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 override TSAFuzzySystem Generate(TSAFuzzySystem Approximate, IGeneratorConf config)
{
start_add_rules = Approximate.RulesDatabaseSet.Count;
TSAFuzzySystem result = Approximate;
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 < Approximate.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++)
{
KnowlegeBaseTSARules temp_rules = new KnowlegeBaseTSARules(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.approxLearnSamples(result.RulesDatabaseSet [start_add_rules + i]));
}
int best_index = errors_of_systems.IndexOf(errors_of_systems.Min());
KnowlegeBaseTSARules 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 void Set_count_add_generator(int count_used_Algorithm)
{
Rules_generator_conf = new IGeneratorConf[count_used_Algorithm];
Rules_generator_type = new int[count_used_Algorithm];
for (int i = 0; i < count_used_Algorithm; i++)
{
Rules_generator_type[i] = -2;
}
}
private universal_conf_form makeConfForm(IGeneratorConf SourceObject, string name)
{
universal_conf_form temp_conf_form = new universal_conf_form();
temp_conf_form.Text = "Параметры генерации " + name;
temp_conf_form.conf_algorithm_params_PG.SelectedObject = SourceObject;
return(temp_conf_form);
}
public override SAFuzzySystem Generate(SAFuzzySystem Approximate, IGeneratorConf config)
{
SAFuzzySystem result = Approximate;
InitEveryoneWithEveryone config1 = config as InitEveryoneWithEveryone;
type_func = config1.IEWEFuncType;
count_slice_vars = config1.IEWECountSlice;
InitRulesEveryoneWithEveryone(Approximate, type_func, count_slice_vars);
return(result);
}
public override TSAFuzzySystem Generate(TSAFuzzySystem Approximate, IGeneratorConf config)
{
type_alg = ((kMeanRulesGeneratorConfig)config).KMRGTypeAlg;
count_rules = ((kMeanRulesGeneratorConfig)config).KMRGCountRules;
type_func = ((kMeanRulesGeneratorConfig)config).KMRGTypeFunc;
nebulisation_factor = ((kMeanRulesGeneratorConfig)config).KMRGExponentialWeight;
Max_iteration = ((kMeanRulesGeneratorConfig)config).KMRGIteraton;
need_precision = ((kMeanRulesGeneratorConfig)config).KMRGAccuracy;
Approxk_mean_base K_Agl = null;
switch (type_alg)
{
case Type_k_mean_algorithm.GathGeva: K_Agl = new Approxk_mean_Gath_Geva(Approximate.LearnSamplesSet, Max_iteration, need_precision, count_rules, nebulisation_factor); break;
case Type_k_mean_algorithm.GustafsonKessel: K_Agl = new Approxk_mean_Gustafson_kessel(Approximate.LearnSamplesSet, Max_iteration, need_precision, count_rules, nebulisation_factor); break;
case Type_k_mean_algorithm.FCM: K_Agl = new Approxk_mean_base(Approximate.LearnSamplesSet, Max_iteration, need_precision, count_rules, nebulisation_factor); break;
}
K_Agl.Calc();
KnowlegeBaseTSARules New_Rules = new KnowlegeBaseTSARules();
for (int i = 0; i < count_rules; i++)
{
int [] order_terms = new int [Approximate.LearnSamplesSet.CountVars];
List <Term> term_set = new List <Term>();
for (int j = 0; j < Approximate.LearnSamplesSet.CountVars; j++)
{
Term temp_term = Term.MakeTerm(K_Agl.Centroid_cordinate_S[i][j], Math.Sqrt(Calc_distance_for_member_ship_function_for_Clust(i, j, K_Agl)) * 3, type_func, j);
term_set.Add(temp_term);
}
New_Rules.ConstructNewRule(term_set, Approximate);
}
TSAFuzzySystem Result = Approximate;
if (Result.RulesDatabaseSet.Count > 0)
{
Result.RulesDatabaseSet[0] = New_Rules;
}
else
