// for training the face,
public void face_training(SVMProblem f_training)
{
SVMProblem trainingSet = SVMProblemHelper.Load(@"C:\Users\temp\Desktop\0921_towp.txt");
SVMProblem testSet = SVMProblemHelper.Load(@"C:\Users\temp\Desktop\0921_towpt.txt");
trainingSet = trainingSet.Normalize(SVMNormType.L2);
testSet = testSet.Normalize(SVMNormType.L2);
SVMParameter parameter = new SVMParameter();
parameter.Type = SVMType.NU_SVC;
parameter.Kernel = SVMKernelType.SIGMOID;
parameter.C = 1;
parameter.Gamma = 1;
parameter.Probability = true;
double[] crossValidationResults;
int nFold = 10;
trainingSet.CrossValidation(parameter, nFold, out crossValidationResults);
double crossValidationAccuracy = trainingSet.EvaluateClassificationProblem(crossValidationResults);
SVMModel model = trainingSet.Train(parameter);
double[] testResults = testSet.Predict(model);
int[,] confusionMatrix;
double testAccuracy = testSet.EvaluateClassificationProblem(testResults, model.Labels, out confusionMatrix);
Training_result.Content = "testAccuracy:" + testAccuracy + "\nCross validation accuracy: " + crossValidationAccuracy + "\nCount " + trainingSet.Y.Count;
Training_result.FontSize = 14;
Training_result.FontStyle = FontStyles.Normal;
Training_result.Foreground = Brushes.Red;
Training_result.Background = Brushes.Black;
index++;
}
private static void Train(string prefix)
{
SVMProblem trainingSet = SVMProblemHelper.Load(MnistDataPath + prefix + ".txt");
trainingSet = trainingSet.Normalize(SVMNormType.L2);
SVMParameter parameter = new SVMParameter();
parameter.Type = SVMType.C_SVC;
parameter.Kernel = SVMKernelType.RBF;
parameter.C = 1;
parameter.Gamma = 1;
double[] crossValidationResults;
int nFold = 5;
trainingSet.CrossValidation(parameter, nFold, out crossValidationResults);
double crossValidationAccuracy = trainingSet.EvaluateClassificationProblem(crossValidationResults);
Console.WriteLine("\n\nCross validation accuracy: " + crossValidationAccuracy);
SVMModel model = trainingSet.Train(parameter);
SVM.SaveModel(model, MnistDataPath + "model.txt");
Console.WriteLine("\n\nModel ok!");
}
public void face_training(SVMProblem f_training)
{
SVMProblem trainingSet = SVMProblemHelper.Load(@"C:\Users\temp\Desktop\0921_towp.txt");
SVMProblem testSet = SVMProblemHelper.Load(@"C:\Users\temp\Desktop\0921_towpt.txt");
// f_training.Save(@"C:\Users\temp\Desktop\1005f.txt");
// trainingSet.Insert(index, f_training.X[0], 2);
trainingSet.Add(f_training.X[0], 1);
trainingSet.Save(@"C:\Users\temp\Desktop\flag.txt");
// trainingSet.Save(@"C:\Users\temp\Desktop\1005.txt");
// Console.WriteLine();
// SVMNode node = new SVMNode();
// node.Index = Convert.ToInt32(o);
// node.Value = Convert.ToDouble(f_training.X);
// nodes.Add(node);
// trainingSet.Add(nodes.ToArray(), 1);
// int number = randon.Next(0, trainingSet.X.Count);
// int trainingsample = Convert.ToInt32(trainingSet.X.Count * 2 / 3);
// int testingsample = Convert.ToInt32(trainingSet.X.Count / 3);
trainingSet = trainingSet.Normalize(SVMNormType.L2);
testSet = testSet.Normalize(SVMNormType.L2);
SVMParameter parameter = new SVMParameter();
parameter.Type = SVMType.NU_SVC;
parameter.Kernel = SVMKernelType.SIGMOID;
parameter.C = 1;
parameter.Gamma = 1;
parameter.Probability = true;
int nFold = 10;
