private string ErrorInfoSA(IFuzzySystem FS)
{
SAFuzzySystem IFS = FS as SAFuzzySystem;
if (IFS.RulesDatabaseSet.Count < 1)
{
return("Точность нечеткой системы недоступна");
}
approxLearnResult.Add(IFS.approxLearnSamples(IFS.RulesDatabaseSet[0]));
approxTestResult.Add(IFS.approxTestSamples(IFS.RulesDatabaseSet[0]));
approxLearnResultMSE.Add(IFS.RMSEtoMSEforLearn(approxLearnResult[approxLearnResult.Count - 1]));
approxTestResultMSE.Add(IFS.RMSEtoMSEforTest(approxTestResult[approxTestResult.Count - 1]));
approxLearnResultMSEdiv2.Add(IFS.RMSEtoMSEdiv2forLearn(approxLearnResult[approxLearnResult.Count - 1]));
approxTestResultMSEdiv2.Add(IFS.RMSEtoMSEdiv2forTest(approxTestResult[approxTestResult.Count - 1]));
return("Точностью на обучающей выборке(RSME) " + approxLearnResult [approxLearnResult.Count - 1].ToString() + " , Точность на тестовой выборке(RMSE) " + approxTestResult[approxTestResult.Count - 1].ToString() + " " + Environment.NewLine +
"Точностью на обучающей выборке(MSE) " + approxLearnResultMSE[approxLearnResultMSE.Count - 1].ToString() + " , Точность на тестовой выборке(MSE) " + approxTestResultMSE[approxTestResultMSE.Count - 1].ToString() + " " + Environment.NewLine +
"Точностью на обучающей выборке(MSE/2) " + approxLearnResultMSEdiv2[approxLearnResultMSEdiv2.Count - 1].ToString() + " , Точность на тестовой выборке(MSE/2) " + approxTestResultMSEdiv2[approxTestResultMSEdiv2.Count - 1].ToString() + " " + Environment.NewLine);
}
protected override void make_Log(Log_line EventCall, SAFuzzySystem FS = null, string name_Alg = "", DateTime TimerValue = new DateTime(), TimeSpan TimerSpan = new TimeSpan())
{
switch (EventCall)
{
case Log_line.Start:
{
LOG += "(" + TimerValue.ToString() + ")" + " Начало построения системы" + Environment.NewLine;
break;
}
case Log_line.StartGenerate:
{
LOG += "(" + TimerValue.ToString() + ")" + " Начата генерация системы" + Environment.NewLine;
break;
}
case Log_line.StartOptimaze:
{
LOG += "(" + DateTime.Now.ToString() + ")" + " Начата оптимизация системы" + Environment.NewLine;
break;
}
case Log_line.PreGenerate_log:
{
LOG += "(" + DateTime.Now.ToString() + ")" + " Генерация алгоритмом " + name_Alg.ToString() + Environment.NewLine;
break;
}
case Log_line.PostGenerate_log:
{
double LearnResult = FS.approxLearnSamples();
double TestResult = FS.approxTestSamples();
double LearnResultMSE = FS.RMSEtoMSEforLearn(LearnResult);
double TestResultMSE = FS.RMSEtoMSEforTest(TestResult);
double LearnResultMSEdiv2 = FS.RMSEtoMSEdiv2forLearn(LearnResult);
double TestResultMSEdiv2 = FS.RMSEtoMSEdiv2forTest(TestResult);
LOG += "(" + DateTime.Now.ToString() + ")" + " Сгенерирована система сложностью " + FS.ValueComplexity().ToString() + Environment.NewLine +
"Точностью на обучающей выборке(RSME) " + LearnResult.ToString() + ", Точность на тестовой выборке(RMSE) " + TestResult.ToString() + Environment.NewLine +
"Точностью на обучающей выборке(MSE) " + LearnResultMSE.ToString() + ", Точность на тестовой выборке(MSE) " + TestResultMSE.ToString() + Environment.NewLine +
"Точностью на обучающей выборке(MSE/2) " + LearnResultMSEdiv2.ToString() + ", Точность на тестовой выборке(MSE/2) " + TestResultMSEdiv2.ToString() + Environment.NewLine;
LOG += "Использован " + name_Alg.ToString() + Environment.NewLine;
break;
}
case Log_line.PreOptimaze_log:
{
LOG += "(" + DateTime.Now.ToString() + ")" + " Оптимизация алгоритмом " + name_Alg.ToString() + Environment.NewLine;
