEforLearn(theFuzzySystem.approxLearnSamples(theFuzzySystem.RulesDatabaseSet[theFuzzySystem.RulesDatabaseSet.Count - 1]));
X[r] = theFuzzySystem.RulesDatabaseSet[theFuzzySystem.RulesDatabaseSet.Count - 1];
theFuzzySystem.RulesDatabaseSet.Remove(X[r]);
}
minValue = Errors.Min();
iminIndex = Errors.ToList().IndexOf(minValue);
if (minValue < ErrorBest)
{
ErrorBest = minValue;
BestSolution = new KnowlegeBaseSARules(X[iminIndex]);
}
}
if (ErrorBest < ErrorZero)
{
theFuzzySystem.RulesDatabaseSet[0] = BestSolution;
}
return(theFuzzySystem);
}
private void weight()
{
double sum = 0;
double worst = mass[0];
double best = mass[0];
int[] index = new Int32[MCount];
int count = 0;
for (int i = 1; i < MCount; i++)
{
mass[i] = Errors[i];
if (mass[i] > worst)
{
worst = mass[i];
}
if (mass[i] < best)
{
best = mass[i];
}
}
for (int i = 0; i < MCount; i++)
{
if (mass[i] == worst)
{
count++;
index[count - 1] = i;
}
}
if (count > 1)
{
for (int i = 1; i < count; i++)
{
///X[index[i]] = ;
int f = index[i];
KnowlegeBaseSARules temp_c_Rule = new KnowlegeBaseSARules(theFuzzySystem.RulesDatabaseSet[0]);
temp_c_Rule = new KnowlegeBaseSARules(theFuzzySystem.RulesDatabaseSet[0]);
X[f] = temp_c_Rule;
for (int j = 0; j < X[f].TermsSet.Count; j++)
{
for (int k = 0; k < X[f].TermsSet[j].Parametrs.Count(); k++)
{
X[f].TermsSet[j].Parametrs[k] = GaussRandom.Random_gaussian(rand, X[f].TermsSet[j].Parametrs[k], 0.1 * (X[f].TermsSet[j].Parametrs[k])) + theFuzzySystem.LearnSamplesSet.InputAttributes[X[f].TermsSet[j].NumVar].Scatter * 0.05;
}
}
theFuzzySystem.RulesDatabaseSet.Add(X[f]);
theFuzzySystem.UnlaidProtectionFix(theFuzzySystem.RulesDatabaseSet[theFuzzySystem.RulesDatabaseSet.Count - 1]);
Errors[f] = theFuzzySystem.RMSEtoMSEforLearn(theFuzzySystem.approxLearnSamples(theFuzzySystem.RulesDatabaseSet[theFuzzySystem.RulesDatabaseSet.Count - 1]));
X[f] = theFuzzySystem.RulesDatabaseSet[theFuzzySystem.RulesDatabaseSet.Count - 1];
theFuzzySystem.RulesDatabaseSet.Remove(X[f]);
mass[f] = Errors[f];
if (mass[f] > worst)
{
i--;
}
}
}
for (int i = 0; i < MCount; i++)
{
mass[i] = (mass[i] - worst) / (best - worst);
sum = sum + mass[i];
}
for (int i = 0; i < MCount; i++)
{
mass[i] = mass[i] / sum;
}
}
protected override void make_Log(Log_line EventCall, SAFuzzySystem FS = null, string name_Alg = "", DateTime TimerValue = new DateTime(), TimeSpan TimerSpan = new TimeSpan())
{
switch (EventCall)
{
case Log_line.Start:
{
LOG += "(" + TimerValue.ToString() + ")" + " Начало построения системы" + Environment.NewLine;
break;
}
case Log_line.StartGenerate:
{
LOG += "(" + TimerValue.ToString() + ")" + " Начата генерация системы" + Environment.NewLine;
break;
}
case Log_line.StartOptimaze:
{
LOG += "(" + DateTime.Now.ToString() + ")" + " Начата оптимизация системы" + Environment.NewLine;
break;
}
case Log_line.PreGenerate_log:
{
LOG += "(" + DateTime.Now.ToString() + ")" + " Генерация алгоритмом " + name_Alg.ToString() + Environment.NewLine;
break;
}
case Log_line.PostGenerate_log:
{
double LearnResult = FS.approxLearnSamples();
double TestResult = FS.approxTestSamples();
double LearnResultMSE = FS.RMSEtoMSEforLearn(LearnResult);
double TestResultMSE = FS.RMSEtoMSEforTest(TestResult);
double LearnResultMSEdiv2 = FS.RMSEtoMSEdiv2forLearn(LearnResult);
double TestResultMSEdiv2 = FS.RMSEtoMSEdiv2forTest(TestResult);
LOG += "(" + DateTime.Now.ToString() + ")" + " Сгенерирована система сложностью " + FS.ValueComplexity().ToString() + Environment.NewLine +
"Точностью на обучающей выборке(RSME) " + LearnResult.ToString() + ", Точность на тестовой выборке(RMSE) " + TestResult.ToString() + Environment.NewLine +
"Точностью на обучающей выборке(MSE) " + LearnResultMSE.ToString() + ", Точность на тестовой выборке(MSE) " + TestResultMSE.ToString() + Environment.NewLine +
"Точностью на обучающей выборке(MSE/2) " + LearnResultMSEdiv2.ToString() + ", Точность на тестовой выборке(MSE/2) " + TestResultMSEdiv2.ToString() + Environment.NewLine;
