public override TSAFuzzySystem TuneUpFuzzySystem(TSAFuzzySystem Approx, ILearnAlgorithmConf conf)
{
result = Approx;
groups = new List <int[]>();
Init(conf);
SetPopulation();
Population = ListTakagiSugenoApproximateTool.SortRules(Population, result);
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 = ListTakagiSugenoApproximateTool.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 = ListTakagiSugenoApproximateTool.SortRules(Population, result);
result.RulesDatabaseSet[0] = Population[0];
return(result);
}
public override TSAFuzzySystem TuneUpFuzzySystem(TSAFuzzySystem Classifier, ILearnAlgorithmConf conf)
{
Init(conf);
KnowlegeBaseTSARules temp_c_Rule = new KnowlegeBaseTSARules(Classifier.RulesDatabaseSet[0]);
TSAFuzzySystem 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 KnowlegeBaseTSARules(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);
}
}
}
double unlaidtest = result.ErrorLearnSamples(monkey[t]);
//result.UnlaidProtectionFix(monkey[t]);
//Console.WriteLine("Unlaid: " + result.ErrorLearnSamples(monkey[0]).ToString());
if (double.IsNaN(unlaidtest) || double.IsInfinity(unlaidtest))
{
result.UnlaidProtectionFix(monkey[t]);
}
// delete
testvals[t] = result.ErrorLearnSamples(monkey[t]);
Console.WriteLine("Begin: " + t.ToString() + " " + iter.ToString() + " " + testvals[t].ToString());
}
bestsolution = new KnowlegeBaseTSARules(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) && (last == true))
{
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);
}