break;
}
case Log_line.PostOptimaze_log:
{
double LearnResult = FS.approxLearnSamples();
double TestResult = FS.approxTestSamples();
double LearnResultMSE = FS.RMSEtoMSEforLearn(LearnResult);
double TestResultMSE = FS.RMSEtoMSEforTest(TestResult);
double LearnResultMSEdiv2 = FS.RMSEtoMSEdiv2forLearn(LearnResult);
double TestResultMSEdiv2 = FS.RMSEtoMSEdiv2forTest(TestResult);
LOG += "(" + DateTime.Now.ToString() + ")" + " оптимизированная система сложностью " + FS.ValueComplexity().ToString() + Environment.NewLine +
"Точностью на обучающей выборке(RMSE) " + LearnResult.ToString() + ", Точность на тестовой выборке(RMSE) " + TestResult.ToString() + Environment.NewLine +
"Точностью на обучающей выборке(MSE) " + LearnResultMSE.ToString() + ", Точность на тестовой выборке(MSE) " + TestResultMSE.ToString() + Environment.NewLine +
"Точностью на обучающей выборке(MSE/2) " + LearnResultMSEdiv2.ToString() + ", Точность на тестовой выборке(MSE/2) " + TestResultMSEdiv2.ToString() + Environment.NewLine;
LOG += "Использован " + name_Alg.ToString() + Environment.NewLine;
break;
}
case Log_line.EndCircle:
{
LOG += "(" + DateTime.Now.ToString() + ")" + " Время построения системы" + TimerSpan.TotalSeconds.ToString() + Environment.NewLine; break;
}
case Log_line.End:
{
LOG += "(" + DateTime.Now.ToString() + ")" + " Время построения всех систем" + TimerSpan.TotalSeconds.ToString() + Environment.NewLine; break;
}
default: { LOG += "Не верный вызов" + Environment.NewLine; break; }
}
}
public override SAFuzzySystem TuneUpFuzzySystem(SAFuzzySystem Approximate, ILearnAlgorithmConf conf) // + override
{
result = Approximate;
List <KnowlegeBaseSARules> Archive = new List <KnowlegeBaseSARules>();
List <double> ErrorsArchive = new List <double>();
var config = (DynamicTuneConf)conf;
maxError = config.MaxError;
RuleCount = config.RulesCount;
TryCount = config.TryCount;
double error = result.RMSEtoMSEdiv2forLearn(result.approxLearnSamples(result.RulesDatabaseSet[0]));
var kbToOptimize = new KnowlegeBaseSARules(result.RulesDatabaseSet[0]);
var kbBest = new KnowlegeBaseSARules(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);
}
///!!!!
double temp_approx_Values = kbToOptimize.RulesDatabase[j].IndependentConstantConsequent;
/* double[] temp_approx_RegressionConstantConsequent =
* kbToOptimize.RulesDatabase[j].RegressionConstantConsequent.Clone() as double[];
*/
SARule temp_rule = new SARule(
kbToOptimize.TermsSet, order, temp_approx_Values);
// double[] dC = null;
//!!!
temp_rule.IndependentConstantConsequent = KNNConsequent.NearestApprox(result, temp_rule.ListTermsInRule.ToList());
kbToOptimize.RulesDatabase.Add(temp_rule);
//!!!
rulesLeft[j].IndependentConstantConsequent = KNNConsequent.NearestApprox(result, rulesLeft[j].ListTermsInRule.ToList());
// rulesLeft[j].RegressionConstantConsequent = (double[])dC.Clone();
}
foreach (var rule in rulesRight)
{
//!!!
rule.IndependentConstantConsequent = KNNConsequent.NearestApprox(
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.RMSEtoMSEdiv2forLearn(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 KnowlegeBaseSARules(kbToOptimize);
errorBefore = error;
tryCount = 0;
}
else
{
tryCount++;
}
if (tryCount > TryCount)
{
break;
}
}
result.RulesDatabaseSet[0] = kbBest;
RuleCount = kbBest.RulesDatabase.Count;
TryCount = tryCount;
return(result);
}