LOG += "Использован " + name_Alg.ToString() + Environment.NewLine;
break;
}
case Log_line.PreOptimaze_log:
{
LOG += "(" + DateTime.Now.ToString() + ")" + " Оптимизация алгоритмом " + name_Alg.ToString() + Environment.NewLine;
break;
}
case Log_line.PostOptimaze_log:
{
double LearnResult = FS.approxLearnSamples();
double TestResult = FS.approxTestSamples();
double LearnResultMSE = FS.RMSEtoMSEforLearn(LearnResult);
double TestResultMSE = FS.RMSEtoMSEforTest(TestResult);
double LearnResultMSEdiv2 = FS.RMSEtoMSEdiv2forLearn(LearnResult);
double TestResultMSEdiv2 = FS.RMSEtoMSEdiv2forTest(TestResult);
LOG += "(" + DateTime.Now.ToString() + ")" + " оптимизированная система сложностью " + FS.ValueComplexity().ToString() + Environment.NewLine +
"Точностью на обучающей выборке(RMSE) " + LearnResult.ToString() + ", Точность на тестовой выборке(RMSE) " + TestResult.ToString() + Environment.NewLine +
"Точностью на обучающей выборке(MSE) " + LearnResultMSE.ToString() + ", Точность на тестовой выборке(MSE) " + TestResultMSE.ToString() + Environment.NewLine +
"Точностью на обучающей выборке(MSE/2) " + LearnResultMSEdiv2.ToString() + ", Точность на тестовой выборке(MSE/2) " + TestResultMSEdiv2.ToString() + Environment.NewLine;
LOG += "Использован " + name_Alg.ToString() + Environment.NewLine;
break;
}
case Log_line.EndCircle:
{
LOG += "(" + DateTime.Now.ToString() + ")" + " Время построения системы" + TimerSpan.TotalSeconds.ToString() + Environment.NewLine; break;
}
case Log_line.End:
{
LOG += "(" + DateTime.Now.ToString() + ")" + " Время построения всех систем" + TimerSpan.TotalSeconds.ToString() + Environment.NewLine; break;
}
default: { LOG += "Не верный вызов" + Environment.NewLine; break; }
}
}
protected static void writeAboutEstimates(XmlWriter writer, SAFuzzySystem Approximate)
{
writer.WriteStartElement("Estimates");
if (Approximate.TestSamplesSet != null)
{
writer.WriteAttributeString("Count", XmlConvert.ToString(22));
writer.WriteStartElement("Estimate");
writer.WriteAttributeString("Table", Approximate.LearnSamplesSet.FileName);
writer.WriteAttributeString("Type", "RMSE");
writer.WriteAttributeString("Value", XmlConvert.ToString(Approximate.approxLearnSamples(Approximate.RulesDatabaseSet[0])));
writer.WriteEndElement();
writer.WriteStartElement("Estimate");
writer.WriteAttributeString("Table", Approximate.LearnSamplesSet.FileName);
writer.WriteAttributeString("Type", "MSE");
writer.WriteAttributeString("Value", XmlConvert.ToString(Approximate.RMSEtoMSEforLearn(Approximate.approxLearnSamples(Approximate.RulesDatabaseSet[0]))));
writer.WriteEndElement();
writer.WriteStartElement("Estimate");
writer.WriteAttributeString("Table", Approximate.TestSamplesSet.FileName);
writer.WriteAttributeString("Type", "RMSE");
writer.WriteAttributeString("Value", XmlConvert.ToString(Approximate.approxTestSamples(Approximate.RulesDatabaseSet[0])));
writer.WriteEndElement();
writer.WriteStartElement("Estimate");
writer.WriteAttributeString("Table", Approximate.TestSamplesSet.FileName);
writer.WriteAttributeString("Type", "MSE");
writer.WriteAttributeString("Value", XmlConvert.ToString(Approximate.RMSEtoMSEforTest(Approximate.approxTestSamples(Approximate.RulesDatabaseSet[0]))));
writer.WriteEndElement();
}
else
{
writer.WriteAttributeString("Count", XmlConvert.ToString(20));
writer.WriteStartElement("Estimate");
writer.WriteAttributeString("Table", Approximate.LearnSamplesSet.FileName);
writer.WriteAttributeString("Type", "RMSE");
writer.WriteAttributeString("Value", XmlConvert.ToString(Approximate.approxLearnSamples(Approximate.RulesDatabaseSet[0])));
writer.WriteEndElement();
writer.WriteStartElement("Estimate");
writer.WriteAttributeString("Table", Approximate.LearnSamplesSet.FileName);
writer.WriteAttributeString("Type", "MSE");