MainWindow main = new MainWindow();
double[] crossValidationResults; // output labels
trainingSet.CrossValidation(parameter, nFold, out crossValidationResults);
double crossValidationAccuracy = trainingSet.EvaluateClassificationProblem(crossValidationResults);
SVMModel model = SVM.Train(trainingSet, parameter);
// SVMModel model = trainingSet.Train(parameter);
SVM.SaveModel(model, @"C:\Users\temp\Desktop\1005.txt");
double[] testResults = testSet.Predict(model);
// Console.WriteLine("");
int[,] confusionMatrix;
double testAccuracy = testSet.EvaluateClassificationProblem(testResults, model.Labels, out confusionMatrix);
// Console.WriteLine("\n\nCross validation accuracy: " + crossValidationAccuracy);
// Console.WriteLine("testAccuracy:" + testAccuracy);
// Console.WriteLine(Convert.ToString(trainingSet.X.Count));
main.Training_result.Content = "testAccuracy:" + testAccuracy + "\nCross validation accuracy: " + crossValidationAccuracy + "\nCount " + trainingSet.X.Count;
main.Training_result.FontSize = 14;
main.Training_result.FontStyle = FontStyles.Normal;
main.Training_result.Foreground = Brushes.Red;
main.Training_result.Background = Brushes.Black;
// Console.WriteLine(trainingSet1.Length);
// trainingSet.Save(@"C:\Users\temp\Desktop\1005.txt");
index++;
}
public static SVMParameter FindBestHyperparameters(SVMProblem problem, SVMParameter parameter)
{
int nFold = int.Parse(Configuration.Get("nFold"));
int logTo = int.Parse(Configuration.Get("logTo"));
int logFrom = int.Parse(Configuration.Get("logFrom"));
BlockingCollection <ParameterResult> results = new BlockingCollection <ParameterResult>();
List <Task> tasks = new List <Task>();
for (double cLog = logFrom; cLog <= logTo; cLog++)
{
double c = Math.Pow(2, cLog);
tasks.Add(Task.Factory.StartNew(() =>
{
for (double gammaLog = logFrom; gammaLog <= logTo; gammaLog++)
{
SVMParameter parameterUnderTest = parameter.Clone();
parameterUnderTest.C = c;
parameterUnderTest.Gamma = Math.Pow(2, gammaLog);
problem.CrossValidation(parameterUnderTest, nFold, out var crossValidationResults);
double crossValidationAccuracy = problem.EvaluateClassificationProblem(crossValidationResults);
results.Add(new ParameterResult()
{
Accuracy = crossValidationAccuracy, C = parameterUnderTest.C,
Gamma = parameterUnderTest.Gamma
});
}
}));
}
Task.WaitAll(tasks.ToArray());
var resultList = results.ToList();
resultList.Sort();
ParameterResult bestParameter =
HighestScore(resultList);
SaveToCsv(results, "svmData.txt");
SVMParameter returnValue = parameter.Clone();
returnValue.C = bestParameter.C;
returnValue.Gamma = bestParameter.Gamma;
return(returnValue);
}
//학습모델 생성
public static SVMModel SVM_GenModel(String dataset)
{
SVMProblem trainingSet = SVMProblemHelper.Load(dataset); //학습데이터셋 열기
trainingSet = trainingSet.Normalize(SVMNormType.L2);
SVMParameter parameter = new SVMParameter();
parameter.Type = SVMType.C_SVC;
parameter.Kernel = SVMKernelType.RBF;
parameter.C = 1;
parameter.Gamma = 1;
double[] crossValidationResults;
int nFold = 5;
trainingSet.CrossValidation(parameter, nFold, out crossValidationResults);
double crossValidationAccuracy = trainingSet.EvaluateClassificationProblem(crossValidationResults);
SVMModel model = trainingSet.Train(parameter); // 학습된 모델 생성
SVM.SaveModel(model, "model_" + dataset); // 모델 저장