{
Result.RulesDatabaseSet.Add(New_Rules);
}
Result.UnlaidProtectionFix(Result.RulesDatabaseSet[0]);
GC.Collect();
Result.RulesDatabaseSet[0].TermsSet.Trim();
return(Result);
}
public IFuzzySystem Universal(IFuzzySystem FS, IGeneratorConf config)
{
ShrinkFeatures = ((SimpleShrinkFeatureConf)config).ShrinkFeature;
string[] shouldShrink = ShrinkFeatures.Split(new char[] { ',', ' ' }, StringSplitOptions.RemoveEmptyEntries);
for (int i = 0; i < shouldShrink.Length; i++)
{
int a;
int.TryParse(shouldShrink[i], out a);
FS.AcceptedFeatures[a - 1] = false;
}
return(FS);
}
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);
}
public override SAFuzzySystem Generate(SAFuzzySystem Approximate, IGeneratorConf config)
{
SAFuzzySystem result = Approximate;
Systems_ready_to_test = new List <KnowlegeBaseSARules>();
errors_of_systems = new List <double>();
InitEveryoneWithOptimal config1 = config as InitEveryoneWithOptimal;
type_func = config1.IEWOTypeFunc;
count_slice_vars = config1.count_terms;
List <int> Varians_of_run_system = new List <int>();
for (int i = 0; i < Approximate.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++)
{
Approximate.RulesDatabaseSet.Clear();
GeneratorRulesEveryoneWithEveryone.InitRulesEveryoneWithEveryone(Approximate, type_func, Pull_of_systems[i].ToArray());
Systems_ready_to_test.Add(Approximate.RulesDatabaseSet[0]);
errors_of_systems.Add(result.approxLearnSamples(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());
result.RulesDatabaseSet[0].TermsSet.Trim();
GC.Collect();
return(result);
}
public override PCFuzzySystem Generate(PCFuzzySystem Classifier, IGeneratorConf config)
{
PCFuzzySystem result = Classifier;
InitEveryoneWithEveryone config1 = config as InitEveryoneWithEveryone;
type_func = config1.IEWEFuncType;
count_slice_vars = config1.IEWECountSlice;
InitRulesEveryoneWithEveryone(result, type_func, count_slice_vars);
InitRulesEveryoneWithEveryone(result, type_func, count_slice_vars);
result.RulesDatabaseSet[0].TermsSet.Trim();
return(result);
}
public override TSAFuzzySystem Generate(TSAFuzzySystem Approximate, IGeneratorConf config)
{
ResultSystem = Approximate;
type_func = TypeTermFuncEnum.Гауссоида;
var kliConf = config as KLI_conf;
if (kliConf != null)
{
{
ResultSystem = Approximate;
double meanValue = ResultSystem.LearnSamplesSet.DataRows.Select(x => x.DoubleOutput).Average();
var mayError = kliConf.MaxValue * meanValue;
kliGenerate(ResultSystem, type_func, mayError);
// File.AppendAllLines("./out.txt", new List<string>() { mayError + " " + Result.RulesDatabaseSet[0].ValueComplexity + " " + Result.RMSEtoMSEforLearn(Result.ErrorLearnSamples()) / 2 + " " + Result.RMSEtoMSEforTest(Result.ErrorTestSamples()) / 2 });
}
}
return(ResultSystem);
}
public override PCFuzzySystem Generate(PCFuzzySystem Classifier, IGeneratorConf config)
{
PCFuzzySystem result = Classifier;
//Filtre(result);
InitBySamplesConfig config1 = config as InitBySamplesConfig;
type_func = config1.IBSTypeFunc;
calc_min_max_for_class(result.LearnSamplesSet);
InitRulesBySamples(Classifier, type_func);
result.RulesDatabaseSet[0].TermsSet.Trim();
//Chiu(result);
//Console.WriteLine("Обуч: " + Math.Round(result.ClassifyLearnSamples(result.RulesDatabaseSet[0]), 2));
//Console.WriteLine("Тест: " + Math.Round(result.ClassifyTestSamples(result.RulesDatabaseSet[0]), 2));
//Console.WriteLine();
