public override PCFuzzySystem TuneUpFuzzySystem(PCFuzzySystem Classifier, ILearnAlgorithmConf conf)
{
BacteryAlgorithmConfig Config = conf as BacteryAlgorithmConfig;
sendBactery = Config.BFOCountSolution;
interPSOtoSend = Config.BFOCountIteration;
result = Classifier;
if (result.RulesDatabaseSet.Count < 1)
{
throw new InvalidDataException("Нечеткая система не проинициализированна");
}
KnowlegeBasePCRules backSave = new KnowlegeBasePCRules(result.RulesDatabaseSet[0]);
double backResult = result.ErrorLearnSamples(result.RulesDatabaseSet[0]);
savetoUFS(result.RulesDatabaseSet, 0, 0, 0);
BacteryRunner();
KnowlegeBasePCRules[] solutions = loadDatabase();
solutions = sortSolution(solutions);
if (solutions.Count() < 1)
{
result.RulesDatabaseSet[0] = backSave; return(result);
}
result.RulesDatabaseSet[0] = solutions[0];
double newResult = result.ErrorLearnSamples(result.RulesDatabaseSet[0]);
if (newResult > backResult)
{
result.RulesDatabaseSet[0] = backSave;
}
result.RulesDatabaseSet[0].TermsSet.Trim();
return(result);
}
private KnowlegeBasePCRules[] SortRules(KnowlegeBasePCRules[] Source)
{
double[] keys = new double[Source.Count()];
KnowlegeBasePCRules[] tempSol = Source.Clone() as KnowlegeBasePCRules[];
for (int i = 0; i < Source.Count(); i++)
{
keys[i] = result.ErrorLearnSamples(Source[i]);
}
Array.Sort(keys, tempSol);
return(Source);
}
public void preIterate(PCFuzzySystem Classify, ILearnAlgorithmConf conf)
{
result = Classify;
rand = new Random();
//Узнаем название папки с данными
string path_name = "../../OLD/Data/Keel/Classifier/KEEL-10/";
string folder_name = "";
foreach (var letter in result.LearnSamplesSet.FileName)
{
if (letter != '-')
{
folder_name += letter;
}
else
{
break;
}
}
Init(conf);
//Создаем новые обучающую и тестовую выбоки и удаляем из них некоторое количество случайных элементов
results = new List <PCFuzzySystem>();
for (int i = 0; i < numberOfPopulations; i++)
{
SampleSet new_learn = new SampleSet(path_name + folder_name + "/" + result.LearnSamplesSet.FileName);
SampleSet new_test = new SampleSet(path_name + folder_name + "/" + result.TestSamplesSet.FileName);
results.Add(new PCFuzzySystem(new_learn, new_test));
int ground = (int)Math.Round(results[i].LearnSamplesSet.DataRows.Count * 0.1);
for (int j = 0; j < ground; j++)
{
results[i].LearnSamplesSet.DataRows.RemoveAt(rand.Next(0, results[i].LearnSamplesSet.DataRows.Count));
}
}
//Создаем популяции и архив лучших положений каждой частицы
Populations = new List <List <KnowlegeBasePCRules> >();
for (int i = 0; i < numberOfPopulations; i++)
{
Populations.Add(SetPopulation(new List <KnowlegeBasePCRules>()));
}
BEST = new List <KnowlegeBasePCRules>();
bestError = new double[numberOfPopulations];
for (int i = 0; i < numberOfPopulations; i++)
{
BEST.Add(new KnowlegeBasePCRules(result.RulesDatabaseSet[0]));
bestError[i] = result.ErrorLearnSamples(BEST[i]);
if (double.IsNaN(bestError[i]) || double.IsInfinity(bestError[i]) || bestError[i] == 100)
{
result.UnlaidProtectionFix(BEST[i]);
bestError[i] = result.ErrorLearnSamples(BEST[i]);
}
}
}
public virtual void Final()
{
KnowlegeBasePCRules best = main_pop.get_best_database();
double errorBest = result.ErrorLearnSamples(best);
double currentError = result.ErrorLearnSamples(result.RulesDatabaseSet[0]);
if (currentError > errorBest)
{
result.RulesDatabaseSet[0] = main_pop.get_best_database();
}
result.RulesDatabaseSet[0] = main_pop.get_best_database();
GC.Collect();
}
public virtual void oneWatchJump(PCFuzzySystem result)
{
for (int j = 0; j < population_count; j++)
{
WJVector_gen(j);
if (result.ErrorLearnSamples(WJVector) <= result.ErrorLearnSamples(monkey[j]))
{
monkey[j] = WJVector;
// delete
//double testval = result.ErrorLearnSamples(monkey[j]);
//Console.WriteLine(j.ToString() + " " + iter.ToString() + " " + testval.ToString() + " - Success at WatchJump");
}
}
}
public override PCFuzzySystem Generate(PCFuzzySystem Classifier, IGeneratorConf config)
{
PCFuzzySystem result = Classifier;
if (result.RulesDatabaseSet.Count == 0)
{
AbstractNotSafeGenerator tempGen = new GeneratorRulesEveryoneWithEveryone();
result = tempGen.Generate(result, config);
GC.Collect();
}
count_shrink = ((TermShrinkAndRotateConf)config).TSARCShrinkVars;
size_shrink = ((TermShrinkAndRotateConf)config).TSARCShrinkTerm;
type_func = ((TermShrinkAndRotateConf)config).IEWEFuncType;
count_slices = ((TermShrinkAndRotateConf)config).IEWECountSlice;
List <int> Varians_of_run_system = new List <int>();
for (int i = 0; i < Classifier.CountFeatures; i++)
{
int count_terms_for_var = Classifier.RulesDatabaseSet[0].TermsSet.FindAll(x => x.NumVar == i).Count;
if (i < count_shrink)
{
Varians_of_run_system.Add(count_terms_for_var - size_shrink);
}
else
{
Varians_of_run_system.Add(count_terms_for_var);
}
}
Varians_of_run_system.Sort();
TypeTermFuncEnum type_of_term = Classifier.RulesDatabaseSet[0].TermsSet[0].TermFuncType;
Generate_all_variant_in_pool(Varians_of_run_system);
for (int i = 0; i < Pull_of_systems.Count; i++)
{
Classifier.RulesDatabaseSet.Clear();
GeneratorRulesEveryoneWithEveryone.InitRulesEveryoneWithEveryone(Classifier, type_of_term, Pull_of_systems[i].ToArray());
Systems_ready_to_test.Add(Classifier.RulesDatabaseSet[0]);
errors_of_systems.Add(result.ErrorLearnSamples(result.RulesDatabaseSet[0]));
}
int best_index = errors_of_systems.IndexOf(errors_of_systems.Min());
result.RulesDatabaseSet.Clear();
result.RulesDatabaseSet.Add(Systems_ready_to_test[best_index]);
Console.WriteLine(Pull_of_systems.Count());
GC.Collect();
// result.UnlaidProtectionFix();
result.RulesDatabaseSet[0].TermsSet.Trim();
return(result);
}
public void eliteSelection()
{
double[] currentError = new double[childrenMassive.Count()];
for (int i = 0; i < childrenMassive.Count(); i++)
//Parallel.For(0, childrenMassive.Count(), i =>
{
fullFuzzySystem.RulesDatabaseSet.Add(childrenMassive[i]);
fullFuzzySystem.UnlaidProtectionFix(childrenMassive[i]);
currentError[i] = (fullFuzzySystem.ErrorLearnSamples(fullFuzzySystem.RulesDatabaseSet[i + 1]));
}
//});
Array.Sort(currentError, childrenMassive);
populationMassive = childrenMassive.ToList().GetRange(0, populationMassive.Count()).ToArray();
fullFuzzySystem.RulesDatabaseSet.RemoveRange(1, childrenMassive.Count());
}
public override PCFuzzySystem TuneUpFuzzySystem(PCFuzzySystem Classifier, ILearnAlgorithmConf conf)
{
result = Classifier;
groups = new List <int[]>();
Init(conf);
SetPopulation();
Population = SortRules(Population);
NS = new int[m];
for (int i = 0; i < m; i++)
{
NS[i] = (N - 1) / m;
}
cur_iter = 0;
while (cur_iter < iter)
{
groups = GroupStream();
if (p_one > rand.NextDouble())
{
ChooseOneCluster();
}
else
{
ChooseTwoClusters();
}
Population = ListPittsburgClassifierTool.SortRules(Population, result);
Console.WriteLine(cur_iter + " - Итерация");
Console.WriteLine("Обуч. выборка = " + result.ErrorLearnSamples(Population[0]));
Console.WriteLine("Тест. выборка = " + result.ErrorTestSamples(Population[0]));
cur_iter++;
}
Population = ListPittsburgClassifierTool.SortRules(Population, result);
result.RulesDatabaseSet[0] = Population[0];
return(result);
}
public override PCFuzzySystem TuneUpFuzzySystem(PCFuzzySystem Classifier, ILearnAlgorithmConf config)
{
PCFuzzySystem result = Classifier;
if (result.RulesDatabaseSet.Count == 0)
{
throw new System.FormatException("Что то не то с входными данными");
}
OptimizeTermShrinkAndRotateConf Config = config as OptimizeTermShrinkAndRotateConf;
count_shrink = Config.OTSARCountShrinkVars;
size_shrink = Config.OTSARCountShrinkTerm;
List <int> Varians_of_run_system = new List <int>();
for (int i = 0; i < Classifier.CountFeatures; i++)
{
int count_terms_for_var = Classifier.RulesDatabaseSet[0].TermsSet.FindAll(x => x.NumVar == i).Count;
if (i < count_shrink)
{
Varians_of_run_system.Add(count_terms_for_var - size_shrink);
}
else
{
Varians_of_run_system.Add(count_terms_for_var);
}
}
Varians_of_run_system.Sort();
TypeTermFuncEnum type_of_term = Classifier.RulesDatabaseSet[0].TermsSet[0].TermFuncType;
Generate_all_variant_in_pool(Varians_of_run_system);
for (int i = 0; i < Pull_of_systems.Count; i++)
{
Classifier.RulesDatabaseSet.Clear();
GeneratorRulesEveryoneWithEveryone.InitRulesEveryoneWithEveryone(Classifier, type_of_term, Pull_of_systems[i].ToArray());