return(model);
}
static void Main(string[] args)
{
// Load the datasets: In this example I use the same datasets for training and testing which is not suggested
SVMProblem trainingSet = SVMProblemHelper.Load(@"Dataset\wine.txt");
SVMProblem testSet = SVMProblemHelper.Load(@"Dataset\wine2.txt");
// Normalize the datasets if you want: L2 Norm => x / ||x||
trainingSet = trainingSet.Normalize(SVMNormType.L2);
testSet = testSet.Normalize(SVMNormType.L2);
// Select the parameter set
SVMParameter parameter = new SVMParameter();
parameter.Type = SVMType.C_SVC;
parameter.Kernel = SVMKernelType.RBF;
parameter.C = 1;
parameter.Gamma = 1;
// Do cross validation to check this parameter set is correct for the dataset or not
double[] crossValidationResults; // output labels
int nFold = 5;
trainingSet.CrossValidation(parameter, nFold, out crossValidationResults);
// Evaluate the cross validation result
// If it is not good enough, select the parameter set again
double crossValidationAccuracy = trainingSet.EvaluateClassificationProblem(crossValidationResults);
// Train the model, If your parameter set gives good result on cross validation
SVMModel model = trainingSet.Train(parameter);
// Save the model
SVM.SaveModel(model, @"Model\wine_model.txt");
// Predict the instances in the test set
double[] testResults = testSet.Predict(model);
Console.WriteLine("aaa:" + testResults[0] + "\n");
/*
* // Evaluate the test results
* int[,] confusionMatrix;
* double testAccuracy = testSet.EvaluateClassificationProblem(testResults, model.Labels, out confusionMatrix);
*
*
*
*
* // Print the resutls
* Console.WriteLine("\n\nCross validation accuracy: " + crossValidationAccuracy);
* Console.WriteLine("\nTest accuracy: " + testAccuracy);
* Console.WriteLine("\nConfusion matrix:\n");
*
* // Print formatted confusion matrix
* Console.Write(String.Format("{0,6}", ""));
* for (int i = 0; i < model.Labels.Length; i++)
* Console.Write(String.Format("{0,5}", "(" + model.Labels[i] + ")"));
* Console.WriteLine();
* for (int i = 0; i < confusionMatrix.GetLength(0); i++)
* {
* Console.Write(String.Format("{0,5}", "(" + model.Labels[i] + ")"));
* for (int j = 0; j < confusionMatrix.GetLength(1); j++)
* Console.Write(String.Format("{0,5}", confusionMatrix[i,j]));
* Console.WriteLine();
* }
*
* Console.WriteLine("\n\nPress any key to quit...");
* Console.ReadLine();*/
}
public static bool trainProblem()
{
if (checkExistingDataset())
{
SVMProblem problem = SVMProblemHelper.Load(Constants.DATA_PATH);
SVMProblem randdata = SVMProblemHelper.Load(Constants.RAND_PATH);
List <string> resultsstring = new List <string>();
List <SVMClass.SVMResult> ResultsList = new List <SVMClass.SVMResult>();
double C, gammasq;
double Cmin = 1, Cmax = 10000, Cstep = 10;
double gmin = 0.0001, gmax = 1000, gstep = 10;
bool satisfied = false;
while (!satisfied)
{
for (C = Cmin; C <= Cmax; C = C * Cstep)
{
for (gammasq = gmin; gammasq <= gmax; gammasq = gammasq * gstep)
{
SVMParameter tempparameter = new SVMParameter();
tempparameter.Type = SVMType.C_SVC;
tempparameter.Kernel = SVMKernelType.RBF;
tempparameter.C = C;
tempparameter.Gamma = gammasq;
SVMModel tempmodel = SVM.Train(problem, tempparameter);
SVMProblem testData = SVMProblemHelper.Load(Constants.RAND_PATH);
