public void ANN_BP_Test()
{
Logger.Info("ANN_BP_Test");
var problem = ProblemFactory.CreateClassificationProblem(ClassificationProblemType.ChessBoard);
Logger.Info("Loading training data.");
var tSet = problem.TrainingSet;
var vSet = problem.ValidationSet;
var classifier = new ANN_BP(problem.Dimension)
{
TrainSet = tSet,
MaximumIteration = int.MaxValue,
ANN_Eta = 0.5,
ANN_Epsilon = 1e-3,
LogInterval = 1000
};
classifier.Train();
Logger.Info("Doing cross-validation.");
var hit = (from e in vSet.Examples let iResult = classifier.Predict(e.X) where e.Label.Id == iResult select e).Count();
var correctRatio = 1.0 * hit / vSet.Count;
Logger.Info("CorrectRatio: {0}", correctRatio);
Assert.IsTrue(correctRatio > 0.900, string.Format("ANN_BP (2-class) Correct Ratio, expected: greater than 0.930, actual: {0}.", correctRatio));
}
public void Binary_SVM_SMO_Test()
{
Logger.Info("Binary_SVM_SMO_Test");
var problem = ProblemFactory.CreateClassificationProblem(ClassificationProblemType.ChessBoard);
var tSet = problem.TrainingSet;
var vSet = problem.ValidationSet;
var classifier = new Binary_SVM_SMO(problem)
{
TrainSet = tSet,
Kernel = new GaussianRBFKernel(0.001)
};
classifier.Train();
Logger.Info("Doing cross-validation.");
var hit = (from e in vSet.Examples let iResult = classifier.Predict(e.X) where e.Label.Id == iResult select e).Count();
var correctRatio = 1.0 * hit / vSet.Count;
Assert.IsTrue(correctRatio > 0.950, string.Format("SVM-SMO (2-class) Correct Ratio, expected: greater than 0.970, actual: {0}.", correctRatio));
}
public void KNN_Test()
{
Logger.Info("KNN_Test");
var problem = ProblemFactory.CreateClassificationProblem(ClassificationProblemType.ChessBoard);
Logger.Info("Loading training data.");
var tSet = problem.TrainingSet;
var vSet = problem.ValidationSet;
var classifier = new KNN
{
KNN_K = 7,
TrainSet = tSet
};
classifier.Train();
Logger.Info("Doing cross-validation.");
var hit = (from e in vSet.Examples let iResult = classifier.Predict(e.X) where e.Label.Id == iResult select e).Count();
var correctRatio = 1.0 * hit / vSet.Count;
Logger.Info("CorrectRatio: {0}", correctRatio);
Assert.IsTrue(correctRatio > 0.900, string.Format("KNN (2-class) Correct Ratio, expected: greater than 0.900, actual: {0}.", correctRatio));
}
public void AdaBoost_Test()
{
Logger.Info("AdaBoost_Test");
var problem = ProblemFactory.CreateClassificationProblem(ClassificationProblemType.ChessBoard);
var tSet = problem.TrainingSet;
var vSet = problem.ValidationSet;
var classifier = new AdaBoost(tSet, 1000, problem.Dimension);
classifier.Train();
var hit = (from e in vSet.Examples let iResult = classifier.Predict(e.X) where e.Label.Id == iResult select e).Count();
var correctRatio = 1.0 * hit / vSet.Count;
Logger.Info("CorrectRatio: {0}", correctRatio);
Assert.IsTrue(correctRatio > 0.900, string.Format("AdaBoost (2-class) Correct Ratio, expected: greater than 0.900, actual: {0}.", correctRatio));
}