Systems_ready_to_test.Add(Classifier.RulesDatabaseSet[0]);
errors_of_systems.Add(result.ErrorLearnSamples(result.RulesDatabaseSet[0]));
}
int best_index = errors_of_systems.IndexOf(errors_of_systems.Min());
result.RulesDatabaseSet.Clear();
result.RulesDatabaseSet.Add(Systems_ready_to_test[best_index]);
Console.WriteLine(Pull_of_systems.Count());
result.RulesDatabaseSet[0].TermsSet.Trim();
// result.UnlaidProtectionFix();
return(result);
}
public override PCFuzzySystem Generate(PCFuzzySystem Classifier, IGeneratorConf config)
{
start_add_rules = Classifier.RulesDatabaseSet.Count;
PCFuzzySystem result = Classifier;
if (result.RulesDatabaseSet.Count == 0)
{
AbstractNotSafeGenerator tempGen = new GeneratorRulesEveryoneWithEveryone();
result = tempGen.Generate(result, config);
GC.Collect();
}
Request_count_rules = ((RullesShrinkConf)config).RSCCountRules;
max_count_rules = ((RullesShrinkConf)config).RSCMaxRules;
count_slices = ((RullesShrinkConf)config).IEWECountSlice;
min_count_rules = ((RullesShrinkConf)config).RSCMinRules;
type_term = ((RullesShrinkConf)config).IEWEFuncType;
int count_of_swith_off = ((RullesShrinkConf)config).RSCMaxRules - Request_count_rules;
List <byte> Varians_of_run_system = new List <byte>();
for (int i = 0; i < Classifier.RulesDatabaseSet[0].RulesDatabase.Count; i++)
{
Varians_of_run_system.Add(1);
}
for (int i = 0; i < count_of_swith_off; i++)
{
Varians_of_run_system[i] = 0;
}
Generate_all_variant_in_pool(Varians_of_run_system);
for (int i = 0; i < Pull_of_systems.Count; i++)
{
KnowlegeBasePCRules temp_rules = new KnowlegeBasePCRules(result.RulesDatabaseSet[0], Pull_of_systems[i]);
temp_rules.TrimTerms();
result.RulesDatabaseSet.Add(temp_rules);
result.UnlaidProtectionFix(result.RulesDatabaseSet[start_add_rules + i]);
errors_of_systems.Add(result.ErrorLearnSamples(result.RulesDatabaseSet[start_add_rules + i]));
}
int best_index = errors_of_systems.IndexOf(errors_of_systems.Min());
KnowlegeBasePCRules best = result.RulesDatabaseSet[start_add_rules + best_index];
result.RulesDatabaseSet.Clear();
result.RulesDatabaseSet.Add(best);
Console.WriteLine(Pull_of_systems.Count());
GC.Collect();
// result.UnlaidProtectionFix();
result.RulesDatabaseSet[0].TermsSet.Trim();
return(result);
}
public static KnowlegeBasePCRules[] SortRules(this KnowlegeBasePCRules[] Source, PCFuzzySystem Classifier)
{
double[] keys = new double[Source.Count()];
KnowlegeBasePCRules[] tempSol = Source.Clone() as KnowlegeBasePCRules[];
for (int i = 0; i < Source.Count(); i++)
{
keys[i] = Classifier.ErrorLearnSamples(Source[i]);
}
Array.Sort(keys, tempSol);
return(tempSol);
}
public virtual void oneGlobalJump(PCFuzzySystem result)
{
for (int j = 0; j < population_count; j++)
{
// SSVector_gen();
SSVector = bestsolution;
IndividualSSVector = new KnowlegeBasePCRules(monkey[j]);
for (int k = 0; k < monkey[j].TermsSet.Count; k++)
{
for (int q = 0; q < monkey[j].TermsSet[k].CountParams; q++)
{
IndividualSSVector.TermsSet[k].Parametrs[q] += (somersault_interval_left + (somersault_interval_right - somersault_interval_left) * StaticRandom.NextDouble()) * (SSVector.TermsSet[k].Parametrs[q] - monkey[j].TermsSet[k].Parametrs[q]);
}
}
if (result.ErrorLearnSamples(IndividualSSVector) <= result.ErrorLearnSamples(monkey[j]))
{
monkey[j] = IndividualSSVector;
// delete
//double testval = result.ErrorLearnSamples(monkey[j]);
//Console.WriteLine(j.ToString() + " " + iter.ToString() + " " + testval.ToString() + " - Success at Global Jump");
}
}
}
public override PCFuzzySystem TuneUpFuzzySystem(PCFuzzySystem Classifier, ILearnAlgorithmConf config)
{
start_add_rules = Classifier.RulesDatabaseSet.Count;
PCFuzzySystem result = Classifier;
if (result.RulesDatabaseSet.Count == 0)
{
throw new System.FormatException("Что то не то с входными данными");
}
OptimizeRullesShrinkConf Config = config as OptimizeRullesShrinkConf;
Request_shrink_Rule = Config.ORSCCountShrinkRules;
int count_of_swith_off = Request_shrink_Rule;
List <byte> Varians_of_run_system = new List <byte>();
for (int i = 0; i < Classifier.RulesDatabaseSet[0].RulesDatabase.Count; i++)
{
Varians_of_run_system.Add(1);
}
for (int i = 0; i < count_of_swith_off; i++)
{
Varians_of_run_system[i] = 0;
}
Generate_all_variant_in_pool(Varians_of_run_system);
for (int i = 0; i < Pull_of_systems.Count; i++)
{
KnowlegeBasePCRules temp_rules = new KnowlegeBasePCRules(result.RulesDatabaseSet[0], Pull_of_systems[i]);
temp_rules.TrimTerms();
result.RulesDatabaseSet.Add(temp_rules);
result.UnlaidProtectionFix(result.RulesDatabaseSet[start_add_rules + i]);
errors_of_systems.Add(result.ErrorLearnSamples(result.RulesDatabaseSet[start_add_rules + i]));
}
int best_index = errors_of_systems.IndexOf(errors_of_systems.Min());
KnowlegeBasePCRules best = result.RulesDatabaseSet[start_add_rules + best_index];
result.RulesDatabaseSet.Clear();
result.RulesDatabaseSet.Add(best);
Console.WriteLine(Pull_of_systems.Count());
result.RulesDatabaseSet[0].TermsSet.Trim();
// result.UnlaidProtectionFix();
return(result);
}
public override PCFuzzySystem TuneUpFuzzySystem(PCFuzzySystem Class, ILearnAlgorithmConf Conf)
{
result = Class;
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.ErrorTestSamples(Population[0]);
double BestMSELearn = result.ErrorLearnSamples(Population[0]);
int BestIter = 0;
for (int i = 1; i <= MaxIter; i++)
{
Console.Clear();
Console.WriteLine((double)i * 100 / MaxIter + "%");
Population = SetNextPosition(groups, Population);
Population = Replacement(groups, Population);
if (flag)
{
Evaporation(groups.Last());//Испарение
}
if (BestMSETest > result.ErrorTestSamples(Population[0]))
{
BestMSETest = result.ErrorTestSamples(Population[0]);
BestMSELearn = result.ErrorLearnSamples(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 PCFuzzySystem Generate(PCFuzzySystem Classifier, IGeneratorConf config)
{
PCFuzzySystem result = Classifier;
Systems_ready_to_test = new List <KnowlegeBasePCRules>();
errors_of_systems = new List <double>();
InitEveryoneWithEveryone config1 = config as InitEveryoneWithEveryone;
type_func = config1.IEWEFuncType;
count_slice_vars = config1.IEWECountSlice;
List <int> Varians_of_run_system = new List <int>();
for (int i = 0; i < Classifier.CountFeatures; i++)
{
int count_terms_for_var = count_slice_vars[i];
Varians_of_run_system.Add(count_terms_for_var);
}
Varians_of_run_system.Sort();
Generate_all_variant_in_pool(Varians_of_run_system);
for (int i = 0; i < Pull_of_systems.Count; i++)
{
Classifier.RulesDatabaseSet.Clear();
GeneratorRulesEveryoneWithEveryone.InitRulesEveryoneWithEveryone(result, type_func, Pull_of_systems[i].ToArray());
Systems_ready_to_test.Add(Classifier.RulesDatabaseSet[0]);
errors_of_systems.Add(result.ErrorLearnSamples(result.RulesDatabaseSet[0]));
}
int best_index = errors_of_systems.IndexOf(errors_of_systems.Min());
result.RulesDatabaseSet.Clear();
result.RulesDatabaseSet.Add(Systems_ready_to_test[best_index]);
for (int i = 0; i < count_slice_vars.Count(); i++)
{
count_slice_vars[i] = result.RulesDatabaseSet[0].TermsSet.Count(x => x.NumVar == i);
}
Console.WriteLine(Pull_of_systems.Count());
GC.Collect();
result.RulesDatabaseSet[0].TermsSet.Trim();
return(result);
}
private string ErrorInfoPC(IFuzzySystem FS)
{
PCFuzzySystem IFS = FS as PCFuzzySystem;
if (IFS.RulesDatabaseSet.Count < 1)
{
return("Точность нечеткой системы недоступна");
}
classLearnResult.Add(IFS.ClassifyLearnSamples(IFS.RulesDatabaseSet[0]));
classTestResult.Add(IFS.ClassifyTestSamples(IFS.RulesDatabaseSet[0]));
classErLearn.Add(IFS.ErrorLearnSamples(IFS.RulesDatabaseSet[0]));
classErTest.Add(IFS.ErrorTestSamples(IFS.RulesDatabaseSet[0]));
return("Точностью на обучающей выборке " + classLearnResult[classLearnResult.Count - 1].ToString() + " , Точность на тестовой выборке " + classTestResult[classTestResult.Count - 1].ToString() + " " + Environment.NewLine +
"Ошибкой на обучающей выборке " + classErLearn[classErLearn.Count - 1].ToString() + " , Ошибкой на тестовой выборке " + classErTest[classErTest.Count - 1].ToString() + " " + Environment.NewLine);
}
protected KnowlegeBasePCRules[] sortSolution(KnowlegeBasePCRules[] Source)
{
KnowlegeBasePCRules temp = result.RulesDatabaseSet[0];
double[] keys = new double[Source.Count()];
