public void kliGenerate(TSAFuzzySystem Approximate, TypeTermFuncEnum typeFunc, double mayError)
{
if ((Approximate.RulesDatabaseSet == null) || (Approximate.RulesDatabaseSet.Count == 0))
{
Approximate.RulesDatabaseSet.Add(new KnowlegeBaseTSARules());
}
originalSimpleSet = new List <SampleSet.RowSample>(Approximate.LearnSamplesSet.DataRows);
originalSimpleSetDistanteToPoint = new List <double>();
CalcFarPoint();
int ruleIndex = 0;
while (originalSimpleSet.Count > 0)
{
int IndexOfMax = originalSimpleSetDistanteToPoint.IndexOf(originalSimpleSetDistanteToPoint.Max());
var maxPoint = originalSimpleSet[IndexOfMax];
originalSimpleSet.Remove(maxPoint);
originalSimpleSetDistanteToPoint.RemoveAt(IndexOfMax);
var iterClasterPoints = new List <SampleSet.RowSample>()
{
maxPoint
};
var mnk = new MNK()
{
n = maxPoint.InputAttributeValue.Count()
};
var P = new Matrix(new[] { new double[] { 0 } });
var B = new HyperVector(0, 0);
mnk.mnkIter(iterClasterPoints.Last().InputAttributeValue.ToList(), iterClasterPoints.Last().DoubleOutput,
ref P, ref B, true);
// var errorIter = errorsMnk(B, iterClasterPoints);
for (int z = 0; z < Approximate.CountFeatures; z++)
{
int NextIndex2 = maxPoint.NearestNormalizedInputIndex(originalSimpleSet, ResultSystem.LearnSamplesSet);
var nextPoint2 = originalSimpleSet[NextIndex2];
mnk.mnkIter(nextPoint2.InputAttributeValue.ToList(), nextPoint2.DoubleOutput, ref P, ref B, false);
iterClasterPoints.Add(nextPoint2);
originalSimpleSet.Remove(nextPoint2);
}
var errorIter = errorsMnk(B, iterClasterPoints);
while (errorIter < mayError)
{
if (originalSimpleSet.Count < Approximate.CountFeatures)
{
while (originalSimpleSet.Count > 0)
{
int NextIndex2 = maxPoint.NearestNormalizedInputIndex(originalSimpleSet, ResultSystem.LearnSamplesSet);
var nextPoint2 = originalSimpleSet[NextIndex2];
mnk.mnkIter(nextPoint2.InputAttributeValue.ToList(), nextPoint2.DoubleOutput, ref P, ref B, false);
iterClasterPoints.Add(nextPoint2);
originalSimpleSet.Remove(nextPoint2);
originalSimpleSetDistanteToPoint.RemoveAt(NextIndex2);
}
break;
}
int NextIndex = maxPoint.NearestNormalizedInputIndex(originalSimpleSet, ResultSystem.LearnSamplesSet);
var nextPoint = originalSimpleSet[NextIndex];
mnk.mnkIter(nextPoint.InputAttributeValue.ToList(), nextPoint.DoubleOutput, ref P, ref B, false);
errorIter = errorsMnk(B, new List <SampleSet.RowSample>(iterClasterPoints)
{
nextPoint
});
if (errorIter < mayError)
{
iterClasterPoints.Add(nextPoint);
maxPoint = nextPoint;
originalSimpleSet.Remove(nextPoint);
originalSimpleSetDistanteToPoint.RemoveAt(NextIndex);
}
}
var numbersRule = new List <int>();
List <Term> forRWLSM = new List <Term>();
for (int i = 0; i < Approximate.CountFeatures; i++)
{
var parametrs = new double[Term.CountParamsinSelectedTermType(typeFunc)];
parametrs[0] = iterClasterPoints.Sum(x => x.InputAttributeValue[i]) / iterClasterPoints.Count;
parametrs[1] =
Math.Sqrt(
iterClasterPoints.Sum(x => Math.Pow(x.InputAttributeValue[i] - parametrs[0], 2.0)) /
iterClasterPoints.Count);
if (parametrs[1] < Math.Pow(10, -300))
{
parametrs[1] = Approximate.LearnSamplesSet.InputAttributes[i].Scatter / 100.0;
}
var temp_term = new Term(parametrs, typeFunc, i);
forRWLSM.Add(temp_term);
Approximate.RulesDatabaseSet[0].TermsSet.Add(temp_term);
numbersRule.Add(Approximate.RulesDatabaseSet[0].TermsSet.Count - 1);
}
ruleIndex++;
double[] coeffs = null;
double coef = LSMWeghtReqursiveSimple.EvaluteConsiquent(ResultSystem, forRWLSM, out coeffs);
var temp_rule = new TSARule(Approximate.RulesDatabaseSet[0].TermsSet, numbersRule.ToArray(), coef, coeffs);
Approximate.RulesDatabaseSet[0].RulesDatabase.Add(temp_rule);
}
}
public static void kliGenerate(TSAFuzzySystem Approximate, TypeTermFuncEnum typeFunc, double mayError)
{
