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);
}