KnowlegeBasePCRules[] tempSol = Source.Clone() as KnowlegeBasePCRules[];
for (int i = 0; i < Source.Count(); i++)
{
result.RulesDatabaseSet[0] = Source[i];
keys[i] = result.ErrorLearnSamples(result.RulesDatabaseSet[0]);
}
Array.Sort(keys, tempSol);
result.RulesDatabaseSet[0] = temp;
return(tempSol);
}
public virtual void CheckForBest(PCFuzzySystem result)
{
KnowlegeBasePCRules temp = monkey.SelectBest(result, 1)[0];
double tempnumber = result.ErrorLearnSamples(temp);
if (bestsolutionnumber > tempnumber)
{
bestsolution = new KnowlegeBasePCRules(temp);
bestsolutionnumber = tempnumber;
// delete
Console.WriteLine("NEWBEST " + bestsolutionnumber);
final_iter = 0;
}
else
{
final_iter++;
}
}
public override int Run(string[] args)
{
Console.WriteLine("Start");
fill_params(args);
foreach (string filenametra in System.IO.Directory.GetFiles(file_learn, "*tra.dat", System.IO.SearchOption.AllDirectories))
{
string filenameTST = filenametra.Replace("tra.dat", "tst.dat");
Console.WriteLine("Params get \nfile tra {0} \nfile name tst {1} ", filenametra, filenameTST);
Class_learn_set = new SampleSet(filenametra);
Console.WriteLine("Tra create");
Class_test_set = new SampleSet(filenameTST);
Console.WriteLine("Tst create");
conf = new InitBySamplesConfig();
conf.Init(Class_learn_set.CountVars);
// fill_conf();
conf.loadParams(confParams);
file_out = filenametra.Replace("tra.dat", ((InitBySamplesConfig)conf).IBSTypeFunc.ToString() + "_out.ufs");
Console.WriteLine("Conf Filed");
Class_Pittsburg = new PCFuzzySystem(Class_learn_set, Class_test_set);
Console.WriteLine("Classifier created");
generator = new GeneratorRulesBySamples();
Class_Pittsburg = generator.Generate(Class_Pittsburg, conf);
Console.WriteLine("Generation complite");
PCFSUFSWriter.saveToUFS(Class_Pittsburg, file_out);
StreamWriter sw = new StreamWriter(Path.Combine(file_learn, ((InitBySamplesConfig)conf).IBSTypeFunc.ToString() + "_log.txt"), true);
sw.WriteLine(filenametra + "\t" + Class_Pittsburg.ErrorLearnSamples(Class_Pittsburg.RulesDatabaseSet[0]));
sw.WriteLine(filenameTST + "\t" + Class_Pittsburg.ErrorTestSamples(Class_Pittsburg.RulesDatabaseSet[0]));
sw.Close();
Console.WriteLine("Saved");
}
return(1);
}
public override PCFuzzySystem TuneUpFuzzySystem(PCFuzzySystem Class, ILearnAlgorithmConf conf) // Здесь ведется оптимизация вашим алгоритмом
{
theFuzzySystem = Class;
// Console.WriteLine(theFuzzySystem.RulesDatabaseSet[0].TermsSet.Count);
iterMax = ((gsa_conf)conf).Количество_итераций;
MCount = ((gsa_conf)conf).Количество_частиц;
G0 = ((gsa_conf)conf).Гравитационная_постоянная;
alpha = ((gsa_conf)conf).Коэффициент_уменьшения;
epsilon = ((gsa_conf)conf).Малая_константа;
X = new KnowlegeBasePCRules[MCount];
Errors = new double[MCount];
mass = new double[MCount];
temp_c_Rule = new KnowlegeBasePCRules(theFuzzySystem.RulesDatabaseSet[0]);
X[0] = temp_c_Rule;
Errors[0] = theFuzzySystem.ErrorLearnSamples(X[0]);
ErrorZero = Errors[0];
ErrorBest = Errors[0];
BestSolution = new KnowlegeBasePCRules(theFuzzySystem.RulesDatabaseSet[0]);
R = new double[MCount][, , ];
speed = new double[MCount, X[0].TermsSet.Count, X[0].TermsSet[0].Parametrs.Count()];
for (int i = 0; i < MCount; i++)
{
R[i] = new double[MCount, X[0].TermsSet.Count, X[0].TermsSet[0].Parametrs.Count()];
}
RR = new double[MCount, MCount];
a = new double[MCount, X[0].TermsSet.Count, X[0].TermsSet[0].Parametrs.Count()];
for (int i = 1; i < MCount; i++)
{
temp_c_Rule = new KnowlegeBasePCRules(theFuzzySystem.RulesDatabaseSet[0]);
X[i] = temp_c_Rule;
for (int j = 0; j < X[i].TermsSet.Count; j++)
{
for (int k = 0; k < X[i].TermsSet[j].Parametrs.Count(); k++)
{
X[i].TermsSet[j].Parametrs[k] = GaussRandom.Random_gaussian(rand, X[i].TermsSet[j].Parametrs[k], 0.1 * (X[i].TermsSet[j].Parametrs[k])) + theFuzzySystem.LearnSamplesSet.InputAttributes[X[i].TermsSet[j].NumVar].Scatter * 0.05;
}
}
//theFuzzySystem.RulesDatabaseSet.Add(X[i]);
//theFuzzySystem.UnlaidProtectionFix(theFuzzySystem.RulesDatabaseSet.Count - 1);
//Errors[i] = theFuzzySystem.ErrorLearnSamples(X[i]);
//X[i] = theFuzzySystem.RulesDatabaseSet[theFuzzySystem.RulesDatabaseSet.Count - 1];
//theFuzzySystem.RulesDatabaseSet.Remove(X[i]);
theFuzzySystem.UnlaidProtectionFix(X[i]);
Errors[i] = theFuzzySystem.ErrorLearnSamples(X[i]);
}
for (int iter = 0; iter < iterMax; iter++)
{
//g(t) = G(0)*e^(-a*t/T);
G = G0 * Math.Pow(Math.E, ((-1) * alpha * iter / iterMax));
algorithm();
for (int r = 0; r < MCount; r++)
{
theFuzzySystem.UnlaidProtectionFix(X[r]);
Errors[r] = theFuzzySystem.ErrorLearnSamples(X[r]);
}
minValue = Errors.Min();
iminIndex = Errors.ToList().IndexOf(minValue);
if (minValue < ErrorBest)
{
ErrorBest = minValue;
BestSolution = new KnowlegeBasePCRules(X[iminIndex]);
}
}
//theFuzzySystem.RulesDatabaseSet[0] = BestSolution;
if (ErrorBest < ErrorZero)
{
theFuzzySystem.RulesDatabaseSet[0] = BestSolution;
}
return(theFuzzySystem);
}
private void weight()
{
double sum = 0;
double best = mass[0];
double worst = mass[0];
int[] index = new Int32[MCount];
int count = 0;
for (int i = 1; i < MCount; i++)
{
mass[i] = Errors[i];
if (mass[i] > best)
{
best = mass[i];
}
if (mass[i] < worst)
{
worst = mass[i];
}
}
for (int i = 0; i < MCount; i++)
{
if (mass[i] == best)
{
count++;
index[count - 1] = i;
}
}
if (count > 1)
{
for (int i = 1; i < count; i++)
{
///X[index[i]] = ;
int f = index[i];
KnowlegeBasePCRules temp_c_Rule = new KnowlegeBasePCRules(theFuzzySystem.RulesDatabaseSet[0]);
temp_c_Rule = new KnowlegeBasePCRules(theFuzzySystem.RulesDatabaseSet[0]);
X[f] = temp_c_Rule;
for (int j = 0; j < X[f].TermsSet.Count; j++)
{
for (int k = 0; k < X[f].TermsSet[j].Parametrs.Count(); k++)
{
X[f].TermsSet[j].Parametrs[k] = GaussRandom.Random_gaussian(rand, X[f].TermsSet[j].Parametrs[k], 0.1 * (X[f].TermsSet[j].Parametrs[k])) + theFuzzySystem.LearnSamplesSet.InputAttributes[X[f].TermsSet[j].NumVar].Scatter * 0.05;
}
}
theFuzzySystem.UnlaidProtectionFix(X[f]);
Errors[f] = theFuzzySystem.ErrorLearnSamples(X[f]);
mass[f] = Errors[f];
if (mass[f] > best)
{
i--;
}
}
}
for (int i = 0; i < MCount; i++)
{
mass[i] = (mass[i] - worst) / (best - worst);
sum = sum + mass[i];
}
for (int i = 0; i < MCount; i++)
{
mass[i] = mass[i] / sum;
}
}
//основные вычисления
public override PCFuzzySystem TuneUpFuzzySystem(PCFuzzySystem Classify, ILearnAlgorithmConf conf)
{
result = Classify;
rand = new Random();
Init(conf);
SetPopulation();
bool[] BEST = result.AcceptedFeatures;
double bestError = result.ErrorLearnSamples(result.RulesDatabaseSet[0]);
//отчистка консоли
Dictionary <bool[], double> PopulationWithAccuracy = new Dictionary <bool[], double>();
double accuracy = 0;
//запуск итераций
for (int it = 0; it < iter; it++)
{
//расчитыавем значение фитнес-функции
for (int i = 0; i < Population.Count; i++)
{
result.AcceptedFeatures = Population[i];
accuracy = result.ErrorLearnSamples(result.RulesDatabaseSet[0]);
PopulationWithAccuracy.Add(Population[i], accuracy);
}
Population.Clear();
foreach (var pair in PopulationWithAccuracy.OrderByDescending(pair => pair.Value))
{
Population.Add(pair.Key);
}
PopulationWithAccuracy.Clear();
double[] K = new double[Population.Count];
double sumK = 0;
double avK = 0;
for (int i = 0; i < Population.Count; i++)
{
result.AcceptedFeatures = Population[i];
K[i] = result.ErrorLearnSamples(result.RulesDatabaseSet[0]);
sumK += K[i];
}
avK = sumK / K.Length;
bool[] KDis = new bool[result.CountFeatures];
for (int i = 0; i < KDis.Length; i++)
{
if (rand.Next(0, 2) == 0)
{
KDis[i] = false;
}
else
{
KDis[i] = true;
}
}
//перебрать значения фитнес функции
//расчитыавем значение D
double dit;
dit = it;
double diter;
diter = iter;
bool D = true;
//расчитыавем значение Xfood
var Xfood = new bool[result.CountFeatures];
for (int t = 0; t < Xfood.Length; t++)
{
Xfood[t] = false;
for (int i = 0; i < Population.Count; i++)
{
Xfood[t] = merge(Population[i][t], KDis[t]);
}
}
//расчитываем значение Cfood
double Cfood = 2 * (1 - (dit / diter));
bool CDisfood;
if (Cfood <= 1)
{
CDisfood = false;
}
else
{
CDisfood = true;
}
//расчитываем значение Bfood
List <bool[]> Bfood = new List <bool[]>(Population.Count);
for (int i = 0; i < Population.Count; i++)
{
Bfood.Add(new bool[Population[i].Length]);
if (rand.Next(0, 2) == 0)
{
Kroofifood = false;
