private string ErrorInfoTSA(IFuzzySystem FS)
{
TSAFuzzySystem IFS = FS as TSAFuzzySystem;
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]));
// Console.WriteLine($"Time\t{IFS.sw.ElapsedMilliseconds} {Environment.NewLine }Ticks\t{IFS.sw.ElapsedTicks}");
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);
}
private static void writeAboutEstimates(XmlWriter writer, TSAFuzzySystem 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();
}
public override TSAFuzzySystem TuneUpFuzzySystem(TSAFuzzySystem 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 KnowlegeBaseTSARules[MCount];
Errors = new double[MCount];
mass = new double[MCount];
double ErrorBest;
KnowlegeBaseTSARules BestSolution;
double minValue;
int iminIndex;
KnowlegeBaseTSARules temp_c_Rule = new KnowlegeBaseTSARules(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 KnowlegeBaseTSARules(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 KnowlegeBaseTSARules(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];
KnowlegeBaseTSARules temp_c_Rule = new KnowlegeBaseTSARules(theFuzzySystem.RulesDatabaseSet[0]);
temp_c_Rule = new KnowlegeBaseTSARules(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;
}
}
