internal OptimizationView(AlgorithmOptimization AO)
{
InitializeComponent();
Text = "Оптимизация " + AO.algorithm.h.nodes[1].getValue();
this.AO = AO;
}
public void algorithmOptimization()
{
expert = new Expert("Эксперт 1", this);
mainThread = System.Threading.Thread.CurrentThread;
sourceDataFile = pathPrefix + @"Временные ряды\BTC_USD_exmo.txt";
var dataset_file = expert.savePreparedDataset(sourceDataFile, "<symbol_time>;<server_time>;<TIME>;<DATE>;<local_time>;<TICKER>;<PER>;<DATEandTIME>;<DATE_TIME>;\"\"", true);
algorithm = new Easy(this, "Easy");
// algorithm.h.addVariable(0, "learning_rate", 0.00001, 0.002, 0.003, 0.0017);
algorithm.h.addVariable(0, "window_size", 10, 300, 3, 50);
algorithm.h.addVariable(0, "number_of_epochs", 1, 80, 5, 47);
algorithm.h.setValueByName("learning_rate", "0.0017");
// algorithm.h.setValueByName( "window_size","14");
// algorithm.h.setValueByName("number_of_epochs", "5");
algorithm.h.setValueByName("split_point", "0.9");
algorithm.h.setValueByName("steps_forward", "10");
algorithm.h.setValueByName("start_point", "0");
algorithm.h.setValueByName("normalize", "true");
//algorithm.h.add("input_file", dataset_file);
algorithm.h.add("input_file", pathPrefix + @"Временные ряды\BTC_USD_exmo-dataset.txt");
algorithm.h.add("path_prefix", pathPrefix);
algorithm.h.add("predicted_column_index:0");
algorithm.h.setValueByName("show_train_charts", "True");
// MyAlgorithmOptimization MAO = new MyAlgorithmOptimization(this, algorithm);
// MAO.run();
// algorithm.train();
/////////////////////////////////////////////////////////////
// АНСАМБЛИРОВАНИЕ
/* Ensemble ensemble = new Ensemble(this);
* //ensemble.algorithms.Add((new Easy(this, "Easy[0]").Open(@"E:\Anton\Desktop\MAIN\Экспертная система\Экспертная система\Алгоритмы прогнозирования\Easy\h.json")));
* ensemble.algorithms.Add((new Easy(this, "Easy[0]").Open(@"E:\Anton\Desktop\MAIN\Optimization\Easy\Easy[0]\h.json")));
* // ensemble.algorithms.Add((new Easy(this, "Easy[1]").Open(@"E:\Anton\Desktop\MAIN\Optimization\Easy\Easy[1]\h.json")));
* // ensemble.algorithms.Add((new Easy(this, "Easy[3]").Open(@"E:\Anton\Desktop\MAIN\Optimization\Easy\Easy[3]\h.json")));
* // ensemble.algorithms.Add((new Easy(this, "Easy[2]").Open(@"E:\Anton\Desktop\MAIN\Optimization\Easy\Easy[2]\h.json")));
* // ensemble.algorithms.Add((new Easy(this, "Easy[5]").Open(@"E:\Anton\Desktop\MAIN\Optimization\Easy\Easy[5]\h.json")));
*
* // ensemble.cyclicPrediction(true);
* ensemble.stepByStepPrediction();*/
/////////////////////////////////////////////////////////////
///
///
// I.executePythonScript(pathPrefix + @"\Экспертная система\Экспертная система\Алгоритмы прогнозирования\Easy\cyclic_prediction.py", "--json_file_path \"" + pathPrefix + @"\Optimization\Easy\Easy[0]\h.json" + '\"');
// I.executePythonScript(pathPrefix + @"\Экспертная система\Экспертная система\Алгоритмы прогнозирования\Easy\cyclic_prediction.py", "--json_file_path \"" + pathPrefix + @"\Экспертная система\Экспертная система\Алгоритмы прогнозирования\Easy\h.json" + '\"');
// algorithm.h.setValueByName("show_train_charts", "True");
// algorithm.getAccAndStdDev(File.ReadAllLines(@"E:\Anton\Desktop\MAIN\Optimization\Easy\Easy[0]\predictions.txt"));
// algorithm.getAccAndStdDev(File.ReadAllLines(pathPrefix + @"\Optimization\Easy\Easy[0]\cyclic_prediction.txt"));
// vis.enableGrid = true;
// vis.addCSV(@"E:\Anton\Desktop\MAIN\Экспертная система\Экспертная система\Алгоритмы прогнозирования\Easy\cyclic_prediction.txt", "\"MT_250\"", "\"MT_250\"", "r", 1000, 0, 0);
// vis.addCSV(@"E:\Anton\Desktop\MAIN\Экспертная система\Экспертная система\Алгоритмы прогнозирования\Easy\cyclic_prediction.txt", "\"MT_250\"", "(predicted -> )\"MT_250\"", "pred", 1000, 0, 0);
// vis.addCSV(@"E:\Anton\Desktop\MAIN\Экспертная система\Экспертная система\Алгоритмы прогнозирования\Easy\predictions.txt", "realVSpredictions", "MT_250", "real", 1000, 0.95, 0);
// vis.addCSV(@"E:\Anton\Desktop\MAIN\Экспертная система\Экспертная система\Алгоритмы прогнозирования\Easy\predictions.txt", "realVSpredictions", "LAST_COLUMN", "predictions", 1000, 0.95, 0);
AO = new AlgorithmOptimization(algorithm, this,
population_value: 16,
mutation_rate: 16,
architecture_variation_rate: 0,
elite_ratio: 0.25,
iterarions: 500,
test_count: 1,
AlgorithmOptimization.TargetFunctionType.STDDEV);
AO.run();
// algorithm.h.draw(0, picBox, 25, 300);
// algorithm.Save();
/* algorithm = new BidAsk(this, "BidAsk");
* algorithm.getAccAndStdDev(File.ReadAllLines(@"E:\Anton\Desktop\MAIN\Экспертная система\Экспертная система\Алгоритмы прогнозирования\BidAsk\predictions.txt"));
* // algorithm.Open(@"E:\Anton\Desktop\MAIN\Optimization\BidAsk\BidAsk[0]\h.json");
*
* //<DATE_TIME>;<bid_top>;<bid_quantity>;<bid_amount>;<ask_top>;<ask_quantity>;<ask_amount>
*
* /* sourceDataFile = pathPrefix + @"Временные ряды\BTC_USD_exmo.txt";
* expert.H.add("input_file", expert.savePreparedDataset(sourceDataFile, "<symbol_time>;<server_time>;<TIME>;<DATE>;<local_time>;<TICKER>;<PER>;<DATEandTIME>;<DATE_TIME>", true));
*
* Expert.addSpread(pathPrefix + @"Временные ряды\BTC_USD_exmo-dataset.txt", sourceDataFile);*/
/* algorithm.h.setValueByName("bid_column_index", "0");
* algorithm.h.setValueByName("ask_column_index", "3");
*
* algorithm.h.setValueByName("start_point", "0");
* algorithm.h.setValueByName("normalize", "true");
* algorithm.h.add("input_file", pathPrefix + @"Временные ряды\BTC_USD_exmo-dataset.txt");
* algorithm.h.add("path_prefix", pathPrefix);
* algorithm.h.add("predicted_column_index:3");
* algorithm.h.setValueByName("show_train_charts", "True");
*
* algorithm.train().Wait();
*/
vis.refresh();
}