public void buildAndTest()
{
mainThread = System.Threading.Thread.CurrentThread;
expert = new Expert("Эксперт Easy 1", this);
expert.AddAlgorithm((new Easy(this, "Easy[0]").Open(@"E:\Anton\Desktop\MAIN\Экспертная система\Экспертная система\Алгоритмы прогнозирования\Easy\h.json")));
// expert.Add(new CNN_1(this, "CNN_1[0]"));
// expert.algorithms[0].Open(@"E:\Anton\Desktop\MAIN\Эксперт 1\CNN_1[0]\h.json");
// expert.algorithms[0].Open(pathPrefix + @"Optimization\LSTM_1\LSTM_1[0]\h.json");
// expert.algorithms[1].Open(pathPrefix + @"Optimization\LSTM_2\LSTM_2[0]\h.json");
// expert.algorithms[2].Open(pathPrefix + @"Optimization\ANN_1\ANN_1[0]\h.json");
/* expert.H.setValueByName("start_point", "0.5");
* expert.H.setValueByName("normalize", "true");
* expert.H.add("input_file", pathPrefix + @"Временные ряды\USD_RUB_exmo-dataset.txt");
* expert.H.add("path_prefix", pathPrefix);
* expert.H.add("drop_columns:<local_time>");
* expert.H.add("predicted_column_index:1");
* expert.H.add("name:show_train_charts,value:True");*/
// expert.H.add("normalize:true");
// expert.H.add("input_file", pathPrefix + @"Временные ряды\85123A-dataset.txt");
// expert.H.add("path_prefix", pathPrefix);
//expert.H.add("name:show_train_charts,value:False");
// expert.H.add("predicted_column_index:0");
/* expert.copyExpertParametersToAlgorithms();
* expert.trainAllAlgorithms(false);
*
* expert.copyHyperparametersFromAlgorithmsToExpert();
* expert.H.draw(0, picBox, this, 20, 150);
* expert.Save();
*/
expert.copyHyperparametersFromAlgorithmsToExpert();
expert.H.add("drop_columns:<DATE>");
expert.H.setValueByName("normalize", "true");
expert.Save();
sourceDataFile = pathPrefix + @"Временные ряды\LD2011_2014-250 столбец-dataset+date.txt";
expert.test_prediction(new DateTime(2011, 10, 01), sourceDataFile);
}
public void iteration_of_optimization()
{
if (opt_inc == 1)
{
string new_save_folder;
expert.copyExpertParametersToAlgorithms();
for (int j = 0; j < expert.algorithms.Count; j++)
{
new_save_folder = form1.pathPrefix + "Optimization\\" + name + "\\" + expert.expertName + "[0]" + "\\" + expert.algorithms[j].getValueByName("model_name") + "\\";
Directory.CreateDirectory(new_save_folder);
expert.algorithms[j].h.setValueByName("save_folder", new_save_folder);
string predictionsFilePath = new_save_folder + "predictions.txt";
expert.algorithms[j].h.setValueByName("predictions_file_path", predictionsFilePath);
expert.algorithms[j].h.setValueByName("json_file_path", new_save_folder + "h.json");
File.WriteAllText(new_save_folder + "h.json", expert.algorithms[j].h.toJSON(0), System.Text.Encoding.Default);
expert.algorithms[j].train();
}
expert.copyHyperparametersFromAlgorithmsToExpert();
expert.H.setValueByName("report_path", form1.pathPrefix + "Optimization\\" + name + "\\" + expert.expertName + "[0]");
expert.H.setValueByName("code", "0");
expert.Save(form1.pathPrefix + "Optimization\\" + name + "\\" + expert.expertName + "[0]");
expert.test_trading(date1, date2, rawDatasetFilePath);
//отрисовка истрии баланса
for (int i = 0; i < expert.deposit1History.Count; i++)
{
variablesVisualizer.addPoint(expert.deposit2History[i] + (expert.deposit1History[i] * expert.closeValueHistory[i]), "exit [0]");
