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)
{
iskl_prizn = "";
count_iteration = ((Param)conf).Количество_итераций;
count_populate = ((Param)conf).Число_осколков;
exploration = ((Param)conf).Фактор_исследования;
reduce_koef = ((Param)conf).Уменьшающий_коэффициент;
priznaki_usech = ((Param)conf).Усечённые_признаки;
int iter = 0, i, j, count_terms;
int count_cons;
double RMSE_best, cosFi, RMSE_best2, MSEbefore, MSEafter;
int Nd, variables, k = 1, best = 0;
string[] buf;
buf = priznaki_usech.Split(' ');
TSAFuzzySystem result = Approx;
int type = Approx.RulesDatabaseSet[0].TermsSet[0].CountParams;
Nd = Approx.RulesDatabaseSet[0].TermsSet.Count * type;
double[] X_best = new double[Nd + 1];
double[,] X_pred = new double[2, Nd + 1];
double[,] direction = new double[count_populate, Nd + 1];
double[,] d = new double[count_populate, Nd + 1];
double[,] explosion = new double[count_populate, Nd + 1];
double[,] shrapnel = new double[count_populate, Nd + 1];
cosFi = Math.Cos(2 * Math.PI / count_populate);
RMSE_best = Approx.approxLearnSamples(0);
RMSE_best2 = Approx.approxLearnSamples(0);
count_cons = Approx.RulesDatabaseSet[0].all_conq_of_rules.Count();
double[] RMSE = new double[count_populate];
double[] RMSE_all = new double[iter];
double[] RMSE_tst = new double[count_populate];
double[] RMSE2 = new double[count_populate];
double[] RMSE_pred = new double[2];
double[] cons_best = new double[count_cons];
count_terms = Approx.RulesDatabaseSet[0].TermsSet.Count;
variables = Approx.LearnSamplesSet.CountVars;
int[] terms = new int[variables];
double[] X_best2 = new double[variables];
double[,] d3 = new double[count_populate, variables];
double[,] priznak = new double[count_populate, variables];
for (i = 0; i < variables; i++)
{
priznak[0, i] = 1;
X_best2[i] = 1;
}
KnowlegeBaseTSARules[] X = new KnowlegeBaseTSARules[count_populate];
for (int s = 0; s < count_populate - 1; s++)
{
X[s] = new KnowlegeBaseTSARules(Approx.RulesDatabaseSet[0]);
Approx.RulesDatabaseSet.Add(X[s]);
}
if (buf[0] != "")
{
for (k = 0; k < buf.Count(); k++)
{
Approx.AcceptedFeatures[int.Parse(buf[k]) - 1] = false;
priznak[0, int.Parse(buf[k]) - 1] = 0;
iskl_prizn += buf[k] + " ";
}
}
RMSE_best = Approx.approxLearnSamples(0);
for (iter = 0; iter <= count_iteration; iter++)
{
best = 0;
if (iter == 0)
{
k = 1;
for (int h = 0; h < count_terms; h++)
{
for (int p = 0; p < type; p++)
{
shrapnel[0, k] = Approx.RulesDatabaseSet[0].TermsSet[h].Parametrs[p];
X_best[k] = shrapnel[0, k];
X_pred[0, k] = shrapnel[0, k];
X_pred[1, k] = shrapnel[0, k];
k++;
}
}
RMSE_pred[0] = Approx.approxLearnSamples(0);
RMSE_pred[1] = Approx.approxLearnSamples(0);
k = 1;
for (int h = 0; h < count_terms; h++)
{
for (int p = 0; p < type; p++)
{
d[0, k] = RandomNext(Approx.LearnSamplesSet.InputAttribute(Approx.RulesDatabaseSet[0].TermsSet[h].NumberOfInputVar).Min, Approx.LearnSamplesSet.InputAttribute(Approx.RulesDatabaseSet[0].TermsSet[h].NumberOfInputVar).Max);
k++;
