private void ChangeExplorerPositions(int i)
{
KnowlegeBaseTSARules temp = new KnowlegeBaseTSARules();
temp = ExplorerParticles[i];
for (int k = 0; k < temp.TermsSet.Count; k++)
{
for (int j = 0; j < temp.TermsSet[k].Parametrs.Length; j++)
{
temp.TermsSet[k].Parametrs[j] += VelocityVector.TermsSet[k].Parametrs[j];
}
}
if (result.approxLearnSamples(ExplorerParticles[i]) < result.approxLearnSamples(ParticlesBest[ExplorerParticles[i]]))
{
ParticlesBest.Remove(ExplorerParticles[i]);
ParticlesBest.Add(temp, ExplorerParticles[i]);
}
else
{
KnowlegeBaseTSARules tmp = new KnowlegeBaseTSARules();
tmp = ParticlesBest[ExplorerParticles[i]];
ParticlesBest.Remove(ExplorerParticles[i]);
ParticlesBest.Add(temp, tmp);
}
ExplorerParticles[i] = temp;
}
private KnowlegeBaseTSARules[] SortRules(KnowlegeBaseTSARules[] Source)
{
double[] keys = new double[Source.Count()];
KnowlegeBaseTSARules[] tempSol = Source.Clone() as KnowlegeBaseTSARules[];
for (int i = 0; i < Source.Count(); i++)
{
keys[i] = result.approxLearnSamples(Source[i]);
}
Array.Sort(keys, tempSol);
return(Source);
}
public override TSAFuzzySystem TuneUpFuzzySystem(TSAFuzzySystem Approximate, ILearnAlgorithmConf Conf)
{
result = Approximate;
List <int[]> groups = new List <int[]>();
Init(Conf);
SetPopulation();
Population = SortRules(Population);
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;
for (int i = 1; i <= MaxIter; i++)
{
Console.WriteLine(i + " - Итерация");
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;
}
}
Console.WriteLine(ToString(true));
Console.WriteLine("Итер - " + BestIter + " MSET - " + BestMSETest + " MSEL - " + BestMSELearn);
result.RulesDatabaseSet[0] = Population[0];
return(result);
}
public override TSAFuzzySystem TuneUpFuzzySystem(TSAFuzzySystem Approx, ILearnAlgorithmConf conf)
{
result = Approx;
Init(conf);
HeadLeader = new KnowlegeBaseTSARules(result.RulesDatabaseSet[0]);
VelocityVector = new KnowlegeBaseTSARules(result.RulesDatabaseSet[0]);
VelocityVectorLL = new KnowlegeBaseTSARules(result.RulesDatabaseSet[0]);
VelocityVectorHL = new KnowlegeBaseTSARules(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 <KnowlegeBaseTSARules, KnowlegeBaseTSARules>();
foreach (var Particle in Population)
{
ParticlesBest.Add(Particle, Universal);
}
LocalLeaders = new KnowlegeBaseTSARules[numberOfLocalLeaders];
ExplorerParticles = new KnowlegeBaseTSARules[numberOfAllParts - numberOfAimlessParts - numberOfLocalLeaders - 1];
AimlessParticles = new KnowlegeBaseTSARules[numberOfAimlessParts];
iter = 0;
while (iter < MaxIter)
{
Population = ListTakagiSugenoApproximateTool.SortRules(Population, result);
SetRoles();
ChangeExplorersPositions();
ChangeAimlessPositions();
DiscardRoles();
if (iter == (MaxIter - 1) || iter == 0)
{
Console.WriteLine("Iteration: " + (iter + 1).ToString());
Console.WriteLine(Math.Round(result.RMSEtoMSEforLearn(result.approxLearnSamples(Population[0])), 3));
Console.WriteLine(Math.Round(result.RMSEtoMSEforTest(result.approxTestSamples(Population[0])), 3));
}
iter++;
}
result.RulesDatabaseSet[0] = Population[0];
return(result);
}