}
else
{
Kroofifood = true;
}
for (int t = 0; t < Bfood[i].Length; t++)
{
Bfood[i][t] = merge(merge(CDisfood, Kroofifood), Xfood[t]);
}
}
//расчитываем значение Bbest
List <bool[]> Bbest = new List <bool[]>(Population.Count);
for (int i = 0; i < Population.Count; i++)
{
Bbest.Add(new bool[Population[i].Length]);
if (rand.Next(0, 2) == 0)
{
Kroofifood = false;
}
else
{
Kroofifood = true;
}
for (int t = 0; t < Bbest[i].Length; t++)
{
Bbest[i][t] = merge(Kroofifood, Xfood[t]);
}
}
//расчитываем значение B
List <bool[]> B = new List <bool[]>(Population.Count);
for (int i = 0; i < Population.Count; i++)
{
B.Add(new bool[Population[i].Length]);
for (int t = 0; t < B[i].Length; t++)
{
B[i][t] = merge(Bfood[i][t], Bbest[i][t]);
}
}
//расчитываем значение F
List <bool[]> F = new List <bool[]>(Population.Count);
for (int i = 0; i < Population.Count; i++)
{
if (i == 0)
{
F.Add(new bool[Population[i].Length]);
for (int t = 0; t < F[i].Length; t++)
{
F[i][t] = merge(true, B[i][t]);
}
}
else
{
F.Add(new bool[Population[i].Length]);
for (int t = 0; t < F[i].Length; t++)
{
F[i][t] = merge(merge(true, B[i][t]), merge(false, F[i - 1][t]));
}
}
}
List <int>[] neihbors = new List <int> [Population.Count];
//расчитываем значение alocal
List <bool[]> alocal = new List <bool[]>(Population.Count);
for (int i = 0; i < Population.Count; i++)
{
alocal.Add(new bool[Population[i].Length]);
neihbors[i] = countneihbors(Population[i]);
for (int t = 0; t < alocal[i].Length; t++)
{
alocal[i][t] = false;
for (int j = 0; j < neihbors[i].Count; j++)
{
bool KRoofij = merge(KDis[t], Population[neihbors[i][j]][t]);
bool[] XRoofij = new bool[Population.Count];
XRoofij = CalcXroof(Population[i], Population[neihbors[i][j]]);
alocal[i][t] = merge(KRoofij, XRoofij[t]);
}
}
}
//расчитываем значение Cbest
bool Cbest;
if (rand.Next(0, 2) == 0)
{
Cbest = false;
}
else
{
Cbest = true;
}
//расчитываем значение atarget
List <bool[]> atarget = new List <bool[]>(Population.Count);
for (int i = 0; i < Population.Count; i++)
{
atarget.Add(new bool[Population[i].Length]);
bool[] XRoofibest = new bool[Population.Count];
XRoofibest = CalcXroof(Population[i], Population[0]);
for (int t = 0; t < alocal[i].Length; t++)
{
bool KRoofibest = KDis[t];
atarget[i][t] = merge(merge(Cbest, KRoofibest), XRoofibest[t]);
}
}
//расчитываем значение a
List <bool[]> a = new List <bool[]>(Population.Count);
for (int i = 0; i < Population.Count; i++)
{
a.Add(new bool[Population[i].Length]);
for (int t = 0; t < a[i].Length; t++)
{
a[i][t] = merge(atarget[i][t], alocal[i][t]);
}
}
//расчитываем значение N
List <bool[]> N = new List <bool[]>(Population.Count);
for (int i = 0; i < Population.Count; i++)
{
if (i == 0)
{
N.Add(new bool[Population[i].Length]);
for (int t = 0; t < N[i].Length; t++)
{
N[i][t] = merge(true, a[i][t]);
}
}
else
{
N.Add(new bool[Population[i].Length]);
for (int t = 0; t < F[i].Length; t++)
{
N[i][t] = merge(a[i][t], N[i - 1][t]);
}
}
}
//расчитываем значение dX
List <bool[]> dX = new List <bool[]>(Population.Count);
for (int i = 0; i < Population.Count; i++)
{
dX.Add(new bool[Population[i].Length]);
for (int t = 0; t < a[i].Length; t++)
{
dX[i][t] = merge(merge(F[i][t], N[i][t]), D);
}
}
//выводим значение BEST
// Console.Write("Значение BEST_ = ");
// Console.WriteLine(BEST);
//расчитываем значение X(t+dt)
for (int i = 0; i < Population.Count; i++)
{
Population[i] = new bool[Population[i].Length];
for (int t = 0; t < Population[i].Length; t++)
{
Population[i][t] = merge(Population[i][t], dX[i][t]);
}
}
for (int i = 0; i < Population.Count; i++)
{
result.AcceptedFeatures = Population[i];
double temp = result.ErrorLearnSamples(result.RulesDatabaseSet[0]);
if (temp < bestError)
{
BEST = Population[i];
bestError = temp;
}
}
double y = it;
if (y % 10 == 0 & y != 0)
{
Console.WriteLine(it);
Console.WriteLine(bestError);
}
}
result.AcceptedFeatures = BEST;
for (int i = 0; i < result.AcceptedFeatures.Length; i++)
{
if (result.AcceptedFeatures[i] == false)
{
Console.Write("0 ");
}
else
{
Console.Write("1 ");
}
}
Console.WriteLine();
Console.WriteLine(result.ErrorLearnSamples(result.RulesDatabaseSet[0]));
Console.WriteLine(result.ErrorTestSamples(result.RulesDatabaseSet[0]));
return(result);
}
public PittsburgElementofStorage(PCFuzzySystem Checker, KnowlegeBasePCRules SourceElem, string algName) : base(algName)
{
Element = new KnowlegeBasePCRules(SourceElem);
LearnError = Checker.ErrorLearnSamples(SourceElem);
TestError = Checker.ErrorTestSamples(SourceElem);
}
//основные вычисления
public override PCFuzzySystem TuneUpFuzzySystem(PCFuzzySystem Classify, ILearnAlgorithmConf conf)
{
rand = new Random();
result = Classify;
Init(conf);
SetPopulation();
KnowlegeBasePCRules BEST = new KnowlegeBasePCRules(result.RulesDatabaseSet[0]);
double bestError = result.ErrorLearnSamples(BEST);
//отчистка консоли
#if debug
Console.Clear();
#endif
//запуск итераций
for (int it = 0; it < iter; it++)
{
#if debug
//вывод номера итерации
Console.Write("Итерация __№__ = ");
Console.WriteLine(it);
#endif
//расчитыавем значение фитнес-функции
Population = ListPittsburgClassifierTool.SortRules(Population, result);
double[] K = new double[Population.Length];
for (int i = 0; i < Population.Length; i++)
{
K[i] = result.ErrorLearnSamples(Population[i]);
#if debug
Console.Write("Значние K[i1] = ");
Console.WriteLine(K[i]);
#endif
if (double.IsNaN(K[i]) || double.IsInfinity(K[i]))
{
result.UnlaidProtectionFix(Population[i]);
K[i] = result.ErrorLearnSamples(Population[i]);
#if debug
Console.Write("Значние K[i2] = ");
Console.WriteLine(K[i]);
#endif
}
}
Kworst = K.Max();
if (double.IsNaN(Kworst) || double.IsInfinity(Kworst))
{
int iworst = K.ToList().IndexOf(Kworst);
#if debug
Console.Write("Значние iworst = ");
Console.WriteLine(iworst);
#endif
}
#if debug
//вывод Kworst
Console.Write("Значние KWorst = ");
Console.WriteLine(Kworst);
#endif
Kbest = K.Min();
#if debug
//вывод Kbest
Console.Write("Значние Kbest = ");
Console.WriteLine(Kbest);
#endif
int ibest = K.ToList().IndexOf(Kbest);
#if debug
//вывод ibest
Console.Write("Значние ibest = ");
Console.WriteLine(ibest);
#endif
//перебрать значения фитнес функции
//расчитыавем значение D
double dit;
dit = it;
double diter;
diter = iter;
double D = (dmax * (rand.NextDouble() * 2 - 1) * (dit / diter));
//расчитываем значение rand1 для D
double rand1;
rand1 = D / (dmax * (it) / iter);
#if debug
//выводим значение rand1 для D
Console.Write("Значение Drand = ");
Console.WriteLine(rand1);
//выводим значение D
Console.Write("Значение __D__ = ");
Console.WriteLine(D);
#endif
//расчитыавем значение Xfood
double divide = K.Select(x => 1 / x).ToList().Sum();
var Xfood = new KnowlegeBasePCRules(Population[0]);
for (int t = 0; t < Xfood.TermsSet.Count; t++)
{
for (int p = 0; p < Xfood.TermsSet[t].CountParams; p++)
{
Xfood.TermsSet[t].Parametrs[p] = 0;
for (int i = 0; i < Population.Length; i++)
{
Xfood.TermsSet[t].Parametrs[p] += Population[i].TermsSet[t].Parametrs[p] / K[i];
#if debug
//выводим значение Xfood
Console.Write("Значение Xfood = ");
Console.WriteLine(Xfood.TermsSet[t].Parametrs[p]);
#endif
}
Xfood.TermsSet[t].Parametrs[p] /= divide;
}
}
#if debug
//вывод divide
Console.Write("Значние divide = ");
Console.WriteLine(divide);
#endif
//расчитываем значение Kfood
double Kfood = result.ErrorLearnSamples(Xfood);
if (double.IsNaN(Kfood) || double.IsInfinity(Kfood))
{
result.UnlaidProtectionFix(Xfood);
Kfood = result.ErrorLearnSamples(Xfood);
}
#if debug
//выводим значение Kfood
Console.Write("Значение Kfood = ");
Console.WriteLine(Kfood);
#endif
//расчитываем значение Cfood
double Cfood = 2 * (1 - (dit / diter));
#if debug
//выводим значение Cfood
Console.Write("Значение Cfood = ");
Console.WriteLine(Cfood);
#endif
//расчитываем значение Bfood
KnowlegeBasePCRules[] Bfood = new KnowlegeBasePCRules[Population.Length];
for (int i = 0; i < Population.Length; i++)
{
Bfood[i] = new KnowlegeBasePCRules(Population[i]);
double KRoofifood = CalcKroof(K[i], Kfood);
KnowlegeBasePCRules Xroofifood = new KnowlegeBasePCRules(CalcXroof(Population[i], Xfood));
for (int t = 0; t < Bfood[i].TermsSet.Count; t++)
{
for (int p = 0; p < Bfood[i].TermsSet[t].CountParams; p++)
{
Bfood[i].TermsSet[t].Parametrs[p] = Cfood * KRoofifood * Xroofifood.TermsSet[t].Parametrs[p];