}
}
}
for (i = 1; i <= Nd; i++)
{
if (exploration > iter)
{
for (j = 1; j < count_populate; j++)
{
int sum = 0, sum2 = 0;
generate:
sum++;
sum2++;
//формула расстояния исправлена
d[j, i] = d[j - 1, i] * randn();
//double sluch = randn();
//if (sluch < 0) d[j, i] = d[j - 1, i] * (-1) * Math.Pow(sluch, 2);
//else d[j, i] = d[j - 1, i] * Math.Pow(sluch, 2);
explosion[j, i] = d[j, i] * cosFi;
if (sum > 20)
{
if ((i + (type - 2)) % type == 0)
{
shrapnel[j, i] = (shrapnel[0, i] + explosion[j, i]) + (shrapnel[j, i - 1] - shrapnel[j, i]);
if (sum2 > 2)
{
shrapnel[j, i] = (shrapnel[0, i] + explosion[j, i]) + (shrapnel[j, i - type] - shrapnel[j, i]);
sum = 19;
}
if (sum2 > 3)
{
shrapnel[j, i] = (shrapnel[0, i] + explosion[j, i]) + (shrapnel[j, i - type] - shrapnel[j, i]) + (shrapnel[j, i - 1] - shrapnel[j, i]);
sum = 19;
sum2 = 0;
}
}
else
{
shrapnel[j, i] = (shrapnel[0, i] + explosion[j, i]) + (shrapnel[j, i - 1] - shrapnel[j, i]);
sum = 19;
}
}
else
{
shrapnel[j, i] = shrapnel[0, i] + explosion[j, i];
}
if ((i == 2) || (i == 1))
{
shrapnel[j, i] = Approx.LearnSamplesSet.InputAttribute(Approx.RulesDatabaseSet[0].TermsSet[(int)(i - 1) / type].NumberOfInputVar).Min;
}
if (Approx.RulesDatabaseSet[0].TermsSet[(int)((i - 1) / type)].NumberOfInputVar != Approx.RulesDatabaseSet[0].TermsSet[(int)((i - 2) / type)].NumberOfInputVar)
{
shrapnel[j, i] = Approx.LearnSamplesSet.InputAttribute(Approx.RulesDatabaseSet[0].TermsSet[(int)(i - 1) / type].NumberOfInputVar).Min; goto exit;
}
if (Approx.RulesDatabaseSet[0].TermsSet[(int)((i - 1) / type)].NumberOfInputVar != Approx.RulesDatabaseSet[0].TermsSet[(int)((i - type) / type)].NumberOfInputVar)
{
shrapnel[j, i] = Approx.LearnSamplesSet.InputAttribute(Approx.RulesDatabaseSet[0].TermsSet[(int)(i - 1) / type].NumberOfInputVar).Min; goto exit;
}
if (Approx.RulesDatabaseSet[0].TermsSet[(int)((i - 1) / type)].NumberOfInputVar != (variables - 1))
{
if (Approx.RulesDatabaseSet[0].TermsSet[(int)((i - 1) / type)].NumberOfInputVar != Approx.RulesDatabaseSet[0].TermsSet[(int)((i) / type)].NumberOfInputVar)
{
shrapnel[j, i] = Approx.LearnSamplesSet.InputAttribute(Approx.RulesDatabaseSet[0].TermsSet[(int)(i - 1) / type].NumberOfInputVar).Max; goto exit;
}
if (Approx.RulesDatabaseSet[0].TermsSet[(int)((i - 1) / type)].NumberOfInputVar != Approx.RulesDatabaseSet[0].TermsSet[(int)((i + 1) / type)].NumberOfInputVar)
{
shrapnel[j, i] = Approx.LearnSamplesSet.InputAttribute(Approx.RulesDatabaseSet[0].TermsSet[(int)(i - 1) / type].NumberOfInputVar).Max; goto exit;
}
}
if (Approx.RulesDatabaseSet[0].TermsSet[(int)((i - 1) / type)].NumberOfInputVar == (variables - 1))
{
if ((i == (count_terms * 3 - 1)) || (i == (count_terms * 3)))
{
shrapnel[j, i] = Approx.LearnSamplesSet.InputAttribute(Approx.RulesDatabaseSet[0].TermsSet[(int)(i - 1) / type].NumberOfInputVar).Max;
}
}
if (((i + (type - 2)) % type == 0) && (shrapnel[j, i] < shrapnel[j, i - 1]))
{
