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 TestPredictRunsSuccessfullyWithoutException()
{
TestTrainValidData();
List <object> validData = GenerateValidList(5);
KnnModel.Predict(validData, CalcDistance);
KnnModel.PredictTopN(validData, CalcDistance, 6);
}
public object Predict(List <object> inputData, Func <List <double>, List <double>, double> distanceCalculationFunction)
{
return(Model.Predict(inputData, distanceCalculationFunction));
}
