private static void StartSCProblem()
{
var bestClassifiers = new Individual[8];
CultureInfo.DefaultThreadCurrentCulture = CultureInfo.InvariantCulture;
ECActivator.AddSourceAssemblies(new[] { Assembly.GetAssembly(typeof(IEvolutionState)), Assembly.GetAssembly(typeof(MultiClassifierProblem)) });
var parameters = Evolve.LoadParameterDatabase(new[] { "-file", @"Params\App\Iris\single.params" });
IEvolutionState state = Evolve.Initialize(parameters, 0);
var date = DateTime.Now.Ticks.ToString();
var directoryName = "stats/" + date;
var directory = System.IO.Directory.CreateDirectory(directoryName);
var writer = new System.IO.StreamWriter(directoryName + "/multi_cl_stats.txt", false);
// IEvolutionState state = Evolve.Initialize(Evolve.LoadParameterDatabase(new[] { "-file", @"Params\App\Iris\single.params" }), 0);
for (var i = 0; i < 8; i++)
{
// parameters = Evolve.LoadParameterDatabase(new[] { "-file", @"Params\App\Iris\single.params", "-stat.file", @"out1.stat" });
if (i != 0)
{
state = Evolve.Initialize(parameters, 0);
}
MultiClassificationClass.Current = MultiClassificationClass.Labels[i];
// state.Output.FilePrefix = i.ToString();
state.Run(EvolutionState.C_STARTED_FRESH);
var best = ((SimpleStatistics)((SimpleEvolutionState)state).Statistics).BestOfRun[0];
int humanGraph = state.Output.AddLog(directoryName + "/multi_cl_human_graph_" + MultiClassificationClass.Current + ".txt");
int ecjGraph = state.Output.AddLog(directoryName + "/multi_cl_ecj_graph_" + MultiClassificationClass.Current + ".txt");
((GPIndividual)best).Trees[0].PrintStyle = GPTree.PRINT_STYLE_DOT;
((GPIndividual)best).Trees[0].PrintTreeForHumans(state, humanGraph);
((GPIndividual)best).Trees[0].PrintTree(state, ecjGraph);
state.Output.Close();
// System.IO.File.Delete("out.stat");
bestClassifiers[i] = best;
var writer2 = new System.IO.StreamWriter(directoryName + "/classifier_code_" + MultiClassificationClass.Current + ".txt");
var code = TreeReader.PrintCodeFromTree(((GPIndividual)best).Trees[0]);
writer2.Write(code);
writer2.Close();
var accuracy = (double)(1552 - ((KozaFitness)bestClassifiers[i].Fitness).StandardizedFitness) / 1552;
writer.WriteLine("Klasyfikator dla gestu " + MultiClassificationClass.Labels[i]);
writer.WriteLine("Ilość generacji: " + state.NumGenerations.ToString());
writer.WriteLine("Rozmiar drzewa: " + ((GPIndividual)bestClassifiers[i]).Size.ToString());
writer.WriteLine("Poprawność klasyfikacji: " + accuracy.ToString());
writer.WriteLine();
// ((GPIndividual)bestClassifiers[0]).Trees[0].Child.Eval
//((GPIndividual)bestClassifiers[0]).Trees[0].
// ((SingleClassifierProblem)state.Evaluator.p_problem)
}
writer.Close();
CheckClassifierOnTestData(bestClassifiers, state, (int)ClassifierType.MULTI_CLASSIFIER, directoryName);
}
private static void StartGP()
{
CultureInfo.DefaultThreadCurrentCulture = CultureInfo.InvariantCulture;
var test = new DataLoader(@"Datasets\gesty_pogrupowane.txt", 11, 1491, 1, 2, '\t');
ECActivator.AddSourceAssemblies(new[] { Assembly.GetAssembly(typeof(IEvolutionState)), Assembly.GetAssembly(typeof(ClassificationProblem)) });
IEvolutionState state = Evolve.Initialize(Evolve.LoadParameterDatabase(new[] { "-file", @"Params\App\Iris\koza.params" }), 2);
state.Run(EvolutionState.C_STARTED_FRESH);
var best = ((SimpleStatistics)((SimpleEvolutionState)state).Statistics).BestOfRun[0];
var date = DateTime.Now.Ticks.ToString();
var directoryName = "stats/" + date;
var directory = System.IO.Directory.CreateDirectory(directoryName);
var stats = new List <string>();
stats.Add("Ilość pokoleń: " + state.NumGenerations);
var accuracy = (double)(1552 - ((KozaFitness)best.Fitness).StandardizedFitness) / 1552;
stats.Add("Rozmiar drzewa: " + ((GPIndividual)best).Size.ToString());
stats.Add("Głębokość drzewa: " + ((GPIndividual)best).Trees[0].Child.Depth.ToString());
stats.Add("Poprawność klasyfikacji dla danych uczących: " + accuracy.ToString());
System.IO.File.WriteAllLines(directoryName + "/single_cl_stats.txt", stats.ToArray());
var writer = new System.IO.StreamWriter(directoryName + "/classifier_code.txt");
var code = TreeReader.PrintCodeFromTree(((GPIndividual)best).Trees[0]);
writer.Write(code);
writer.Close();
int humanGraph = state.Output.AddLog(directoryName + "/single_cl_human_graph.txt");
int ecjGraph = state.Output.AddLog(directoryName + "/single_cl_ecj_graph.txt");
((GPIndividual)best).Trees[0].PrintStyle = GPTree.PRINT_STYLE_DOT;
((GPIndividual)best).Trees[0].PrintTreeForHumans(state, humanGraph);
((GPIndividual)best).Trees[0].PrintTree(state, ecjGraph);
CheckClassifierOnTestData(new[] { best }, state, (int)ClassifierType.SINGLE_CLASSIFIER, directoryName);
//var data = new ClassificationData();
/*
* state.Output.AddLog(@"F:\Logs\test.gv");
* ((GPIndividual)best[0]).Trees[0].PrintStyle = GPTree.PRINT_STYLE_DOT;
* ((GPIndividual)best[0]).Trees[0].PrintTreeForHumans(state, state.Output.NumLogs - 1);
* ((GPIndividual)best[0]).Trees[0].PrintTree(state, state.Output.NumLogs - 1);
*/
}
private static void TestCode()
{
ECActivator.AddSourceAssemblies(new[] { Assembly.GetAssembly(typeof(IEvolutionState)), Assembly.GetAssembly(typeof(ClassificationProblem)) });
var parameters = Evolve.LoadParameterDatabase(new[] { "-file", @"Params\App\Iris\koza.params" });
IEvolutionState state = Evolve.Initialize(parameters, 0);
state.Setup(state, new Parameter(new string[] { "a" }));
//state.Run(EvolutionState.C_STARTED_FRESH);
// var individual = (GPIndividual)((SimpleStatistics)state.Statistics).BestOfRun[0];
var tree = new GPTree();
var reader = new System.IO.StreamReader("single_cl_ecj_graph.txt");
tree.ReadTree(state, reader);
var writer = new System.IO.StreamWriter("testcode.txt");
var code = TreeReader.PrintCodeFromTree(tree);
writer.Write(code);
writer.Close();
}