if (shrapnel[j, i] == Approx.LearnSamplesSet.InputAttribute(Approx.RulesDatabaseSet[0].TermsSet[(int)(i / type)].NumberOfInputVar).Min)
{
i--;
}
goto generate;
}
if ((i % type == 0) && (shrapnel[j, i] < shrapnel[j, i - 1]))
{
goto generate;
}
if (i != 1)
{
if (((i - (type - 2)) % type == 0) && ((shrapnel[j, i] > shrapnel[j, i - 1]) || (shrapnel[j, i] > Approx.LearnSamplesSet.InputAttribute(Approx.RulesDatabaseSet[0].TermsSet[(int)(i / type)].NumberOfInputVar).Max) || (shrapnel[j, i] < Approx.LearnSamplesSet.InputAttribute(Approx.RulesDatabaseSet[0].TermsSet[(int)(i / type)].NumberOfInputVar).Min)))
{
goto generate;
}
}
if (((i + (type - 2)) % type == 0) && ((shrapnel[j, i] < Approx.LearnSamplesSet.InputAttribute(Approx.RulesDatabaseSet[0].TermsSet[(int)(i / type)].NumberOfInputVar).Min) || (shrapnel[j, i] > Approx.LearnSamplesSet.InputAttribute(Approx.RulesDatabaseSet[0].TermsSet[(int)(i / type)].NumberOfInputVar).Max)))
{
goto generate;
}
exit:
if (i > type)
{
if (Approx.RulesDatabaseSet[0].TermsSet[(int)((i - 1) / type)].NumberOfInputVar != 0)
{
if (Approx.RulesDatabaseSet[0].TermsSet[(int)((i - 1 - type) / type)].NumberOfInputVar != Approx.RulesDatabaseSet[0].TermsSet[(int)((i - 1) / type)].NumberOfInputVar - 1)
{
if (((i + (type - 2)) % type == 0) && ((shrapnel[j, i] < shrapnel[j, i - type])))
{
goto generate;
}
}
}
}
}
}
else
{
//d[0, i] = d2(X_pred[0, i], X_pred[1, i], RMSE_pred[0], RMSE_pred[1]);
//for (j = 1; j < count_populate; j++)
//{
// if ((X_pred[1, i] - X_pred[0, i]) != 0)
// {
// direction[j, i] = m(X_pred[0, i], X_pred[1, i], RMSE_pred[0], RMSE_pred[1]);
// }
// else direction[j, i] = 1;
// int sum = 0, sum2 = 0;
//generate:
// sum++;
// sum2++;
// double random;
// random = randn();
// if(random<0) explosion[j, i] = d[j-1, i]*rand.NextDouble() * cosFi*(-1);
// else explosion[j, i] = d[j - 1, i] * rand.NextDouble() * cosFi;
// if (sum2 > 50) sum2 = 0;
// if (sum > 20)
// {
// if ((i + (type - 2)) % type == 0)
// {
// shrapnel[j, i] = Shrapnel(explosion[j, i], shrapnel[0, i], d[j - 1, i], direction[j, i]) + (shrapnel[j, i - 1] - shrapnel[j, i]);
// if (sum2 > 2)
// {
// shrapnel[j, i] = Shrapnel(explosion[j, i], shrapnel[0, i], d[j - 1, i], direction[j, i]) + (shrapnel[j, i - type] - shrapnel[j, i]);
// sum = 19;
// }
// if (sum2 > 3)
// {
// shrapnel[j, i] = Shrapnel(explosion[j, i], shrapnel[0, i], d[j - 1, i], direction[j, i]) + (shrapnel[j, i - type] - shrapnel[j, i]) + (shrapnel[j, i - 1] - shrapnel[j, i]);
// sum = 19;
// sum2 = 0;
// }
// }
// else
// {
// shrapnel[j, i] = Shrapnel(explosion[j, i], shrapnel[0, i], d[j - 1, i], direction[j, i]) + (shrapnel[j, i - 1] - shrapnel[j, i]);
// sum = 19;
// }
// }
// else shrapnel[j, i] = Shrapnel(explosion[j,i],shrapnel[0,i],d[j-1,i],direction[j,i]);
// if ((i == 2) || (i == 1))
// {
// shrapnel[j, i] = Approx.LearnSamplesSet.InputAttributeMin(Approx.RulesDatabaseSet[0].TermsSet[(int)(i - 1) / type].NumberOfInputVar);
// }