variablesVisualizer.addPoint(expert.deposit2History[i], "deposit2 [0]");
variablesVisualizer.addPoint(expert.closeValueHistory[i], "close [0]");
}
for (int i = 0; i < population_value; i++)
{
expert.H.setValueByName("code", i.ToString());
population[i] = Copy(expert.H, form1.pathPrefix + "Optimization\\" + name + "\\" + expert.expertName + "[0]" + "\\", form1.pathPrefix + "Optimization\\" + name + "\\" + expert.expertName + "[" + i.ToString() + "]" + "\\");
}
variablesIDs.Clear();
recurciveVariableAdding(population[0], 0, name + "[0]");
/* for (int i = 1; i < population_value; i++)
* {
* variablesIDs.Clear();
* recurciveVariableAdding(population[i], 0, name + "[" + i.ToString() + "]");
* } */
refreshEOTree();
variablesVisualizer.refresh();
}
else
{
//kill and concieve
kill_and_conceive();
//mutation
for (int i = 0; i < mutation_rate; i++)
{
mutation();
}
for (int i = 0; i < population_value; i++)
{
File.WriteAllText(form1.pathPrefix + "Optimization\\" + name + "\\" + name + "[" + i.ToString() + "]" + "\\h.json", population[i].toJSON(0), System.Text.Encoding.Default);
}
// ВВЕДЕНО МНОГОКРАТНОЕ ТЕСТИРОВНИЕ ИНДИВИДОВ ВСЕХ КАТЕГОРИЙ ДЛЯ ПОВЫШЕНИЯ ПОВТОРЯЕМОСТИ РЕЗУЛЬТАТОВ
// target_functions - матрица результатов тестирования,
// где номер строки (первый индекс (i)) - индекс индивида, а номер столбца (второй индекс (j)) - итерация тестирования
double[,] target_functions = new double[population_value, test_count];
for (int tc = 0; tc < test_count; tc++)
{
if (opt_inc == 2)
{
if (agentManager.agents.Count == 0)
{
if (multiThreadTraining)
{
if (form1.multiThreadTrainingRATE != 0)
{
multiThreadTrainingRATE = form1.multiThreadTrainingRATE;
}
log("multiThreadTrainingRATE = " + multiThreadTrainingRATE.ToString(), Color.Yellow);
if (multiThreadTrainingRATE > population_value)
{
multiThreadTrainingRATE = population_value;
}
for (int begin = 0; begin < population_value; begin += multiThreadTrainingRATE)
{
var now1 = new DateTimeOffset(DateTime.Now);
var start1 = now1.ToUnixTimeSeconds();
int end = 0;
if (begin + multiThreadTrainingRATE >= population_value)
{
end = population_value;
}
else
{
end = begin + multiThreadTrainingRATE;
}
List <Expert> experts = new List <Expert>();
for (int i = begin; i < end; i++)
{
Expert exp = Expert.Open(form1.pathPrefix + "Optimization\\" + name + "\\" + name + "[" + i.ToString() + "]", name, form1);
// exp.H = population[i].Clone();
experts.Add(exp);
}
Task[] trainTasks = new Task[end - begin];
foreach (Expert exp in experts)
{
trainTasks[experts.IndexOf(exp)] = new Task(() => experts[experts.IndexOf(exp)].trainAllAlgorithms(false));
trainTasks[experts.IndexOf(exp)].Start();
}
foreach (var task in trainTasks)
{
task.Wait();
}
for (int i = begin; i < end; i++)
{
// experts[i].copyHyperparametersFromAlgorithmsToExpert();
experts[i - begin].Save(form1.pathPrefix + "Optimization\\" + name + "\\" + name + "[" + i.ToString() + "]");
population[i] = experts[i - begin].H.Clone();
}
log((end).ToString() + '/' + population_value.ToString() + " training comlete" + TimeSpan.FromSeconds((new DateTimeOffset(DateTime.Now).ToUnixTimeSeconds()) - start1).ToString(), Color.LimeGreen);
}
}
else
{
for (int i = 0; i < population_value; i++)
{