#if debug
//выводим значение Bfood
Console.Write("Значение Bfood = ");
Console.WriteLine(Bfood[i].TermsSet[t].Parametrs[p]);
#endif
}
}
}
//расчитываем значение Bbest
KnowlegeBasePCRules[] Bbest = new KnowlegeBasePCRules[Population.Length];
for (int i = 0; i < Population.Length; i++)
{
Bbest[i] = new KnowlegeBasePCRules(Population[i]);
double KRoofifood = CalcKroof(K[i], K[ibest]);
KnowlegeBasePCRules Xroofifood = new KnowlegeBasePCRules(CalcXroof(Population[i], Population[ibest]));
for (int t = 0; t < Bbest[i].TermsSet.Count; t++)
{
for (int p = 0; p < Bbest[i].TermsSet[t].CountParams; p++)
{
Bbest[i].TermsSet[t].Parametrs[p] = KRoofifood * Xroofifood.TermsSet[t].Parametrs[p];
#if debug
//выводим значение Bbest
Console.Write("Значение Bbest = ");
Console.WriteLine(Bbest[i].TermsSet[t].Parametrs[p]);
#endif
}
}
}
//расчитываем значение B
KnowlegeBasePCRules[] B = new KnowlegeBasePCRules[Population.Length];
for (int i = 0; i < Population.Length; i++)
{
B[i] = new KnowlegeBasePCRules(Population[i]);
for (int t = 0; t < B[i].TermsSet.Count; t++)
{
for (int p = 0; p < B[i].TermsSet[t].CountParams; p++)
{
B[i].TermsSet[t].Parametrs[p] = Bfood[i].TermsSet[t].Parametrs[p] + Bbest[i].TermsSet[t].Parametrs[p];
#if debug
//выводим значение B
Console.Write("Значение __B__ = ");
Console.WriteLine(B[i].TermsSet[t].Parametrs[p]);
#endif
}
}
}
//расчитываем значение F
KnowlegeBasePCRules[] F = new KnowlegeBasePCRules[Population.Length];
for (int i = 0; i < Population.Length; i++)
{
if (i == 0)
{
F[i] = new KnowlegeBasePCRules(Population[i]);
for (int t = 0; t < F[i].TermsSet.Count; t++)
{
for (int p = 0; p < F[i].TermsSet[t].CountParams; p++)
{
F[i].TermsSet[t].Parametrs[p] = Vf * B[i].TermsSet[t].Parametrs[p];
#if debug
//выводим значение F
Console.Write("Значение __F__ = ");
Console.WriteLine(F[i].TermsSet[t].Parametrs[p]);
#endif
}
}
}
else
{
F[i] = new KnowlegeBasePCRules(Population[i]);
for (int t = 0; t < F[i].TermsSet.Count; t++)
{
for (int p = 0; p < F[i].TermsSet[t].CountParams; p++)
{
F[i].TermsSet[t].Parametrs[p] = Vf * B[i].TermsSet[t].Parametrs[p] + wf * F[i - 1].TermsSet[t].Parametrs[p];
#if debug
//выводим значение F
Console.Write("Значение __F__ = ");
Console.WriteLine(F[i].TermsSet[t].Parametrs[p]);
#endif
}
}
}
}
List <int> [] neihbors = new List <int> [Population.Length];
//расчитываем значение alocal
KnowlegeBasePCRules[] alocal = new KnowlegeBasePCRules[Population.Length];
for (int i = 0; i < Population.Length; i++)
{
alocal[i] = new KnowlegeBasePCRules(Population[i]);
neihbors[i] = countneihbors(Population[i]);
/*
#if debug
* //вывод значений количества соседей
* for (int g = 0; g < Population.Length; g++)
* {
* Console.Write("Знаение countneihbors = ");
* Console.WriteLine(countneihbors(Population[g]));
* }
#endif
*/
for (int t = 0; t < alocal[i].TermsSet.Count; t++)
{
for (int p = 0; p < alocal[i].TermsSet[t].CountParams; p++)
{
alocal[i].TermsSet[t].Parametrs[p] = 0;
for (int j = 0; j < neihbors[i].Count; j++)
{
double KRoofij = CalcKroof(K[i], K[neihbors[i][j]]);
KnowlegeBasePCRules XRoofij = new KnowlegeBasePCRules(CalcXroof(Population[i], Population[neihbors[i][j]]));
alocal[i].TermsSet[t].Parametrs[p] += KRoofij * XRoofij.TermsSet[t].Parametrs[p];
#if debug
//выводим значение alocal
Console.Write("Знаение alocal = ");
Console.WriteLine(alocal[i].TermsSet[t].Parametrs[p]);
#endif
}
}
}
}
//расчитываем значение Cbest
double Cbest = 2 * (rand.NextDouble() - (dit / diter));
#if debug
//выводим значение Cbest
Console.Write("Значение Сbest = ");
Console.WriteLine(Cbest);
#endif
//расчитываем значение rand для Cbest
double rand2;
rand2 = it / iter - Cbest / 2;
#if debug
//выводим значение rand2 для Cbest
Console.Write("Значение Crand = ");
Console.WriteLine(rand2);
#endif
//расчитываем значение atarget
KnowlegeBasePCRules[] atarget = new KnowlegeBasePCRules[Population.Length];
for (int i = 0; i < Population.Length; i++)
{
atarget[i] = new KnowlegeBasePCRules(Population[i]);
double KRoofibest = CalcKroof(K[i], K[ibest]);
KnowlegeBasePCRules XRoofibest = new KnowlegeBasePCRules(CalcXroof(Population[i], Population[ibest]));
for (int t = 0; t < alocal[i].TermsSet.Count; t++)
{
for (int p = 0; p < atarget[i].TermsSet[t].CountParams; p++)
{
atarget[i].TermsSet[t].Parametrs[p] = Cbest * KRoofibest * XRoofibest.TermsSet[t].Parametrs[p];
#if debug
//выводим значение atarget
Console.Write("Знание atarget = ");
Console.WriteLine(atarget[i].TermsSet[t].Parametrs[p]);
#endif
}
}
}
//расчитываем значение a
KnowlegeBasePCRules[] a = new KnowlegeBasePCRules[Population.Length];
for (int i = 0; i < Population.Length; i++)
{
a[i] = new KnowlegeBasePCRules(Population[i]);
for (int t = 0; t < a[i].TermsSet.Count; t++)
{
for (int p = 0; p < a[i].TermsSet[t].CountParams; p++)
{
a[i].TermsSet[t].Parametrs[p] = atarget[i].TermsSet[t].Parametrs[p] + alocal[i].TermsSet[t].Parametrs[p];
#if debug
//выводим значение a
Console.Write("Значение __a__ = ");
Console.WriteLine(a[i].TermsSet[t].Parametrs[p]);
#endif
}
}
}
//расчитываем значение N
KnowlegeBasePCRules[] N = new KnowlegeBasePCRules[Population.Length];
for (int i = 0; i < Population.Length; i++)
{
if (i == 0)
{
N[i] = new KnowlegeBasePCRules(Population[i]);
for (int t = 0; t < N[i].TermsSet.Count; t++)
{
for (int p = 0; p < F[i].TermsSet[t].CountParams; p++)
{
N[i].TermsSet[t].Parametrs[p] = Vf * a[i].TermsSet[t].Parametrs[p];
#if debug
//выводим значение N
Console.Write("Значение __N__ = ");
Console.WriteLine(N[i].TermsSet[t].Parametrs[p]);
#endif
}
}
}
else
{
N[i] = new KnowlegeBasePCRules(Population[i]);
for (int t = 0; t < F[i].TermsSet.Count; t++)
{
for (int p = 0; p < N[i].TermsSet[t].CountParams; p++)
{
N[i].TermsSet[t].Parametrs[p] = nmax * a[i].TermsSet[t].Parametrs[p] + wn * N[i - 1].TermsSet[t].Parametrs[p];
#if debug
//выводим значение N
Console.Write("Значение __N__ = ");
Console.WriteLine(N[i].TermsSet[t].Parametrs[p]);
#endif
}
}
}
}
//расчитываем значение dX
KnowlegeBasePCRules[] dX = new KnowlegeBasePCRules[Population.Length];
for (int i = 0; i < Population.Length; i++)
{
dX[i] = new KnowlegeBasePCRules(Population[i]);
for (int t = 0; t < a[i].TermsSet.Count; t++)
{
for (int p = 0; p < a[i].TermsSet[t].CountParams; p++)
{
dX[i].TermsSet[t].Parametrs[p] = F[i].TermsSet[t].Parametrs[p] + N[i].TermsSet[t].Parametrs[p] + D;
#if debug
//выводим значение dX
Console.Write("Значение _dX__ = ");
Console.WriteLine(dX[i].TermsSet[t].Parametrs[p]);
#endif
}
}
}
//выводим значение BEST
// Console.Write("Значение BEST_ = ");
// Console.WriteLine(BEST);
//расчитываем значение X(t+dt)
for (int i = 0; i < Population.Length; i++)
{
Population[i] = new KnowlegeBasePCRules(Population[i]);
for (int t = 0; t < Population[i].TermsSet.Count; t++)
{
for (int p = 0; p < F[i].TermsSet[t].CountParams; p++)
{
Population[i].TermsSet[t].Parametrs[p] = Population[i].TermsSet[t].Parametrs[p] + calcdeltat(ct) * dX[i].TermsSet[t].Parametrs[p];
#if debug
//выводим значение Xnew
Console.Write("Знание X(t+dt) = ");
Console.WriteLine(Population[i].TermsSet[t].Parametrs[p]);
#endif
}
}
}
for (int i = 0; i < Population.Length; i++)
{
double temp = result.ErrorLearnSamples(Population[i]);
if (double.IsNaN(temp) || double.IsInfinity(temp))
{
result.UnlaidProtectionFix(Xfood);
temp = result.ErrorLearnSamples(Population[i]);
}
if (temp < bestError)
{
BEST = new KnowlegeBasePCRules(Population[i]);
bestError = temp;
}
}
double y = it;
if (y % 10 == 0 & y != 0)
{
Console.WriteLine(it);
Console.WriteLine(bestError);
}
#if debug
// выводим значение лучшей ошибки Kbest
Console.Write("Значние BestError = ");
Console.WriteLine(bestError);
Console.WriteLine(".");
#endif
}
result.RulesDatabaseSet[0] = BEST;
return(result);
}
public override PCFuzzySystem TuneUpFuzzySystem(PCFuzzySystem Classifier, ILearnAlgorithmConf conf)
{
Random rand = new Random(DateTime.Now.Millisecond);
PCFuzzySystem result = Classifier;
BW.DoWork += new DoWorkEventHandler(BW_DoWork);
BW.RunWorkerCompleted += new RunWorkerCompletedEventHandler(BW_RunWorkerCompleted);
BW.RunWorkerAsync();
MultiGoalOptimaze_conf config = conf as MultiGoalOptimaze_conf;
string PathAlg = (new FileInfo(Application.ExecutablePath)).DirectoryName + "\\FS\\";