// if (Approx.RulesDatabaseSet[0].TermsSet[(int)((i - 1) / type)].NumberOfInputVar != Approx.RulesDatabaseSet[0].TermsSet[(int)((i - 2) / type)].NumberOfInputVar)
// {
// shrapnel[j, i] = Approx.LearnSamplesSet.InputAttributeMin(Approx.RulesDatabaseSet[0].TermsSet[(int)(i - 1) / type].NumberOfInputVar); goto exit;
// }
// if (Approx.RulesDatabaseSet[0].TermsSet[(int)((i - 1) / type)].NumberOfInputVar != Approx.RulesDatabaseSet[0].TermsSet[(int)((i - type) / type)].NumberOfInputVar)
// {
// shrapnel[j, i] = Approx.LearnSamplesSet.InputAttributeMin(Approx.RulesDatabaseSet[0].TermsSet[(int)(i - 1) / type].NumberOfInputVar); goto exit;
// }
// if (Approx.RulesDatabaseSet[0].TermsSet[(int)((i - 1) / type)].NumberOfInputVar != (variables - 1))
// {
// if (Approx.RulesDatabaseSet[0].TermsSet[(int)((i - 1) / type)].NumberOfInputVar != Approx.RulesDatabaseSet[0].TermsSet[(int)((i) / type)].NumberOfInputVar)
// {
// shrapnel[j, i] = Approx.LearnSamplesSet.InputAttributeMax(Approx.RulesDatabaseSet[0].TermsSet[(int)(i - 1) / type].NumberOfInputVar); goto exit;
// }
// if (Approx.RulesDatabaseSet[0].TermsSet[(int)((i - 1) / type)].NumberOfInputVar != Approx.RulesDatabaseSet[0].TermsSet[(int)((i + 1) / type)].NumberOfInputVar)
// {
// shrapnel[j, i] = Approx.LearnSamplesSet.InputAttributeMax(Approx.RulesDatabaseSet[0].TermsSet[(int)(i - 1) / type].NumberOfInputVar); goto exit;
// }
// }
// if (Approx.RulesDatabaseSet[0].TermsSet[(int)((i - 1) / type)].NumberOfInputVar == (variables - 1))
// {
// if((i==(count_terms*3-1))||(i==(count_terms*3)))
// shrapnel[j, i] = Approx.LearnSamplesSet.InputAttributeMax(Approx.RulesDatabaseSet[0].TermsSet[(int)(i - 1) / type].NumberOfInputVar);
// }
// if (((i + (type - 2)) % type == 0) && (shrapnel[j, i] < shrapnel[j, i - 1]))
// {
// if (shrapnel[j, i] == Approx.LearnSamplesSet.InputAttributeMin(Approx.RulesDatabaseSet[0].TermsSet[(int)(i / type)].NumberOfInputVar)) i--;
// goto generate;
// }
// if ((i % type == 0) && (shrapnel[j, i] < shrapnel[j, i - 1]))
// {
// goto generate;
// }
// if (i != 1)
// {
// if (((i - (type - 2)) % type == 0) && ((shrapnel[j, i] > shrapnel[j, i - 1]) || (shrapnel[j, i] > Approx.LearnSamplesSet.InputAttributeMax(Approx.RulesDatabaseSet[0].TermsSet[(int)(i / type)].NumberOfInputVar)) || (shrapnel[j, i] < Approx.LearnSamplesSet.InputAttributeMin(Approx.RulesDatabaseSet[0].TermsSet[(int)(i / type)].NumberOfInputVar))))
// {
// goto generate;
// }
// }
// if (((i + (type - 2)) % type == 0) && ((shrapnel[j, i] < Approx.LearnSamplesSet.InputAttributeMin(Approx.RulesDatabaseSet[0].TermsSet[(int)(i / type)].NumberOfInputVar)) || (shrapnel[j, i] > Approx.LearnSamplesSet.InputAttributeMax(Approx.RulesDatabaseSet[0].TermsSet[(int)(i / type)].NumberOfInputVar))))
// {
// goto generate;
// }
//exit:
// if (i > type)
// {
// if (Approx.RulesDatabaseSet[0].TermsSet[(int)((i - 1) / type)].NumberOfInputVar != 0)
// {
// if (Approx.RulesDatabaseSet[0].TermsSet[(int)((i - 1 - type) / type)].NumberOfInputVar != Approx.RulesDatabaseSet[0].TermsSet[(int)((i - 1) / type)].NumberOfInputVar - 1)