expert = Expert.Open(form1.pathPrefix + "Optimization\\" + name + "\\" + name + "[" + i.ToString() + "]", name, form1);
expert.trainAllAlgorithms(false);
expert.Save(form1.pathPrefix + "Optimization\\" + name + "\\" + name + "[" + i.ToString() + "]");
population[i] = expert.H.Clone();
log((i + 1).ToString() + '/' + population_value.ToString() + " training comlete", Color.LimeGreen);
}
}
}
else
{
for (int i = 0; i < population_value; i++)
{
var algorithmBranches = population[i].getNodesByparentID(expert.committeeNodeID);
foreach (Node algorithmBranch in algorithmBranches)
{
agentManager.tasks.Add(new AgentTask("train", new Hyperparameters(population[i].toJSON(algorithmBranch.ID), form1)));
}
Task.Factory.StartNew(() => { agentManager.work(); }).Wait();
// СПИСОК ВЕТВЕЙ АЛГОРИТМОВ
List <Node> toDelete = population[i].getNodesByparentID(expert.committeeNodeID);
//удаление старых конфигураций алгоритмов
for (int j = 0; j < toDelete.Count; j++)
{
population[i].deleteBranch(toDelete[j].ID);
}
//приращение новых конфигураций к узлу "committee"
for (int j = 0; j < agentManager.tasks.Count; j++)
{
population[i].addBranch(agentManager.tasks[j].h, agentManager.tasks[j].h.nodes[0].name(), expert.committeeNodeID);
}
agentManager.tasks.Clear();
// expert.synchronizeHyperparameters();
File.WriteAllText(form1.pathPrefix + "Optimization\\" + name + "\\" + name + "[" + i.ToString() + "]" + "\\h.json", population[i].toJSON(0), System.Text.Encoding.Default);
}
}
}
for (int i = 0; i < population_value; i++)
{
variablesVisualizer.Clear("committee response [" + i.ToString() + "]");
variablesVisualizer.Clear("deposit2 [" + i.ToString() + "]");
variablesVisualizer.Clear("deposit1 [" + i.ToString() + "]");
variablesVisualizer.Clear("exit [" + i.ToString() + "]");
variablesVisualizer.Clear("close [" + i.ToString() + "]");
}
//Тестирование (вычисление целевой функции)
for (int i = 0; i < population_value; i++)
{
expert = Expert.Open(form1.pathPrefix + "Optimization\\" + name + "\\" + name + "[" + i.ToString() + "]", name, form1);
expert.test_trading(date1, date2, rawDatasetFilePath);
log("test[" + i.ToString() + "]: " + expert.H.getValueByName("expert_target_function"), Color.Purple);
population[i] = expert.H.Clone();
File.WriteAllText(form1.pathPrefix + "Optimization\\" + name + "\\" + name + "[" + i.ToString() + "]" + "\\h.json", population[i].toJSON(0), System.Text.Encoding.Default);
target_functions[i, tc] = Convert.ToDouble(population[i].getValueByName("expert_target_function").Replace('.', ','));
//отрисовка истрии баланса
for (int j = 0; j < expert.deposit1History.Count; j++)
{
variablesVisualizer.addPoint(expert.committeeResponseHistory[j][0], "committee response [" + i.ToString() + "]");
// variablesVisualizer.addPoint(expert.actionHistory[j], "actions [" + i.ToString() + "]");
variablesVisualizer.addPoint(expert.deposit2History[j] + (expert.deposit1History[j] * expert.closeValueHistory[j]), "exit [" + i.ToString() + "]");
variablesVisualizer.addPoint(expert.deposit1History[j], "deposit1 [" + i.ToString() + "]");
variablesVisualizer.addPoint(expert.closeValueHistory[j], "close [" + i.ToString() + "]");
}
variablesVisualizer.refresh();
}
