static void Main(string[] args)
{
//double[][] trainInputs =
//{
//// The first two are from class 0
//new double[] { -5, -2, -1 },
//new double[] { -5, -5, -6 },
//// The next four are from class 1
//new double[] { 2, 1, 1 },
//new double[] { 1, 1, 2 },
//new double[] { 1, 2, 2 },
//new double[] { 3, 1, 2 },
//// The last three are from class 2
//new double[] { 11, 5, 4 },
//new double[] { 15, 5, 6 },
//new double[] { 10, 5, 6 },
//};
//int[] trainOutputs =
//{
// 0, 0, // First two from class 0
// 1, 1, 1, 1, // Next four from class 1
// 2, 2, 2 // Last three from class 2
//};
//double[][] testInputs =
//{
//// The first two are from class 0
//new double[] { -3, -1, -1 },
//new double[] { -9, -7, -5 },
//};
//int[] testOutputs =
//{
// 0, 0, // First two from class 0
//};
DataTable dataTrain = new CsvReader(@"H:\Documents\Visual Studio 2015\Projects\ML\ML\CSV\train\train.csv", true).ToTable();
DataTable dataTest = new CsvReader(@"H:\Documents\Visual Studio 2015\Projects\ML\ML\CSV\test\testWithLabels.csv", true).ToTable();
// I/O data //
int[] trainOutputs = dataTrain.Columns["label"].ToArray <int>();
dataTrain.Columns.Remove("label");
double[][] trainInputs = dataTrain.ToJagged <double>();
int[] testOutputs = dataTest.Columns["label"].ToArray <int>();
dataTest.Columns.Remove("label");
double[][] testInputs = dataTest.ToJagged <double>();
// I/O data //
// knn //B
var knn = new KNN(trainInputs, trainOutputs, 4);
var machineKNN = knn.MachineLearning();
int[] predictedKNN = machineKNN.Decide(testInputs);
machineKNN.Save(@"H:\Documents\Visual Studio 2015\Projects\ML\ML\models\knn.bin");
OutputResultsСlassifier showKNN = new OutputResultsСlassifier(machineKNN, testInputs, testOutputs);
showKNN.SavePredicted(predictedKNN);
showKNN.SaveAccuracy();
// knn //
// nb //
var nb = new NB(trainInputs, trainOutputs);
var machineNB = nb.MachineLearning();
int[] predictedNB = machineNB.Decide(testInputs);
machineNB.Save(@"H:\Documents\Visual Studio 2015\Projects\ML\ML\models\nb.bin");
OutputResultsСlassifier show_nb = new OutputResultsСlassifier(machineNB, testInputs, testOutputs);
show_nb.SavePredicted(predictedNB);
show_nb.SaveAccuracy();
// nb //
// svm //
var svm = new SVM(trainInputs, trainOutputs);
var machineSVM = svm.MachineLearning();
int[] predictedSVM = machineSVM.Decide(testInputs);
machineSVM.Save(@"H:\Documents\Visual Studio 2015\Projects\ML\ML\models\svm.bin");
OutputResultsСlassifier showSVM = new OutputResultsСlassifier(machineSVM, testInputs, testOutputs);
showSVM.SavePredicted(predictedSVM);
showSVM.SaveAccuracy();
// svm //
// mlr //
var mlr = new MLR(trainInputs, trainOutputs);
var machineMLR = mlr.MachineLearning();
int[] predictedMLR = machineMLR.Decide(testInputs);
machineMLR.Save(@"H:\Documents\Visual Studio 2015\Projects\ML\ML\models\mlr.bin");
OutputResultsСlassifier showMLR = new OutputResultsСlassifier(machineMLR, testInputs, testOutputs);
showMLR.SavePredicted(predictedSVM);
showMLR.SaveAccuracy();
showMLR.SaveProbabilities(machineMLR);
// mlr //
}