double[] results = testData.Predict(tempmodel);
int[,] confusionMatrix;
double testAccuracy = testData.EvaluateClassificationProblem(results, tempmodel.Labels, out confusionMatrix);
// Do cross validation to check this parameter set is correct for the dataset or not
double[] crossValidationResults; // output labels
int nFold = 10;
problem.CrossValidation(tempparameter, nFold, out crossValidationResults);
// Evaluate the cross validation result
// If it is not good enough, select the parameter set again
double crossValidationAccuracy = problem.EvaluateClassificationProblem(crossValidationResults);
SVMClass.SVMResult compiled = new SVMClass.SVMResult();
compiled.C = C;
compiled.gamma = gammasq;
compiled.testAcc = testAccuracy;
compiled.crossValidAcc = crossValidationAccuracy;
ResultsList.Add(compiled);
}
}
// Evaluate the test results
double maxTestAcc = ResultsList.Max(resultdata => resultdata.testAcc);
//int maxTestAccIndex = ResultsList.FindIndex(resultdata => resultdata.testAcc.Equals(maxTestAcc));
double maxValidAcc = ResultsList.Max(resultdata => resultdata.crossValidAcc);
//int maxValidAccIndex = ResultsList.FindIndex(resultdata => resultdata.crossValidAcc.Equals(maxValidAcc));
if (maxTestAcc < 95 || maxValidAcc < 95)
{
satisfied = false;
Cstep--;
gstep--;
}
else
{
satisfied = true;
List <SVMClass.SVMResult> topResults = ResultsList.FindAll(resultdata => resultdata.testAcc.Equals(maxTestAcc));
List <SVMClass.SVMResult> topValid = ResultsList.FindAll(resultdata => resultdata.crossValidAcc.Equals(maxValidAcc));
while (topResults.Count > topValid.Count)
{
topResults.RemoveAt(ResultsList.FindIndex(resultsdata => resultsdata.crossValidAcc.Equals(ResultsList.Min(resultdata => resultdata.crossValidAcc))));
}
double maxC = topResults.Max(resultdata => resultdata.C);
int maxCIndex = topResults.FindIndex(resultdata => resultdata.C.Equals(maxC));
double bestgamma = topResults[maxCIndex].gamma;
// maxC or not???
//double bestC = topResults[topResults.Count - 2].C; //topResults[maxCIndex].C;
//double bestgamma = topResults[topResults.Count - 2].gamma;//topResults[maxCIndex].gamma;
Console.WriteLine("Best C: " + maxC + " Best gammasq: " + bestgamma);
Constants.C = maxC;
Constants.gammasq = bestgamma;
foreach (SVMClass.SVMResult resultdata in topResults)
{
Console.WriteLine(resultdata.C.ToString() + " " + resultdata.gamma.ToString());
}
}
}
SVMParameter parameter = new SVMParameter();
parameter.Type = SVMType.C_SVC;
parameter.Kernel = SVMKernelType.RBF;
parameter.C = Constants.C;
parameter.Gamma = Constants.gammasq;
Variables.model = SVM.Train(problem, parameter);
//File.WriteAllText(Constants.MODEL_PATH, String.Empty);
//SVM.SaveModel(Variables.model, Constants.MODEL_PATH);
Console.WriteLine("Trained and saved model.\n");
//return Variables.model;
return(true);
}
else
{
MessageBox.Show("Invalid training data!");
return(false);
}
}
private void testSVM()
{
if (!holdCommandListener)
{
holdCommandListener = true;
}
string parentpath = System.AppDomain.CurrentDomain.BaseDirectory;
string DATA_PATH = parentpath + "Datasets\\dataset - Copy (2).txt";
string MODEL_PATH = parentpath + "Model\\testmodel.txt";
string NEWDATA_PATH = parentpath + "Datasets\\testdata.txt";
string RESULTS_PATH = parentpath + "Datasets\\results.txt";