log((tc + 1).ToString() + '/' + test_count.ToString() + " test comlete", Color.LimeGreen);
}
variablesVisualizer.refresh();
//вычисление итоговых значений критерия оптимальности
for (int i = 0; i < population_value; i++)
{
double sum = 0;
// AVG
for (int j = 0; j < test_count; j++)
{
sum += target_functions[i, j];
}
double AVG = sum / test_count;
sum = 0;
// StdDev
for (int j = 0; j < test_count; j++)
{
sum += (AVG - target_functions[i, j]) * (AVG - target_functions[i, j]);
}
double StdDev = Math.Sqrt(sum / test_count);
// если target_function равна (AVG - StdDev), то последующее вычисление критерия оптимальности будет давать результаты ВЫШЕ, чем target_function
population[i].setValueByName("expert_target_function", (AVG - StdDev).ToString().Replace(',', '.'));
population[i].setValueByName("expert_target_function_AVG", (AVG).ToString().Replace(',', '.'));
population[i].setValueByName("expert_target_function_StdDev", (StdDev).ToString().Replace(',', '.'));
// File.WriteAllText(population[i].getValueByName("json_file_path"), population[i].toJSON(0), System.Text.Encoding.Default);
}
// сортировка
Hyperparameters temp;
for (int i = 0; i < population_value - 1; i++)
{
for (int j = i + 1; j < population_value; j++)
{
double i_value = Convert.ToDouble(population[i].getValueByName("expert_target_function").Replace('.', ','));
double j_value = Convert.ToDouble(population[j].getValueByName("expert_target_function").Replace('.', ','));
if (i_value < j_value || (double.IsNaN(i_value) && (!double.IsNaN(j_value))))
{
log(" [" + i.ToString() + "] <- [" + j.ToString() + "]: " + i_value + "<" + j_value, Color.Orchid);
string tempFolder = form1.pathPrefix + "Optimization\\" + name + "\\temp";
string path_to_i = form1.pathPrefix + "Optimization\\" + name + "\\" + name + "[" + i.ToString() + "]";
string path_to_j = form1.pathPrefix + "Optimization\\" + name + "\\" + name + "[" + j.ToString() + "]";
temp = Copy(population[i], path_to_i, tempFolder);
population[i] = Copy(population[j], path_to_j, path_to_i);
population[j] = Copy(temp, tempFolder, path_to_j);
population[i].setValueByName("report_path", form1.pathPrefix + "Optimization\\" + name + "\\" + expert.expertName + "[" + i.ToString() + "]");
population[j].setValueByName("report_path", form1.pathPrefix + "Optimization\\" + name + "\\" + expert.expertName + "[" + j.ToString() + "]");
}
}
}
}
for (int i = 0; i < population_value; i++)
{
population[i].setValueByName("name", population[i].nodes[0].name() + "[" + i.ToString() + "]");
}
log("Обновление отображаемых параметров", Color.Lime);
refreshEOTree();
// A.draw(0, form1.picBox, form1, 15, 150);
variableChangeMonitoring();
variablesVisualizer.addPoint(Convert.ToDouble(population[0].getValueByName("expert_target_function").Replace('.', ',')), "best_individ");
double averegeBest = 0;
for (int i = 0; i < population_value; i++)
{
var t_f = Convert.ToDouble(population[i].getValueByName("expert_target_function").Replace('.', ','));
if (i < population_value * elite_ratio)
{
averegeBest += t_f;
}
variablesVisualizer.addPoint(t_f, " [" + population[i].getValueByName("code") + "]");
}
variablesVisualizer.addPoint((averegeBest / (population_value * elite_ratio)).ToString(), "averege best");
variablesVisualizer.refresh();
}