config.Init2(PathAlg, Classifier.LearnSamplesSet.FileName);
countFuzzySystem = config.Итераций_алгоритма;
allowSqare = config.Допустимый_процент_перекрытия_по_площади_термов / 100;
allowBorder = config.Допустимый_процент_перекрытия_по_границам / 100;
int seedPath = rand.Next();
sizePercent = config.азмер_шага_по_точности;
sizeComplexity = config.азмер_шага_по_сложности;
sizeInteraply = config.азмер_шага_по_интерпретируемости;
diviver = config.Уменьшать_шаги_в;
trysBeforeDivide = config.Уменьшать_шаг_после;
path = config.path;
dataSetName = config.dataSetName;
toMany = config.азрешено_похожих_систем;
isPSO = config.toBool(config.Использовать_АРЧ);
isANT = config.toBool(config.Использовать_НАМК);
isBEE = config.toBool(config.Использовать_САПК);
isES = config.toBool(config.Использовать_ЕС);
isGA = config.toBool(config.Использовать_ГА);
// isBFO = config.toBool(config.Использовать_АПБ);
isTermShrink = config.toBool(config.Удалять_термы);
isRuleShrink = config.toBool(config.Удалять_правила);
isUnionTerm = config.toBool(config.Объединять_термы);
isLindBreakCross = config.toBool(config.Исключать_пересечение_лигвистически_далеких_термов);
countANT = config.Использовать_НАМК_раз_за_такт;
countPSO = config.Использовать_за_такт_АРЧ_раз;
countBEE = config.Использовать_САПК_раз_за_такт;
countES = config.Использовать_ЕС_раз_за_такт;
countGA = config.Использовать_ГА_раз_за_такт;
// countBFO = config.Использовать_за_такт_АПБ_раз;
typeComplexity = (int)config.Критерий_сложности;
typeInterpreting = (int)config.Критерий_интерпретируемости;
List <AbstractNotSafeLearnAlgorithm> learnAlgorithms = initAlgoritms();
List <ILearnAlgorithmConf> learnAlgorithmsconfig = initAlgoritmsConfigs(Classifier.CountFeatures);
List <double> ValueLPercent = new List <double>();
List <double> ValueTPercent = new List <double>();
List <double> ValueComplexity = new List <double>();
List <double> ValueInterability = new List <double>();
List <double> SummaryGoods = new List <double>();
List <KnowlegeBasePCRules> Storage = new List <KnowlegeBasePCRules>();
List <int> candidate = new List <int>();
KnowlegeBasePCRules Best = result.RulesDatabaseSet[0];
// result.RulesDatabaseSet[0] = Best;
baseLearn = (result.ErrorLearnSamples(result.RulesDatabaseSet[0]));
ValueLPercent.Add(baseLearn);
ValueTPercent.Add(result.ErrorTestSamples(result.RulesDatabaseSet[0])); // ClassifyTestSamples());
baseComplexity = getComplexity(result);
ValueComplexity.Add(baseComplexity);
baseIntebility = getInterpreting(result, allowBorder, allowSqare);
ValueInterability.Add(baseIntebility);
Storage.Add(Best);
int NSCount = 0;
int deleted = 0;
for (int numberStep = 0; numberStep < countFuzzySystem; numberStep++)
{
bool mustToDivide = true;
int usedAlg = 0;
for (int tr = 0; tr < trysBeforeDivide; tr++)
{
deleted = 0;
// Parallel.For(0, learnAlgorithms.Count(), i =>
usedAlg = 0;
for (int i = 0; i < learnAlgorithms.Count(); i++)
{
Console.WriteLine("F****d in Storage.Add(new c_Rules(Best))");
Storage.Add(new KnowlegeBasePCRules(Best));
Console.WriteLine("F****d in result.RulesDatabaseSet.Clear()");
result.RulesDatabaseSet.Clear();
Console.WriteLine("F****d in result.RulesDatabaseSet.Add( Storage[Storage.Count - 1])");
result.RulesDatabaseSet.Add(Storage[Storage.Count - 1]);
usedAlg++;
bool before_VAlue = true;
try
{
learnAlgorithms[i].TuneUpFuzzySystem(result, learnAlgorithmsconfig[i]);
GC.Collect();
before_VAlue = false;
ValueLPercent.Add(result.ErrorLearnSamples(result.RulesDatabaseSet[0])); // ClassifyLearnSamples());
ValueTPercent.Add(result.ErrorTestSamples(result.RulesDatabaseSet[0])); //ClassifyTestSamples());
ValueComplexity.Add(getComplexity(result));
ValueInterability.Add(getInterpreting(result, allowBorder, allowSqare));
double temp = ValueLPercent[ValueLPercent.Count - 1] + ValueComplexity[ValueComplexity.Count() - 1] + ValueInterability[ValueInterability.Count() - 1];
Storage[Storage.Count - 1] = result.RulesDatabaseSet[0];
if (double.IsNaN(temp))
{
Console.WriteLine("FuckAlarm " + i.ToString() + learnAlgorithms[i].ToString() + " is NAN");
ValueLPercent.RemoveAt(ValueLPercent.Count() - 1);
ValueTPercent.RemoveAt(ValueTPercent.Count() - 1);
ValueComplexity.RemoveAt(ValueComplexity.Count() - 1);
ValueInterability.RemoveAt(ValueInterability.Count() - 1);
Storage.RemoveAt(Storage.Count() - 1);
usedAlg--;
}
}
catch (Exception)
{
if (before_VAlue)
{
Console.WriteLine("FuckAlarm " + i.ToString() + learnAlgorithms[i].ToString() + " before VAlue");
}
else
{
Console.WriteLine("FuckAlarm " + i.ToString() + learnAlgorithms[i].ToString() + " after VAlue");
ValueLPercent.RemoveAt(ValueLPercent.Count() - 1);
ValueTPercent.RemoveAt(ValueTPercent.Count() - 1);
ValueComplexity.RemoveAt(ValueComplexity.Count() - 1);
ValueInterability.RemoveAt(ValueInterability.Count() - 1);
Storage.RemoveAt(Storage.Count() - 1);
}
}
NSCount++;
Console.WriteLine("F****d in ResultShow");
ResultShow += "[" + NSCount.ToString() + "]\t" + ValueLPercent[ValueLPercent.Count() - 1].ToString() + "\t" + ValueTPercent[ValueTPercent.Count() - 1].ToString() +
"\t" + ValueComplexity[ValueComplexity.Count() - 1].ToString() + "\t" + ValueInterability[ValueInterability.Count() - 1].ToString() + Environment.NewLine;
// i++;
}
//);
Console.WriteLine("F****d in deleted");
deleted = removeDublicate(ValueLPercent, ValueComplexity, ValueInterability, ValueTPercent, Storage, rand);
usedAlg -= deleted;
Console.WriteLine("F****d in candidate");
candidate = canBeNext(ValueLPercent, ValueComplexity, ValueInterability);
if (candidate.Count() > 0)
{
mustToDivide = false; break;
}
}
if (mustToDivide)
{
MessageBox.Show("Divided happend ");
sizePercent = sizePercent / diviver;
sizeComplexity = sizeComplexity / diviver;
sizeInteraply = sizeInteraply / diviver;
continue;
}
Console.WriteLine("F****d in SummaryGoods");
SummaryGoods = reCalcSummary(SummaryGoods, ValueLPercent, ValueComplexity, ValueInterability);
Console.WriteLine("F****d in indexofBest");
int indexofBest = getNewBest(candidate, SummaryGoods);
if (usedAsNext.ContainsKey(indexofBest))
{
usedAsNext[indexofBest]++;
}
else
{
usedAsNext.Add(indexofBest, 1);
}
Console.WriteLine("Best");
Best = Storage[indexofBest];
Console.WriteLine("F****d in for (int i = (Storage.Count - learnAlgorithms.Count); i < Storage.Count(); i++)");
int toSaveCounter = NSCount - usedAlg;
for (int i = (Storage.Count - usedAlg); i < Storage.Count(); i++)
{
result.RulesDatabaseSet[0] = Storage[i];
saveFS(result, path, dataSetName, seedPath, numberStep, toSaveCounter, Best.Equals(result.RulesDatabaseSet[0]));
toSaveCounter++;
}
Console.WriteLine("F****d in result.RulesDatabaseSet[0] = Best;");
result.RulesDatabaseSet[0] = Best;
Console.WriteLine("F****d in End");
baseLearn = (result.ErrorLearnSamples(result.RulesDatabaseSet[0]));// ClassifyLearnSamples();
baseComplexity = getComplexity(result);
baseIntebility = getInterpreting(result, allowBorder, allowSqare);
candidate.Clear();
GC.Collect();
}
isEnd = true;
Thread.Sleep(1000);
result.RulesDatabaseSet[0].TermsSet.Trim();
return(result);
}
public void calc_Error(PCFuzzySystem error_checker)
{
error_checker.UnlaidProtectionFix(hrom_vector.Core_Check);
Error = error_checker.ErrorLearnSamples(hrom_vector.Core_Check);
}
public override PCFuzzySystem TuneUpFuzzySystem(PCFuzzySystem Approximate, ILearnAlgorithmConf conf) // + override
{
result = Approximate;
List <KnowlegeBasePCRules> Archive = new List <KnowlegeBasePCRules>();
List <double> ErrorsArchive = new List <double>();
var config = (DynamicTuneConf)conf;
maxError = config.MaxError;
RuleCount = config.RulesCount;
TryCount = config.TryCount;
double error = result.ErrorLearnSamples(result.RulesDatabaseSet[0]);
var kbToOptimize = new KnowlegeBasePCRules(result.RulesDatabaseSet[0]);
var kbBest = new KnowlegeBasePCRules(kbToOptimize);
double errorBefore = Double.MaxValue;
result.UnlaidProtectionFix(kbToOptimize);
List <input_space> variable_spaces = new List <input_space>();
for (int i = 0; i < result.LearnSamplesSet.InputAttributes.Count; i++)
{
List <Term> terms_of_variable = new List <Term>();
terms_of_variable = kbToOptimize.TermsSet.Where(term => term.NumVar == i).ToList();
variable_spaces.Add(new input_space(terms_of_variable, i));
}
int indexRegion = -1,
indexVar = -1,