List <string> resultsstring = new List <string>();
SVMProblem testSet = SVMProblemHelper.Load(NEWDATA_PATH);
SVMParameter testparameter = new SVMParameter();
testparameter.Type = SVMType.C_SVC;
testparameter.Kernel = SVMKernelType.RBF;
testparameter.C = 0.1; //Constants.C;
testparameter.Gamma = 0.001; // Constants.gammasq;
List <SVMClass.SVMResult> ResultsList = new List <SVMClass.SVMResult>();
SVMProblem problem = SVMProblemHelper.Load(DATA_PATH);
double C = 0.001;
double gammasq = 0.001;
for (C = 1; C <= 1000; C = C * 10)
{
for (gammasq = 0.001; gammasq <= 1000; gammasq = gammasq * 10)
{
SVMParameter parameter = new SVMParameter();
parameter.Type = SVMType.C_SVC;
parameter.Kernel = SVMKernelType.RBF;
parameter.C = C;
parameter.Gamma = gammasq;
SVMModel model = SVM.Train(problem, parameter);
//File.WriteAllText(MODEL_PATH, String.Empty);
//SVM.SaveModel(model, MODEL_PATH);
//Console.WriteLine("Trained and saved model.\n");
//model = SVM.LoadModel(MODEL_PATH);
SVMProblem newData = SVMProblemHelper.Load(NEWDATA_PATH);
//Console.Write("Predicted Result:\n");
double[] results = newData.Predict(model);
//Console.Write(results[0]);
int[,] confusionMatrix;
double testAccuracy = newData.EvaluateClassificationProblem(results, model.Labels, out confusionMatrix);
// Do cross validation to check this parameter set is correct for the dataset or not
double[] crossValidationResults; // output labels
int nFold = 10;
problem.CrossValidation(parameter, nFold, out crossValidationResults);
// Evaluate the cross validation result
// If it is not good enough, select the parameter set again
double crossValidationAccuracy = problem.EvaluateClassificationProblem(crossValidationResults);
//Console.WriteLine("\n\nCross validation accuracy: " + crossValidationAccuracy);
string temp = "";
string resultstring = "Predict accuracy: " + testAccuracy + " C: " + C + " gamma: " + gammasq + " Cross validation accuracy: " + crossValidationAccuracy;
resultsstring.Add(resultstring);
if (parameter.C == testparameter.C && parameter.Gamma == testparameter.Gamma)
{
resultsstring.Add("This one is same as separate test.");
}
foreach (double res in results)
{
temp += res.ToString() + " ";
}
resultsstring.Add(temp);
SVMClass.SVMResult compiled = new SVMClass.SVMResult();
compiled.C = C;
compiled.gamma = gammasq;
compiled.testAcc = testAccuracy;
compiled.crossValidAcc = crossValidationAccuracy;
ResultsList.Add(compiled);
}
}
File.WriteAllLines(RESULTS_PATH, resultsstring);
SVMModel testmodel = SVM.Train(problem, testparameter);
// Predict the instances in the test set
double[] testResults = testSet.Predict(testmodel);
foreach (double result in testResults)
{
Console.WriteLine(result);
}
// Evaluate the test results
double maxTestAcc = ResultsList.Max(resultdata => resultdata.testAcc);
int maxTestAccIndex = ResultsList.FindIndex(resultdata => resultdata.testAcc.Equals(maxTestAcc));
//double maxValidAcc = ResultsList.Max(resultdata => resultdata.crossValidAcc);
//int maxValidAccIndex = ResultsList.FindIndex(resultdata => resultdata.crossValidAcc.Equals(maxValidAcc));
List <SVMClass.SVMResult> topResults = ResultsList.FindAll(resultdata => resultdata.testAcc.Equals(maxTestAcc));
double maxC = topResults.Max(resultdata => resultdata.C);