return(result);
}
public override SAFuzzySystem Generate(FuzzySystem.SingletoneApproximate.SAFuzzySystem Approximate, IGeneratorConf config)
{
Random rand = new Random();
SAFuzzySystem result = Approximate;
if (result.RulesDatabaseSet.Count == 0)
{
KnowlegeBaseSARules temp_rules = new KnowlegeBaseSARules();
result.RulesDatabaseSet.Add(temp_rules);
}
type_term = ((GeneratorRullesSimpleRandomConfig)config).RSRTypeFunc;
stable_terms = (int)((GeneratorRullesSimpleRandomConfig)config).RSRConstant;
count_rules = ((GeneratorRullesSimpleRandomConfig)config).RSRCountRules;
for (int j = 0; j < count_rules; j++)
{
int[] order = new int[result.CountFeatures];
TypeTermFuncEnum temp_type_term;
if (stable_terms == 0)
{
temp_type_term = type_term;
}
else
{
temp_type_term = Generator_type_term();
}
List <Term> temp_term_list = new List <Term>();
for (int k = 0; k < result.CountFeatures; k++)
{
double[] parametrs = new double[Term.CountParamsinSelectedTermType(temp_type_term)];
switch (temp_type_term)
{
case TypeTermFuncEnum.Треугольник:
parametrs[0] = result.LearnSamplesSet.InputAttributes[k].Min + rand.NextDouble() * (result.LearnSamplesSet.InputAttributes[k].Max - result.LearnSamplesSet.InputAttributes[k].Min);
parametrs[1] = result.LearnSamplesSet.InputAttributes[k].Min + rand.NextDouble() * (result.LearnSamplesSet.InputAttributes[k].Max - result.LearnSamplesSet.InputAttributes[k].Min);
parametrs[2] = result.LearnSamplesSet.InputAttributes[k].Min + rand.NextDouble() * (result.LearnSamplesSet.InputAttributes[k].Max - result.LearnSamplesSet.InputAttributes[k].Min);
Array.Sort(parametrs);
break;
case TypeTermFuncEnum.Гауссоида: parametrs[0] = result.LearnSamplesSet.InputAttributes[k].Min + rand.NextDouble() * (result.LearnSamplesSet.InputAttributes[k].Max - result.LearnSamplesSet.InputAttributes[k].Min);
parametrs[1] = (rand.NextDouble() + 0.01) * 0.5 *
(result.LearnSamplesSet.InputAttributes[k].Max -
result.LearnSamplesSet.InputAttributes[k].Min);
break;
case TypeTermFuncEnum.Парабола: parametrs[0] = result.LearnSamplesSet.InputAttributes[k].Min + rand.NextDouble() * (result.LearnSamplesSet.InputAttributes[k].Max - result.LearnSamplesSet.InputAttributes[k].Min);
parametrs[1] = result.LearnSamplesSet.InputAttributes[k].Min + rand.NextDouble() * (result.LearnSamplesSet.InputAttributes[k].Max - result.LearnSamplesSet.InputAttributes[k].Min);
Array.Sort(parametrs);
break;
case TypeTermFuncEnum.Трапеция: parametrs[0] = result.LearnSamplesSet.InputAttributes[k].Min + rand.NextDouble() * (result.LearnSamplesSet.InputAttributes[k].Max - result.LearnSamplesSet.InputAttributes[k].Min);
parametrs[1] = result.LearnSamplesSet.InputAttributes[k].Min + rand.NextDouble() * (result.LearnSamplesSet.InputAttributes[k].Max - result.LearnSamplesSet.InputAttributes[k].Min);
parametrs[2] = result.LearnSamplesSet.InputAttributes[k].Min + rand.NextDouble() * (result.LearnSamplesSet.InputAttributes[k].Max - result.LearnSamplesSet.InputAttributes[k].Min);
parametrs[3] = result.LearnSamplesSet.InputAttributes[k].Min + rand.NextDouble() * (result.LearnSamplesSet.InputAttributes[k].Max - result.LearnSamplesSet.InputAttributes[k].Min);
Array.Sort(parametrs);
break;
}
Term temp_term = new Term(parametrs, temp_type_term, k);
result.RulesDatabaseSet[0].TermsSet.Add(temp_term);
temp_term_list.Add(temp_term);