number_of_input_variables = variable_spaces.Count;
int tryCount = 0;
while (error > maxError)
{
if (Double.IsInfinity(error))
{
throw new Exception("Something went wrong, error is Infinity, region: " + indexRegion);
}
if (Double.IsNaN(error))
{
throw new Exception("Something went wrong, error is NaN, region: " + indexRegion);
}
region_side[][] sides = new region_side[number_of_input_variables][];
for (int i = 0; i < number_of_input_variables; i++)
{
sides[i] = variable_spaces[i].get_region_sides();
}
var cartresult = CartesianProduct.Get(sides);
List <region2> regions = new List <region2>();
foreach (var x in cartresult)
{
regions.Add(new region2(x.ToList(), result, variable_spaces));
}
List <double> region_errors = regions.Select(x => x.region_error()).ToList();
indexRegion = region_errors.IndexOf(region_errors.Max());
for (int i = 0; i < region_errors.Count; i++)
{
if (Double.IsNaN(region_errors[i]) || Double.IsInfinity(region_errors[i]) ||
Double.IsNegativeInfinity(region_errors[i]) || Double.IsPositiveInfinity(region_errors[i]))
{
region_errors[i] = 0;
}
}
List <double> variable_errors = regions[indexRegion].variable_errors();
bool check1 = false;
for (int i = 1; i < variable_errors.Count; i++)
{
if (variable_errors[i - 1] != variable_errors[i])
{
check1 = true;
break;
}
}
if (!check1)
{
indexVar = StaticRandom.Next(variable_errors.Count - 1);
}
else
{
indexVar = variable_errors.IndexOf(variable_errors.Max());
}
Term new_term = regions[indexRegion].new_term(indexVar);
result.RulesDatabaseSet[0] = kbToOptimize;
kbToOptimize.TermsSet.Add(new_term);
// Rules (CHECK REFERENCE TYPES)
int @var = indexVar;
var rulesLeft = kbToOptimize.RulesDatabase.Where(
rule => rule.ListTermsInRule.Contains(regions[indexRegion].sides[indexVar].left)).ToList();
var rulesRight = kbToOptimize.RulesDatabase.Where(
rule => rule.ListTermsInRule.Contains(regions[indexRegion].sides[indexVar].right)).ToList();
for (int j = 0; j < rulesLeft.Count; j++)
{
int[] order = new int[rulesLeft[j].ListTermsInRule.Count];
for (int k = 0; k < rulesLeft[j].ListTermsInRule.Count; k++)
{
Term temp_term = rulesLeft[j].ListTermsInRule[k];
if (temp_term == regions[indexRegion].sides[indexVar].left)
{
temp_term = new_term;
}
order[k] = kbToOptimize.TermsSet.FindIndex(x => x == temp_term);
}
///!!!!
string temp_approx_Values = kbToOptimize.RulesDatabase[j].LabelOfClass;
/* double[] temp_approx_RegressionConstantConsequent =
* kbToOptimize.RulesDatabase[j].RegressionConstantConsequent.Clone() as double[];
*/
PCRule temp_rule = new PCRule(
kbToOptimize.TermsSet, order, temp_approx_Values);
// double[] dC = null;
//!!!
temp_rule.LabelOfClass = KNNClassName.NearestClass(result, temp_rule.ListTermsInRule.ToList());
kbToOptimize.RulesDatabase.Add(temp_rule);
//!!!
rulesLeft[j].LabelOfClass = KNNClassName.NearestClass(result, rulesLeft[j].ListTermsInRule.ToList());
// rulesLeft[j].RegressionConstantConsequent = (double[])dC.Clone();
}
foreach (var rule in rulesRight)
{
//!!!
rule.LabelOfClass = KNNClassName.NearestClass(
result, rule.ListTermsInRule.ToList());
// rule.RegressionConstantConsequent = dC;
}
variable_spaces[indexVar].terms.Add(new_term);
variable_spaces[indexVar].terms.Sort(new CompararerByPick());
// Re-evaluate the system's error
//!!!!
error = result.ErrorLearnSamples(kbToOptimize);
if ((kbToOptimize.RulesDatabase.Count > config.RulesCount))
{
break;
}
#if Console
Console.WriteLine(error + " " + kbToOptimize.TermsSet.Count + " terms\n");
for (int i = 0; i < variable_spaces.Count; i++)
{
Console.WriteLine(variable_spaces[i].terms.Count + " термов по " + i + "му параметру\n");
}
#endif
result.RulesDatabaseSet[0] = kbToOptimize;
// Get the best knowledge base on the 1st place
if (error < errorBefore)
{
kbBest = new KnowlegeBasePCRules(kbToOptimize);
errorBefore = error;
tryCount = 0;
}
else
{
tryCount++;
}
if (tryCount > TryCount)
{
break;
}
}
result.RulesDatabaseSet[0] = kbBest;
RuleCount = kbBest.RulesDatabase.Count;
TryCount = tryCount;
return(result);
}
public override PCFuzzySystem TuneUpFuzzySystem(PCFuzzySystem Classifier, ILearnAlgorithmConf conf)
{
Init(conf);
KnowlegeBasePCRules temp_c_Rule = new KnowlegeBasePCRules(Classifier.RulesDatabaseSet[0]);
PCFuzzySystem result = Classifier;
string file_string = @"..\logs_" + result.TestSamplesSet.FileName + ".txt";
string file_string_to_txt = @"..\result_" + result.TestSamplesSet.FileName + ".txt";
for (int t = 0; t < population_count; t++)
{
monkey[t] = new KnowlegeBasePCRules(result.RulesDatabaseSet[0]);
if (t > 3)
{
for (int k = 0; k < result.RulesDatabaseSet[0].TermsSet.Count; k++)
{
for (int q = 0; q < result.RulesDatabaseSet[0].TermsSet[k].CountParams; q++)
{
//monkey[t].TermsSet[k].Parametrs[q] = StaticRandom.NextDouble() * (result.RulesDatabaseSet[0].TermsSet[k].Max - result.RulesDatabaseSet[0].TermsSet[k].Min);
monkey[t].TermsSet[k].Parametrs[q] = GaussRandom.Random_gaussian(rand, monkey[t].TermsSet[k].Parametrs[q], monkey[t].TermsSet[k].Parametrs[q] * 0.05);
}
}
}
result.UnlaidProtectionFix(monkey[t]);
// delete
testvals[t] = result.ErrorLearnSamples(monkey[t]);
Console.WriteLine("Begin: " + t.ToString() + " " + iter.ToString() + " " + testvals[t].ToString());
}
bestsolution = new KnowlegeBasePCRules(monkey.SelectBest(result, 1)[0]);
bestsolutionnumber = result.ErrorLearnSamples(bestsolution);
deltaLength = result.RulesDatabaseSet[0].TermsSet.Sum(x => x.Parametrs.Length);
if (debug)
{
using (System.IO.StreamWriter file = new System.IO.StreamWriter(file_string, true))
{
file.WriteLine(DateTime.Today.ToString() + "\t" + result.LearnSamplesSet.FileName);
file.WriteLine("Parameters:");
file.WriteLine("Population\t" + population_count.ToString());
file.WriteLine("Iteration count\t" + iter_amount.ToString());
file.WriteLine("Crawl count\t" + crawl_iter.ToString());
file.WriteLine("Jump count\t" + jump_iter.ToString());
file.WriteLine("Somersault count\t" + somersault_iter.ToString());
file.WriteLine("Crawl step\t" + step.ToString()); // crawl step
file.WriteLine("Jump step\t" + watch_jump_parameter.ToString());
file.WriteLine("Somersault left border\t" + somersault_interval_left.ToString());
file.WriteLine("Somersault right border\t" + somersault_interval_right.ToString());
file.WriteLine("\t\tMonkeys");
file.Write("Iterations\t");
for (int t = 0; t < population_count; t++)
{
file.Write("\t" + t);
}
file.WriteLine();
file.Write("0\tbegin");
for (int t = 0; t < population_count; t++)
{
file.Write("\t" + testvals[t].ToString());
}
// excel вставки
// наибольший в таблице
file.WriteLine();
}
}
//iter_amount = somersault_iter * (1 + jump_iter * (1 + crawl_iter));
iter_amount = (((crawl_iter + jump_iter) * jump_iter) + somersault_iter) * somersault_iter;
for (int r = 0; r < somersault_iter; r++)
{
for (int t = 0; t < jump_iter; t++)
{
for (int e = 0; e < crawl_iter; e++)
{
iter++;
oneClimb(result, deltaLength, step);
CheckForBest(result);
//Console.WriteLine(iter_amount.ToString() + "/" + iter.ToString
// дебаг
if (debug)
{
using (System.IO.StreamWriter file = new System.IO.StreamWriter(file_string, true))
{
file.Write(iter.ToString() + "\tcrawl");
for (int p = 0; p < population_count; p++)
{
file.Write("\t" + result.ErrorLearnSamples(monkey[p]).ToString());
}
file.WriteLine();
}
}
}
for (int e = 0; e < jump_iter; e++)
{
iter++;
oneWatchJump(result);
//Console.WriteLine(iter_amount.ToString() + "/" + iter.ToString());
CheckForBest(result);
// дебаг
if (debug)
{
using (System.IO.StreamWriter file = new System.IO.StreamWriter(file_string, true))
{
file.Write(iter.ToString() + "\tlocaljump");
for (int p = 0; p < population_count; p++)
{
file.Write("\t" + result.ErrorLearnSamples(monkey[p]).ToString());
}
file.WriteLine();
}
}
}
}
for (int e = 0; e < somersault_iter; e++)
{
iter++;
oneGlobalJump(result);
CheckForBest(result);
// дебаг
if (debug)
{
using (System.IO.StreamWriter file = new System.IO.StreamWriter(file_string, true))
{
file.Write(iter.ToString() + "\tglobaljump");
for (int p = 0; p < population_count; p++)
{
file.Write("\t" + result.ErrorLearnSamples(monkey[p]).ToString());
}
file.WriteLine();
}
}
Console.WriteLine(iter_amount.ToString() + "/" + iter.ToString());
}
}
//Console.WriteLine(final_iter.ToString() + "/" + final_counter.ToString());
//FOR VICTORY!!!