int maxCIndex = topResults.FindIndex(resultdata => resultdata.C.Equals(maxC));
double bestC = topResults[topResults.Count - 2].C; //topResults[maxCIndex].C;
double bestgamma = topResults[topResults.Count - 2].gamma; //topResults[maxCIndex].gamma;
Console.WriteLine("Best C: " + bestC + " Best gammasq: " + bestgamma);
foreach (SVMClass.SVMResult resultdata in topResults)
{
Console.WriteLine(resultdata.C.ToString() + " " + resultdata.gamma.ToString());
}
//int[,] confusionMatrix;
//double testAccuracy = testSet.EvaluateClassificationProblem(testResults, testmodel.Labels, out confusionMatrix);
//Console.WriteLine("\n\nTest accuracy: " + testAccuracy);
}
public List <PredictionResult> CrossValidate(SAMDataPoint.FeelingModel feelingsmodel, bool useIAPSratings = false, Normalize normalizationType = Normalize.OneMinusOne)
{
List <PredictionResult> predictedResults = new List <PredictionResult>();
//Split into crossvalidation parts
SVMProblem problems = GetFeatureValues(features, samData).NormalizeFeatureList <double>(normalizationType).CreateCompleteProblem(samData, feelingsmodel);
//Get correct results
int[] answers = samData.dataPoints.Select(x => x.ToAVCoordinate(feelingsmodel, useIAPSratings)).ToArray();
int progressCounter = 0;
if (answers.Distinct().Count() <= 1)
{
int numberOfLabels = SAMData.GetNumberOfLabels(feelingsmodel);
//Calculate scoring results
double[,] confus = CalculateConfusion(answers.ToList().ConvertAll(x => (double)x).ToArray(), answers, numberOfLabels);
List <double> pres = CalculatePrecision(confus, numberOfLabels);
List <double> recall = CalculateRecall(confus, numberOfLabels);
List <double> fscore = CalculateFScore(pres, recall);
PredictionResult pR = new PredictionResult(confus, recall, pres, fscore, new SVMParameter(), features, answers.ToList(), answers.ToList().ConvertAll(x => (int)x));
predictedResults.Add(pR);
progressCounter++;
Log.LogMessage(ONLY_ONE_CLASS);
Log.LogMessage("");
return(predictedResults);
}
else if (problems.X.Count == 0)
{
Log.LogMessage("Empty problem in " + Name);
return(null);
}
foreach (SVMParameter SVMpara in Parameters)
{
if (UpdateCallback != null)
{
UpdateCallback(progressCounter, Parameters.Count);
}
double[] guesses = new double[samData.dataPoints.Count];
//model and predict each nfold
try
{
problems.CrossValidation(SVMpara, samData.dataPoints.Count, out guesses);
}
catch (Exception e)
{
for (int i = 0; i < samData.dataPoints.Count; i++)
{
guesses[i] = -1;
}
}
int numberOfLabels = SAMData.GetNumberOfLabels(feelingsmodel);
//Calculate scoring results
double[,] confus = CalculateConfusion(guesses.ToArray(), answers, numberOfLabels);
List <double> pres = CalculatePrecision(confus, numberOfLabels);
List <double> recall = CalculateRecall(confus, numberOfLabels);
List <double> fscore = CalculateFScore(pres, recall);
PredictionResult pR = new PredictionResult(confus, recall, pres, fscore, SVMpara, features, answers.ToList(), Array.ConvertAll(guesses, (x => (int)x)).ToList());
predictedResults.Add(pR);
progressCounter++;
if (UpdateCallback != null)
{
UpdateCallback(progressCounter, Parameters.Count);
}
}
return(predictedResults);
}
private void button2_Click(object sender, EventArgs e)
{
SVMProblem trainingSet = new SVMProblem();
SVMProblem testSet = trainingSet;
