public override ILearnAlgorithmConf getConf(int CountFeatures)
{
GeneticConf currentGeneticConf = new GeneticConf();
currentGeneticConf.Init(CountFeatures);
return(currentGeneticConf);
}
public virtual void Init(ILearnAlgorithmConf Config)
{
step = 0;
errorAfter = 0;
errorBefore = fullFuzzySystem.ClassifyLearnSamples(fullFuzzySystem.RulesDatabaseSet[0]);
currentConf = Config as GeneticConf;
initFunc = new initFuncTypeClassifier(localInit);
if (currentConf.GENCTypeInit == GeneticConf.Alg_Init_Type.Глобальный)
{
initFunc = new initFuncTypeClassifier(globalInit);
}
crossoverFunc = new crossoverFuncTypeClassifier(unifiedCrossover);
if (currentConf.GENCTypeCrossover == GeneticConf.Alg_Crossover_Type.Многоточечный)
{
crossoverFunc = new crossoverFuncTypeClassifier(pointsCrossover);
}
selectionFunc = new selectionFuncTypeClassifier(rouletteSelection);
if (currentConf.GENCTypeSelection == GeneticConf.Alg_Selection_Type.Случайный)
{
selectionFunc = new selectionFuncTypeClassifier(randomSelection);
}
if (currentConf.GENCTypeSelection == GeneticConf.Alg_Selection_Type.Элитарный)
{
selectionFunc = new selectionFuncTypeClassifier(eliteSelection);
}
fullInit(); // Здесь проходит инициализация
}
public virtual void Init(ILearnAlgorithmConf conf)
{
currentConf = conf as GeneticConf;
fullFuzzySystem = result;
step = 0;
errorAfter = 0;
errorBefore = result.approxLearnSamples(result.RulesDatabaseSet[0]);
backUp = result.RulesDatabaseSet[0];
initFunc = new initFuncType(localInit);
if (currentConf.GENCTypeInit == GeneticConf.Alg_Init_Type.Глобальный)
{
initFunc = new initFuncType(globalInit);
}
crossoverFunc = new crossoverFuncType(unifiedCrossover);
if (currentConf.GENCTypeCrossover == GeneticConf.Alg_Crossover_Type.Многоточечный)
{
crossoverFunc = new crossoverFuncType(pointsCrossover);
}
selectionFunc = new selectionFuncType(rouletteSelection);
if (currentConf.GENCTypeSelection == GeneticConf.Alg_Selection_Type.Случайный)
{
selectionFunc = new selectionFuncType(randomSelection);
}
if (currentConf.GENCTypeSelection == GeneticConf.Alg_Selection_Type.Элитарный)
{
selectionFunc = new selectionFuncType(eliteSelection);
}
fullInit(); // Здесь проходит инициализация
}
private List <ILearnAlgorithmConf> initAlgoritmsConfigs(int CountFeature)
{
List <ILearnAlgorithmConf> result = new List <ILearnAlgorithmConf>();
if (isTermShrink)
{
result.Add(new OptimizeTermShrinkAndRotateConf());
}
if (isRuleShrink)
{
result.Add(new OptimizeRullesShrinkConf());
}
if (isUnionTerm)
{
result.Add(new UnionTermsConf());
}
if (isLindBreakCross)
{
result.Add(null);
}
if (isPSO)
{
for (int i = 0; i < countPSO; i++)
{
result.Add(new PSOSearchConf());
}
}
if (isANT)
{
for (int i = 0; i < countANT; i++)
{
result.Add(new MACOSearchConf());
}
}
if (isBEE)
{
for (int i = 0; i < countBEE; i++)
{
result.Add(new BeeStructureConf());
}
}
if (isES)
{
for (int i = 0; i < countES; i++)
{
ESConfig method = new ESConfig();
method.Init(CountFeature);
result.Add(method);
}
}
if (isGA)
{
for (int i = 0; i < countGA; i++)
{
GeneticConf method = new GeneticConf();
method.Init(CountFeature);
result.Add(method);
}
}
/* if (isBFO)
* {
* for (int i = 0; i < countBFO; i++)
* {
* BacterialForagingOptimization.Base.BacteryAlgorithmConfig method = new BacterialForagingOptimization.Base.BacteryAlgorithmConfig();
* method.Init(CountFeature);
* result.Add(method);
* }
* }
*/
return(result);
}