// {
// if (((i + (type - 2)) % type == 0) && ((shrapnel[j, i] < shrapnel[j, i - type])))
// {
// goto generate;
// }
// }
// }
// }
// d[j, i] = d[j-1,i] / Math.Pow(Math.E, (double)iter/(double)reduce_koef);
//}
}
}
for (int z = 0; z < count_populate; z++)
{
k = 1;
for (int h = 0; h < count_terms; h++)
{
for (int p = 0; p < type; p++)
{
Approx.RulesDatabaseSet[z].TermsSet[h].Parametrs[p] = shrapnel[z, k];
k++;
}
}
}
for (j = 0; j < count_populate; j++)
{
RMSE[j] = Approx.approxLearnSamples(j);
RMSE_tst[j] = Approx.approxTestSamples(j);
if (RMSE[j] < RMSE_best)
{
RMSE_best = RMSE[j];
best = j;
}
}
if ((iter != 0) && (iter % 1000 == 0))
{
RWLSMTakagiSugeno LSM = new RWLSMTakagiSugeno();
MSEbefore = RMSE[best];
KnowlegeBaseTSARules zeroSolution = new KnowlegeBaseTSARules(Approx.RulesDatabaseSet[0]);
Approx.RulesDatabaseSet[0] = new KnowlegeBaseTSARules(Approx.RulesDatabaseSet[best]);
KnowlegeBaseTSARules tempSolution = new KnowlegeBaseTSARules(Approx.RulesDatabaseSet[best]);
Approx = LSM.TuneUpFuzzySystem(Approx, new NullConfForAll()) as TSAFuzzySystem;
MSEafter = Approx.approxLearnSamples(0);
if (MSEafter > MSEbefore)
{
Approx.RulesDatabaseSet[0] = tempSolution;
RMSE2[best] = MSEbefore;
}
else
{
RMSE2[best] = MSEafter;
for (int p = 0; p < count_cons; p++)
{
cons_best[p] = Approx.RulesDatabaseSet[0].all_conq_of_rules[p];
}
}
if (RMSE2[best] < RMSE_best)
{
RMSE_best = RMSE2[best];
}
Approx.RulesDatabaseSet[best] = new KnowlegeBaseTSARules(Approx.RulesDatabaseSet[0]);
Approx.RulesDatabaseSet[0] = new KnowlegeBaseTSARules(zeroSolution);
for (int z = 0; z < count_populate; z++)
{
for (int p = 0; p < count_cons; p++)
{
Approx.RulesDatabaseSet[z].RulesDatabase[p].IndependentConstantConsequent = cons_best[p];
}
}
}
k = 1;
for (int h = 0; h < count_terms; h++)
{
for (int p = 0; p < type; p++)
{
shrapnel[0, k] = shrapnel[best, k];
if (exploration > iter)
{
d[0, k] = RandomNext(Approx.LearnSamplesSet.InputAttribute(Approx.RulesDatabaseSet[0].TermsSet[h].NumberOfInputVar).Min, Approx.LearnSamplesSet.InputAttribute(Approx.RulesDatabaseSet[0].TermsSet[h].NumberOfInputVar).Max);
}
Approx.RulesDatabaseSet[0].TermsSet[h].Parametrs[p] = shrapnel[0, k];
k++;
}
}
if (iter % 10 == 0)
{
if (RMSE_pred[1] > RMSE2[best])
{
for (k = 1; k <= Nd; k++)
{
X_pred[0, k] = X_pred[1, k];
X_pred[1, k] = shrapnel[best, k];
}
RMSE_pred[0] = RMSE_pred[1];
RMSE_pred[1] = RMSE2[best];
}
}
else
{
if (RMSE_pred[1] > RMSE[best])
{
for (k = 1; k <= Nd; k++)
{
X_pred[0, k] = X_pred[1, k];
X_pred[1, k] = shrapnel[best, k];
}
RMSE_pred[0] = RMSE_pred[1];
RMSE_pred[1] = RMSE[best];
}
}
}
return(result);
}
public TakagiSugenoElementofStorage(TSAFuzzySystem Checker, KnowlegeBaseTSARules SourceElem, string algName) : base(algName)
{
element = new KnowlegeBaseTSARules(SourceElem);
LearnError = Checker.approxLearnSamples(SourceElem);
TestError = Checker.approxTestSamples(SourceElem);
}