foreach (DataInfo info in mList)
{
SVMNode[] node = new SVMNode[2];
node[0] = new SVMNode(1, info.X / mWidth);
node[1] = new SVMNode(2, info.Y / mHeight);
trainingSet.Add(node, info.Group);
}
// Normalize the datasets if you want: L2 Norm => x / ||x||
//trainingSet = trainingSet.Normalize(SVMNormType.L2);
// Select the parameter set
SVMParameter parameter = new SVMParameter();
parameter.Type = SVMType.C_SVC;
parameter.Kernel = SVMKernelType.RBF;
parameter.C = 1;
parameter.Gamma = 4;
parameter.Coef0 = hScrollBar1.Value;
parameter.Degree = 3;
// Do cross validation to check this parameter set is correct for the dataset or not
double[] crossValidationResults; // output labels
int nFold = 5;
trainingSet.CrossValidation(parameter, nFold, out crossValidationResults);
// Evaluate the cross validation result
// If it is not good enough, select the parameter set again
double crossValidationAccuracy = trainingSet.EvaluateClassificationProblem(crossValidationResults);
// Train the model, If your parameter set gives good result on cross validation
SVMModel model = trainingSet.Train(parameter);
// Save the model
SVM.SaveModel(model, FILE_MODEL);
// Predict the instances in the test set
double[] testResults = testSet.Predict(model);
// Evaluate the test results
int[,] confusionMatrix;
double testAccuracy = testSet.EvaluateClassificationProblem(testResults, model.Labels, out confusionMatrix);
// Print the resutls
Console.WriteLine("\n\nCross validation accuracy: " + crossValidationAccuracy);
Console.WriteLine("\nTest accuracy: " + testAccuracy);
Console.WriteLine("\nConfusion matrix:\n");
// Print formatted confusion matrix
Console.Write(String.Format("{0,6}", ""));
for (int i = 0; i < model.Labels.Length; i++)
{
Console.Write(String.Format("{0,5}", "(" + model.Labels[i] + ")"));
}
Console.WriteLine();
for (int i = 0; i < confusionMatrix.GetLength(0); i++)
{
Console.Write(String.Format("{0,5}", "(" + model.Labels[i] + ")"));
for (int j = 0; j < confusionMatrix.GetLength(1); j++)
{
Console.Write(String.Format("{0,5}", confusionMatrix[i, j]));
}
Console.WriteLine();
}
Pen[] pen = new Pen[4];
pen[0] = new Pen(Color.Black, 1);
pen[1] = new Pen(Color.Red, 1);
pen[2] = new Pen(Color.LightGreen, 1);
pen[3] = new Pen(Color.Blue, 1);
Pen[] pen2 = new Pen[4];
pen2[0] = new Pen(Color.LightGray, 1);
pen2[1] = new Pen(Color.DarkRed, 1);
pen2[2] = new Pen(Color.DarkGreen, 1);
pen2[3] = new Pen(Color.DarkBlue, 1);
Bitmap canvas = new Bitmap(pictureBox1.ClientSize.Width, pictureBox1.ClientSize.Height);
using (Graphics g = Graphics.FromImage(canvas))
{
for (int i = 0; i < pictureBox1.ClientSize.Width; i++)
{
for (int j = 0; j < pictureBox1.ClientSize.Height; j++)
{
SVMNode[] node = new SVMNode[2];
node[0] = new SVMNode(1, (double)i / (double)mWidth);
node[1] = new SVMNode(2, (double)j / (double)mHeight);
double result = SVM.Predict(model, node);
g.DrawRectangle(pen2[(int)result], i, j, 1, 1);
}
}
foreach (DataInfo info in mList)
{
g.DrawEllipse(pen[(int)info.Group], (float)info.X - 5, (float)info.Y - 5, 5, 5);
}
}
Bitmap image = new Bitmap(pictureBox1.ClientSize.Width, pictureBox1.ClientSize.Height);
pictureBox1.BackgroundImage = canvas; // 設置為背景層
pictureBox1.Refresh();
pictureBox1.CreateGraphics().DrawImage(canvas, 0, 0);
}