public ScoreListElem(ScoreListElem Source)
{
Alg = Source.Alg.Clone() as string;
Res = new KnowlegeBaseTSARules(Source.Res);
MaxError = Source.MaxError;
Error = Source.Error;
}
public override FuzzySystem.TakagiSugenoApproximate.TSAFuzzySystem Generate(FuzzySystem.TakagiSugenoApproximate.TSAFuzzySystem Approximate, FuzzySystem.FuzzyAbstract.conf.IGeneratorConf config)
{
TSAFuzzySystem result = Approximate;
type_func = TypeTermFuncEnum.Гауссоида;
ResultSystem = Approximate;
var kliConf = config as KLI_conf;
if (kliConf != null)
{
double meanValue = result.LearnSamplesSet.DataRows.Select(x => x.DoubleOutput).Average();
var mayError = kliConf.MaxValue * meanValue;
double centerValue = mayError;
ScoreListElem[] ResultsKLI = new ScoreListElem[3];
ScoreListElem[] ResultsKLI2 = new ScoreListElem[3];
List <ScoreListElem> ResultsKLIFull = new List <ScoreListElem>();
List <ScoreListElem> ResultsKLI2Full = new List <ScoreListElem>();
double magic = 0.25;
KLI.kliGenerate(result, type_func, centerValue);
ScoreListElem tempRes = new ScoreListElem();
tempRes.MaxError = centerValue;
tempRes.Alg = "Кли";
tempRes.Res = result.RulesDatabaseSet[0];
tempRes.Error = result.ErrorLearnSamples(tempRes.Res);
ResultsKLI[1] = new ScoreListElem(tempRes);
if (!double.IsNaN(tempRes.Error))
{
ResultsKLIFull.Add(new ScoreListElem(tempRes));
}
for (int i = 0; i < 100; i++)
{
double leftValue = centerValue * (1 - magic);
double rigthValue = centerValue * (1 + magic);
tempRes = new ScoreListElem();
KLI.kliGenerate(result, type_func, leftValue);
tempRes.Alg = "Кли";
tempRes.MaxError = leftValue;
tempRes.Res = result.RulesDatabaseSet[0];
tempRes.Error = result.ErrorLearnSamples(tempRes.Res);
ResultsKLI[0] = new ScoreListElem(tempRes);
if (!double.IsNaN(tempRes.Error))
{
ResultsKLIFull.Add(new ScoreListElem(tempRes));
}
result.RulesDatabaseSet.Clear();
tempRes = new ScoreListElem();
KLI.kliGenerate(result, type_func, rigthValue);
tempRes.Alg = "Кли";
tempRes.MaxError = rigthValue;
tempRes.Res = result.RulesDatabaseSet[0];
tempRes.Error = result.ErrorLearnSamples(tempRes.Res);
ResultsKLI[2] = tempRes;
if (!double.IsNaN(tempRes.Error))
{
ResultsKLIFull.Add(new ScoreListElem(tempRes));
}
result.RulesDatabaseSet.Clear();
if (((ResultsKLI[0].Error >= ResultsKLI[1].Error) || double.IsNaN(ResultsKLI[0].Error)) && ((ResultsKLI[1].Error <= ResultsKLI[2].Error) || (double.IsNaN(ResultsKLI[2].Error))))
{
magic *= 0.7;
}
double err = ResultsKLIFull.Min(y => y.Error);
ResultsKLI[1].Error = err;
centerValue = ResultsKLIFull.First(x => x.Error == err).MaxError;
}
centerValue = mayError;
tempRes = new ScoreListElem();
kliGenerate(result, type_func, centerValue);
tempRes.Alg = "Кли2";
tempRes.MaxError = centerValue;
tempRes.Res = result.RulesDatabaseSet[0];
tempRes.Error = result.ErrorLearnSamples(tempRes.Res);
ResultsKLI2[1] = new ScoreListElem(tempRes);
if (!double.IsNaN(tempRes.Error))
{
ResultsKLI2Full.Add(new ScoreListElem(tempRes));
}
result.RulesDatabaseSet.Clear();
magic = 0.25;
for (int i = 0; i < 100; i++)
{
double leftValue = centerValue * (1 - magic);
double rigthValue = centerValue * (1 + magic);
tempRes = new ScoreListElem();
kliGenerate(result, type_func, leftValue);
tempRes.Alg = "Кли2";
tempRes.MaxError = leftValue;
tempRes.Res = result.RulesDatabaseSet[0];
tempRes.Error = result.ErrorLearnSamples(tempRes.Res);
ResultsKLI2[0] = new ScoreListElem(tempRes);
if (!double.IsNaN(tempRes.Error))
{
ResultsKLI2Full.Add(new ScoreListElem(tempRes));
}
result.RulesDatabaseSet.Clear();
tempRes = new ScoreListElem();
kliGenerate(result, type_func, rigthValue);
tempRes.Alg = "Кли2";
tempRes.MaxError = rigthValue;
tempRes.Res = result.RulesDatabaseSet[0];
tempRes.Error = result.ErrorLearnSamples(tempRes.Res);
ResultsKLI2[2] = new ScoreListElem(tempRes);
if (!double.IsNaN(tempRes.Error))
{
ResultsKLI2Full.Add(new ScoreListElem(tempRes));
}
result.RulesDatabaseSet.Clear();
if (((ResultsKLI2[0].Error >= ResultsKLI2[1].Error) || double.IsNaN(ResultsKLI2[0].Error)) && ((ResultsKLI2[1].Error <= ResultsKLI2[2].Error) || (double.IsNaN(ResultsKLI2[2].Error))))
{
magic *= 0.7;
}
double err = ResultsKLI2Full.Min(y => y.Error);
ResultsKLI2[1].Error = err;
centerValue = ResultsKLI2Full.First(x => x.Error == err).MaxError;
}
ScoreListElem BestKli = ResultsKLIFull.Where(x => x.Error == ResultsKLIFull.Min(y => y.Error)).First();
ScoreListElem BestKli2 = ResultsKLI2Full.Where(x => x.Error == ResultsKLI2Full.Min(y => y.Error)).First();
if (BestKli.Error > BestKli2.Error)
{
Best = BestKli2;
}
else
{
Best = BestKli;
}
result.RulesDatabaseSet.Add(Best.Res);
}
return(result);
}
