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);
*/
}
static void Main(string[] args)
{
CultureInfo.DefaultThreadCurrentCulture = CultureInfo.InvariantCulture;
ECActivator.AddSourceAssemblies(new[] { Assembly.GetAssembly(typeof(IEvolutionState)), Assembly.GetAssembly(typeof(FeatureExtractionProblem2)) });
IEvolutionState state = Evolve.Initialize(Evolve.LoadParameterDatabase(new[] { "-file", @"Parameters\problem2.params" }), 11);
state.Run(EvolutionState.C_STARTED_FRESH);
var problem = (FeatureExtractionProblem2)state.Evaluator.p_problem;
// var treeLog1 = state.Output.AddLog(@"F:\Gesty\logs\gvtree_gen1.txt");
//var treeLog2 = state.Output.AddLog(@"F:\Gesty\logs\gvtree_gen10.txt");
//var ecjGraph = state.Output.AddLog(@"F:\Gesty\logs\ecjGraph.txt");
//var bestOfRun = ((SimpleStatistics)state.Statistics).BestOfRun[0];
//((GPIndividual)bestOfRun).Trees[0].PrintTree(state, ecjGraph);
//((GPIndividual)bestOfRun).Trees[0].PrintStyle = GPTree.PRINT_STYLE_DOT;
//((GPIndividual)bestOfRun).Trees[0].PrintTreeForHumans(state, treeLog);
var input = problem.Input;
var stack = problem.Stack;
var stats = (SimpleStatistics)state.Statistics;
var bestOfRun = (GPIndividual)stats.BestOfRun[0];
var treeReader = new StreamReader(@"F:\Gesty\logs\ecjGraph.txt");
bestOfRun.Trees[0].ReadTree(state, treeReader);
bestOfRun.Evaluated = false;
var imageList = new string[] {
@"F:\Gesty\problem2\grayscale\A\a\color_0_0002.png",
@"F:\Gesty\problem2\grayscale\A\b\color_1_0002.png",
@"F:\Gesty\problem2\grayscale\A\c\color_2_0002.png",
@"F:\Gesty\problem2\grayscale\A\f\color_5_0002.png",
@"F:\Gesty\problem2\grayscale\A\h\color_7_0002.png"
};
int i = 0;
foreach (string image in imageList)
{
var dir = @"F:\Gesty\testy\examples";
var tempImg = new Image <Gray, Byte>(image);
tempImg.CopyTo(problem.currentImage[0]);
tempImg.CopyTo(problem.originalImage[0]);
bestOfRun.Trees[0].Child.Eval(state, 0, input, stack, bestOfRun, problem);
tempImg.Save(dir + @"\" + i + ".png");
problem.currentImage[0].Save(dir + @"\" + i + "_trans.png");
ImageTransformer.GetSquareSuperpixelImages(problem.currentImage[0], dir, i.ToString(), 8);
i++;
}
problem.Evaluate(state, bestOfRun, 0, 0);
Console.WriteLine(bestOfRun.Fitness);
/*
* var confMatFile = new StreamWriter(@"F:\Gesty\testy\confmatBest.csv");
* for (int x = 0; x < 10; x++)
* {
* var line = new StringBuilder();
* for (int j = 0; j < 10; j++)
* {
* line.Append(problem.confMat[x, j].ToString() + ';');
* }
* confMatFile.WriteLine(line.ToString().Trim(';'));
* }
* confMatFile.Close();
*/
// Console.WriteLine(bestOfRun.Fitness);
/*
* var tempImg = new Image<Gray, Byte>(@"F:\Gesty\problem2\grayscale\A\a\color_0_0002.png");
* tempImg.CopyTo(problem.currentImage[0]);
* tempImg.CopyTo(problem.originalImage[0]);
* ((GPIndividual)bestOfRun).Trees[0].Child.Eval(state, 0, input, stack, bestOfRun, problem);
* problem.currentImage[0].Save(@"F:\Gesty\testy\transformed.png");
*/
/*
* var gesty = new string[8] { "piesc", "dlon", "1p", "2p", "3p", "4p", "5p", "kciuk" };
* var imageIndex = 0;
* foreach (string gest in gesty)
* {
* var dir = @"F:\Gesty\superpixel\200x200\" + gest;
*
* //oryginalny
* var tempImg = new Image<Gray, Byte>(problem.imageList[imageIndex]);
* tempImg.CopyTo(problem.currentImage[0]);
* tempImg.CopyTo(problem.originalImage[0]);
* tempImg.Dispose();
* bestOfRun.Trees[0].Child.Eval(state, 0, input, stack, bestOfRun, problem);
* problem.imageTransformer.GetSuperpixelImages(problem.currentImage[0], dir, "oryginalny");
* imageIndex += 101;
*
* //obrocony
* var tempImg2 = new Image<Gray, Byte>(problem.imageList[imageIndex]);
* tempImg2.CopyTo(problem.currentImage[0]);
* tempImg2.CopyTo(problem.originalImage[0]);
* tempImg2.Dispose();
* bestOfRun.Trees[0].Child.Eval(state, 0, input, stack, bestOfRun, problem);
* problem.imageTransformer.GetSuperpixelImages(problem.currentImage[0], dir, "obrocony");
* imageIndex += 101;
*
* //zaklocony
* var tempImg3 = new Image<Gray, Byte>(problem.imageList[imageIndex]);
* tempImg3.CopyTo(problem.currentImage[0]);
* tempImg3.CopyTo(problem.originalImage[0]);
* tempImg3.Dispose();
* bestOfRun.Trees[0].Child.Eval(state, 0, input, stack, bestOfRun, problem);
* problem.imageTransformer.GetSuperpixelImages(problem.currentImage[0], dir, "zaklocony");
* imageIndex += 11;
* }
*/
Console.ReadKey();
}