while (final_iter < final_counter)
{
step *= 0.9;
watch_jump_parameter *= 0.9;
somersault_interval_left *= 0.9;
somersault_interval_right *= 0.9;
for (int r = 0; r < somersault_iter; r++)
{
oneClimb(result, deltaLength, step);
CheckForBest(result);
iter++;
}
for (int t = 0; t < jump_iter; t++)
{
oneWatchJump(result);
CheckForBest(result);
iter++;
}
for (int e = 0; e < crawl_iter; e++)
{
oneGlobalJump(result);
CheckForBest(result);
iter++;
}
Console.WriteLine(iter_amount.ToString() + "/" + iter.ToString());
}
/* for (int t = 0; t < population_count; t++)
* if (result.ErrorLearnSamples(monkey[best]) < result.ErrorLearnSamples(monkey[t]))
* best = t; */
CheckForBest(result);
if (bestsolutionnumber <= result.ErrorLearnSamples(result.RulesDatabaseSet[0]))
{
result.RulesDatabaseSet[0] = bestsolution;
}
iter = 0;
if (debug)
{
using (System.IO.StreamWriter file = new System.IO.StreamWriter(file_string, true))
{
file.WriteLine("Results\t" + result.ErrorLearnSamples(bestsolution).ToString() + "\t" + result.ErrorTestSamples(bestsolution).ToString());
}
}
if (totxt)
{
using (System.IO.StreamWriter file_result = new System.IO.StreamWriter(file_string_to_txt, true))
{
file_result.WriteLine(result.ErrorLearnSamples(bestsolution).ToString() + "\t" + result.ErrorTestSamples(bestsolution).ToString());
}
}
return(result);
}
private static void writeAboutEstimates(XmlWriter writer, PCFuzzySystem Classifier)
{
writer.WriteStartElement("Estimates");
if (Classifier.TestSamplesSet != null)
{
writer.WriteAttributeString("Count", XmlConvert.ToString(22));
writer.WriteStartElement("Estimate");
writer.WriteAttributeString("Table", Classifier.LearnSamplesSet.FileName);
writer.WriteAttributeString("Type", "PrecisionPercent");
writer.WriteAttributeString("Value", XmlConvert.ToString(Classifier.ClassifyLearnSamples(Classifier.RulesDatabaseSet[0])));
writer.WriteEndElement();
writer.WriteStartElement("Estimate");
writer.WriteAttributeString("Table", Classifier.LearnSamplesSet.FileName);
writer.WriteAttributeString("Type", "ErrorPercent");
writer.WriteAttributeString("Value", XmlConvert.ToString(Classifier.ErrorLearnSamples(Classifier.RulesDatabaseSet[0])));
writer.WriteEndElement();
writer.WriteStartElement("Estimate");
writer.WriteAttributeString("Table", Classifier.TestSamplesSet.FileName);
writer.WriteAttributeString("Type", "PrecisionPercent");
writer.WriteAttributeString("Value", XmlConvert.ToString(Classifier.ClassifyTestSamples(Classifier.RulesDatabaseSet[0])));
writer.WriteEndElement();
writer.WriteStartElement("Estimate");
writer.WriteAttributeString("Table", Classifier.TestSamplesSet.FileName);
writer.WriteAttributeString("Type", "ErrorPercent");
writer.WriteAttributeString("Value", XmlConvert.ToString(Classifier.ErrorTestSamples(Classifier.RulesDatabaseSet[0])));
writer.WriteEndElement();
}
else
{
writer.WriteAttributeString("Count", XmlConvert.ToString(20));
writer.WriteStartElement("Estimate");
writer.WriteAttributeString("Table", Classifier.LearnSamplesSet.FileName);
writer.WriteAttributeString("Type", "PrecisionPercent");
writer.WriteAttributeString("Value", XmlConvert.ToString(Classifier.ClassifyLearnSamples(Classifier.RulesDatabaseSet[0])));
writer.WriteEndElement();
writer.WriteStartElement("Estimate");
writer.WriteAttributeString("Table", Classifier.LearnSamplesSet.FileName);
writer.WriteAttributeString("Type", "ErrorPercent");
writer.WriteAttributeString("Value", XmlConvert.ToString(Classifier.ErrorLearnSamples(Classifier.RulesDatabaseSet[0])));
writer.WriteEndElement();
}
writer.WriteStartElement("Estimate");
writer.WriteAttributeString("Type", "GIBNormal");
writer.WriteAttributeString("Value", XmlConvert.ToString(Classifier.getGIBNormal()));
writer.WriteEndElement();
writer.WriteStartElement("Estimate");
writer.WriteAttributeString("Type", "GIBSumStraigh");
writer.WriteAttributeString("Value", XmlConvert.ToString(Classifier.getGIBSumStrait()));
writer.WriteEndElement();
writer.WriteStartElement("Estimate");
writer.WriteAttributeString("Type", "GIBSumReverse");
writer.WriteAttributeString("Value", XmlConvert.ToString(Classifier.getGIBSumReverse()));
writer.WriteEndElement();
writer.WriteStartElement("Estimate");
writer.WriteAttributeString("Type", "GICNormal");
writer.WriteAttributeString("Value", XmlConvert.ToString(Classifier.getGICNormal()));
writer.WriteEndElement();
writer.WriteStartElement("Estimate");
writer.WriteAttributeString("Type", "GICSumStraigh");
writer.WriteAttributeString("Value", XmlConvert.ToString(Classifier.getGICSumStraigth()));
writer.WriteEndElement();
writer.WriteStartElement("Estimate");
writer.WriteAttributeString("Type", "GICSumReverse");
writer.WriteAttributeString("Value", XmlConvert.ToString(Classifier.getGICSumReverce()));
writer.WriteEndElement();
writer.WriteStartElement("Estimate");
writer.WriteAttributeString("Type", "GISNormal");
writer.WriteAttributeString("Value", XmlConvert.ToString(Classifier.getGISNormal()));
writer.WriteEndElement();
writer.WriteStartElement("Estimate");
writer.WriteAttributeString("Type", "GISSumStraigh");
writer.WriteAttributeString("Value", XmlConvert.ToString(Classifier.getGISSumStraigt()));
writer.WriteEndElement();
writer.WriteStartElement("Estimate");
writer.WriteAttributeString("Type", "GISSumReverce");
writer.WriteAttributeString("Value", XmlConvert.ToString(Classifier.getGISSumReverce()));
writer.WriteEndElement();
writer.WriteStartElement("Estimate");
writer.WriteAttributeString("Type", "LindisNormal");
writer.WriteAttributeString("Value", XmlConvert.ToString(Classifier.getLindisNormal()));
writer.WriteEndElement();
writer.WriteStartElement("Estimate");
writer.WriteAttributeString("Type", "LindisSumStraigh");
writer.WriteAttributeString("Value", XmlConvert.ToString(Classifier.getLindisSumStraight()));
writer.WriteEndElement();
writer.WriteStartElement("Estimate");
writer.WriteAttributeString("Type", "LindisSumReverse");
writer.WriteAttributeString("Value", XmlConvert.ToString(Classifier.getLindisSumReverse()));
writer.WriteEndElement();
writer.WriteStartElement("Estimate");
writer.WriteAttributeString("Type", "NormalIndex");
writer.WriteAttributeString("Value", XmlConvert.ToString(Classifier.getNormalIndex()));
writer.WriteEndElement();
writer.WriteStartElement("Estimate");
writer.WriteAttributeString("Type", "RealIndex");
writer.WriteAttributeString("Value", XmlConvert.ToString(Classifier.getIndexReal()));
writer.WriteEndElement();
writer.WriteStartElement("Estimate");
writer.WriteAttributeString("Type", "SumStraigthIndex");
writer.WriteAttributeString("Value", XmlConvert.ToString(Classifier.getIndexSumStraigt()));
writer.WriteEndElement();
writer.WriteStartElement("Estimate");
writer.WriteAttributeString("Type", "SumReverseIndex");
writer.WriteAttributeString("Value", XmlConvert.ToString(Classifier.getIndexSumReverse()));
writer.WriteEndElement();
writer.WriteStartElement("Estimate");
writer.WriteAttributeString("Type", "ComplexitIt");
writer.WriteAttributeString("Value", XmlConvert.ToString(Classifier.getComplexit()));
writer.WriteEndElement();
writer.WriteStartElement("Estimate");
writer.WriteAttributeString("Type", "CountRules");
writer.WriteAttributeString("Value", XmlConvert.ToString(Classifier.getRulesCount()));
writer.WriteEndElement();
writer.WriteEndElement();
}
private void OriginalOperator(List <KnowlegeBasePCRules> Population, int cluster_index)
{
NewPopulation = new KnowlegeBasePCRules[groups[cluster_index].Length];
double epsi_newstep = rand.NextDouble() * Math.Exp(1 - (iter / (iter - cur_iter + 1)));
for (int i = 0; i < groups[cluster_index].Length; i++)
{
NewPopulation[i] = Population[groups[cluster_index][i]];
}
for (int i = 0; i < groups[cluster_index].Length; i++)
{
int number = groups[cluster_index][i];
for (int j = 0; j < NewPopulation[i].TermsSet.Count; j++)
{
for (int k = 0; k < NewPopulation[i].TermsSet[j].Parametrs.Length; k++)
{
NewPopulation[i].TermsSet[j].Parametrs[k] += epsi_newstep * rand.NextDouble();
}
}
double errornew = result.ErrorLearnSamples(NewPopulation[i]);
double errorold = result.ErrorLearnSamples(Population[number]);
if (errornew < errorold)
{
Population[number] = NewPopulation[i];
}
}
}