order[k] = result.RulesDatabaseSet[0].TermsSet.Count - 1;
}
double DoubleOutput = KNNConsequent.NearestApprox(result, temp_term_list);
SARule temp_Rule = new SARule(result.RulesDatabaseSet[0].TermsSet, order, DoubleOutput);
result.RulesDatabaseSet[0].RulesDatabase.Add(temp_Rule);
}
result.RulesDatabaseSet[0].TermsSet.Trim();
result.UnlaidProtectionFix(result.RulesDatabaseSet[0]);
GC.Collect();
return(result);
}
public IFuzzySystem Generate(IFuzzySystem Approximate, IGeneratorConf config)
{
SAFuzzySystem toRunFuzzySystem = Approximate as SAFuzzySystem;
return(Generate(toRunFuzzySystem, config));
}
public IFuzzySystem Generate(IFuzzySystem Classifier, IGeneratorConf config)
{
PCFuzzySystem toRunFuzzySystem = Classifier as PCFuzzySystem;
return(Generate(toRunFuzzySystem, config));
}
public override TSAFuzzySystem Generate(TSAFuzzySystem Approximate, IGeneratorConf config)
{
TSAFuzzySystem result = Approximate;
Pull_of_systems = Pull_of_systems = new List <List <int> >();
Systems_ready_to_test = new List <KnowlegeBaseTSARules>();
errors_of_systems = new List <double>();
DynamicTuneConfGenerator config1 = config as DynamicTuneConfGenerator;
type_func = config1.IEWOTypeFunc;
List <int> Varians_of_run_system = new List <int>();
List <int> allOne = new List <int>();
for (int i = 0; i < Approximate.CountFeatures; i++)
{
allOne.Add(1);
}
Pull_of_systems.Add(new List <int>(allOne));
for (int j = 0; j < Approximate.CountFeatures - 1; j++)
{
Varians_of_run_system.Clear();
allOne[j] = 2;
Varians_of_run_system.AddRange(new List <int> (allOne));
Varians_of_run_system.Sort();
Generate_all_variant_in_pool(Varians_of_run_system);
}
allOne[Approximate.CountFeatures - 1] = 2;
Pull_of_systems.Add(new List <int> (allOne));
for (int i = 0; i < Pull_of_systems.Count; i++)
{
Approximate.RulesDatabaseSet.Clear();
GeneratorRulesEveryoneWithEveryone.InitRulesEveryoneWithEveryone(Approximate, type_func, Pull_of_systems[i].ToArray());
Systems_ready_to_test.Add(Approximate.RulesDatabaseSet[0]);
errors_of_systems.Add(result.approxLearnSamples(result.RulesDatabaseSet[0]));
}
DynamicTuneClass dt = new DynamicTuneClass();
Approximate = dt.TuneUpFuzzySystem(Approximate, config1);
Systems_ready_to_test.Add(Approximate.RulesDatabaseSet[0]);
errors_of_systems.Add(result.approxLearnSamples(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]);
count_slice_vars = new int[Approximate.CountFeatures];
for (int i = 0; i < count_slice_vars.Count(); i++)
{
count_slice_vars[i] = result.RulesDatabaseSet[0].TermsSet.Count(x => x.NumVar == i);
}
maxError = config1.MaxError;
TryCount = config1.TryCount;
RuleCount = result.RulesDatabaseSet[0].RulesDatabase.Count;
Console.WriteLine(Pull_of_systems.Count());
result.RulesDatabaseSet[0].TermsSet.Trim();
GC.Collect();
return(result);
}
public override PCFuzzySystem Generate(PCFuzzySystem Classifier, IGeneratorConf config)
{
Classifier = Universal(Classifier, config) as PCFuzzySystem;
Classifier.RulesDatabaseSet.AsParallel().ForAll(x => x.ShrinkNotAcceptedFeatures(Classifier.AcceptedFeatures));
return(Classifier);
}
public override TSAFuzzySystem Generate(TSAFuzzySystem Approximate, IGeneratorConf config)
{
Approximate = Universal(Approximate, config) as TSAFuzzySystem;
Approximate.RulesDatabaseSet.AsParallel().ForAll(x => x.ShrinkNotAcceptedFeatures(Approximate.AcceptedFeatures));
return(Approximate);
}