var ssr = new List <SampleSet.RowSample>();
for (int index = 0; index < Approximate.LearnSamplesSet.DataRows.Count; index++)
{
var row = new SampleSet.RowSample();
List <double> InputAttributeValueLIST = new List <double>();
for (int i = 0; i < Approximate.AcceptedFeatures.Count(); i++)
{
double q = Convert.ToDouble(Approximate.LearnSamplesSet.DataRows[index].InputAttributeValue[i].ToString());
if (Approximate.AcceptedFeatures[i])
{
InputAttributeValueLIST.Add(q);
}
row.InputAttributeValue = InputAttributeValueLIST.ToArray();
row.DoubleOutput = Approximate.LearnSamplesSet.DataRows[index].DoubleOutput;
}
ssr.Add(row);
}
List <SampleSet.AttributeInfo> ss =
new List <SampleSet.AttributeInfo>(Approximate.LearnSamplesSet.InputAttributes);
for (int i = (Approximate.AcceptedFeatures.Count() - 1); i >= 0; i--)
{
if (!Approximate.AcceptedFeatures[i])
{
ss.RemoveAt(i);
}
}
if ((Approximate.RulesDatabaseSet == null) || (Approximate.RulesDatabaseSet.Count == 0))
{
Approximate.RulesDatabaseSet.Add(new KnowlegeBaseTSARules());
//wtf????????????? что за, если null придет то всё плохо плохо, а мы его пропускаем выше лол
}
var originalSimpleSet = new List <SampleSet.RowSample>(ssr);
int ruleIndex = 0;
while (originalSimpleSet.Count > 0)
{
//а тут нормировка особой роли не играет, хммм ну или почти не играет
var maxPoint = originalSimpleSet.OrderByDescending(x => (Math.Sqrt(x.InputAttributeValue.Sum(y => Math.Pow(y, 2.0)) /
x.InputAttributeValue.Count()))).First();
var iterClasterPoints = new List <SampleSet.RowSample>()
{
maxPoint
};
var mnk = new MNK()
{
n = maxPoint.InputAttributeValue.Count()
};
var P = new Matrix(new[] { new double[] { 0 } });
var B = new HyperVector(0, 0);
mnk.mnkIter(iterClasterPoints.Last().InputAttributeValue.ToList(), iterClasterPoints.Last().DoubleOutput,
ref P, ref B, true);
var errorIter = errorsMnk(B, iterClasterPoints);
while (errorIter < mayError)
{
originalSimpleSet.RemoveAll(x => iterClasterPoints.Contains(x));
if (originalSimpleSet.Count == 0)
{
break;
}
//это без нормировки
// var nextPoint =
// originalSimpleSet.OrderBy(x =>(Math.Sqrt(x.InputAttributeValue.Sum(y => Math.Pow(y -maxPoint.InputAttributeValue[x.InputAttributeValue.ToList().IndexOf(y)],2.0))/x.InputAttributeValue.Count()))).First();
//а это с нормировкой
var nextPoint = originalSimpleSet.OrderBy(simple => Math.Sqrt(simple.InputAttributeValue.Select((inputValue, index) => Math.Pow((inputValue - maxPoint.InputAttributeValue[index]) / (ss[index].Scatter), 2.0)).Sum())).First();
mnk.mnkIter(nextPoint.InputAttributeValue.ToList(), nextPoint.DoubleOutput, ref P, ref B, false);
errorIter = errorsMnk(B, new List <SampleSet.RowSample>(iterClasterPoints)
{
nextPoint
});
if (errorIter < mayError)
{
iterClasterPoints.Add(nextPoint);
}
}
var numbersRule = new List <int>();
int spear = 0;
for (int i = 0; i < Approximate.AcceptedFeatures.Count(); i++)//.CountVars
{
if (!Approximate.AcceptedFeatures[i])
{
spear++;
continue;
}
var parametrs = new double[Term.CountParamsinSelectedTermType(typeFunc)];
parametrs[0] = iterClasterPoints.Sum(x => x.InputAttributeValue[i - spear]) / iterClasterPoints.Count;
parametrs[1] =
Math.Sqrt(
iterClasterPoints.Sum(x => Math.Pow(x.InputAttributeValue[i - spear] - parametrs[0], 2.0)) * 2 /
iterClasterPoints.Count);
var temp_term = new Term(parametrs, typeFunc, i);
Approximate.RulesDatabaseSet[0].TermsSet.Add(temp_term);
numbersRule.Add(ruleIndex + i - spear);
}
ruleIndex += Approximate.AcceptedFeatures.Where(x => x == true).Count();//.CountVars
var temp_rule = new TSARule(Approximate.RulesDatabaseSet[0].TermsSet, numbersRule.ToArray(), B.Elements[0].Elements[0], B.Elements.Skip(1).Select(x => x.Elements[0]).ToArray());
Approximate.RulesDatabaseSet[0].RulesDatabase.Add(temp_rule);
}
}