writer.WriteAttributeString("Value", XmlConvert.ToString(Approximate.RMSEtoMSEforLearn(Approximate.approxLearnSamples(Approximate.RulesDatabaseSet[0]))));
writer.WriteEndElement();
}
writer.WriteStartElement("Estimate");
writer.WriteAttributeString("Type", "GIBNormal");
writer.WriteAttributeString("Value", XmlConvert.ToString(Approximate.getGIBNormal()));
writer.WriteEndElement();
writer.WriteStartElement("Estimate");
writer.WriteAttributeString("Type", "GIBSumStraigh");
writer.WriteAttributeString("Value", XmlConvert.ToString(Approximate.getGIBSumStrait()));
writer.WriteEndElement();
writer.WriteStartElement("Estimate");
writer.WriteAttributeString("Type", "GIBSumReverse");
writer.WriteAttributeString("Value", XmlConvert.ToString(Approximate.getGIBSumReverse()));
writer.WriteEndElement();
writer.WriteStartElement("Estimate");
writer.WriteAttributeString("Type", "GICNormal");
writer.WriteAttributeString("Value", XmlConvert.ToString(Approximate.getGICNormal()));
writer.WriteEndElement();
writer.WriteStartElement("Estimate");
writer.WriteAttributeString("Type", "GICSumStraigh");
writer.WriteAttributeString("Value", XmlConvert.ToString(Approximate.getGICSumStraigth()));
writer.WriteEndElement();
writer.WriteStartElement("Estimate");
writer.WriteAttributeString("Type", "GICSumReverse");
writer.WriteAttributeString("Value", XmlConvert.ToString(Approximate.getGICSumReverce()));
writer.WriteEndElement();
writer.WriteStartElement("Estimate");
writer.WriteAttributeString("Type", "GISNormal");
writer.WriteAttributeString("Value", XmlConvert.ToString(Approximate.getGISNormal()));
writer.WriteEndElement();
writer.WriteStartElement("Estimate");
writer.WriteAttributeString("Type", "GISSumStraigh");
writer.WriteAttributeString("Value", XmlConvert.ToString(Approximate.getGISSumStraigt()));
writer.WriteEndElement();
writer.WriteStartElement("Estimate");
writer.WriteAttributeString("Type", "GISSumReverce");
writer.WriteAttributeString("Value", XmlConvert.ToString(Approximate.getGISSumReverce()));
writer.WriteEndElement();
writer.WriteStartElement("Estimate");
writer.WriteAttributeString("Type", "LindisNormal");
writer.WriteAttributeString("Value", XmlConvert.ToString(Approximate.getLindisNormal()));
writer.WriteEndElement();
writer.WriteStartElement("Estimate");
writer.WriteAttributeString("Type", "LindisSumStraigh");
writer.WriteAttributeString("Value", XmlConvert.ToString(Approximate.getLindisSumStraight()));
writer.WriteEndElement();
writer.WriteStartElement("Estimate");
writer.WriteAttributeString("Type", "LindisSumReverse");
writer.WriteAttributeString("Value", XmlConvert.ToString(Approximate.getLindisSumReverse()));
writer.WriteEndElement();
writer.WriteStartElement("Estimate");
writer.WriteAttributeString("Type", "NormalIndex");
writer.WriteAttributeString("Value", XmlConvert.ToString(Approximate.getNormalIndex()));
writer.WriteEndElement();
writer.WriteStartElement("Estimate");
writer.WriteAttributeString("Type", "RealIndex");
writer.WriteAttributeString("Value", XmlConvert.ToString(Approximate.getIndexReal()));
writer.WriteEndElement();
writer.WriteStartElement("Estimate");
writer.WriteAttributeString("Type", "SumStraigthIndex");
writer.WriteAttributeString("Value", XmlConvert.ToString(Approximate.getIndexSumStraigt()));
writer.WriteEndElement();
writer.WriteStartElement("Estimate");
writer.WriteAttributeString("Type", "SumReverseIndex");
writer.WriteAttributeString("Value", XmlConvert.ToString(Approximate.getIndexSumReverse()));
writer.WriteEndElement();
writer.WriteStartElement("Estimate");
writer.WriteAttributeString("Type", "ComplexitIt");
writer.WriteAttributeString("Value", XmlConvert.ToString(Approximate.getComplexit()));
writer.WriteEndElement();
writer.WriteStartElement("Estimate");
writer.WriteAttributeString("Type", "CountRules");
writer.WriteAttributeString("Value", XmlConvert.ToString(Approximate.getRulesCount()));
writer.WriteEndElement();
writer.WriteEndElement();
}
