static void Main()
{
// STEP 4: Read the data
const string dataFilePath = @"D:\MACHINE_LEARNING\SVM\Tutorial\sunnyData.csv";
var dataTable = DataTable.New.ReadCsv(dataFilePath);
List<string> x = dataTable.Rows.Select(row => row["Text"]).ToList();
double[] y = dataTable.Rows.Select(row => double.Parse(row["IsSunny"]))
.ToArray();
var vocabulary = x.SelectMany(GetWords).Distinct().OrderBy(word => word).ToList();
var problemBuilder = new TextClassificationProblemBuilder();
var problem = problemBuilder.CreateProblem(x, y, vocabulary.ToList());
// If you want you can save this problem with :
// ProblemHelper.WriteProblem(@"D:\MACHINE_LEARNING\SVM\Tutorial\sunnyData.problem", problem);
// And then load it again using:
// var problem = ProblemHelper.ReadProblem(@"D:\MACHINE_LEARNING\SVM\Tutorial\sunnyData.problem");
const int C = 1;
var model = new C_SVC(problem, KernelHelper.LinearKernel(), C);
var accuracy = model.GetCrossValidationAccuracy(10);
Console.Clear();
Console.WriteLine(new string('=', 50));
Console.WriteLine("Accuracy of the model is {0:P}", accuracy);
model.Export(string.Format(@"D:\MACHINE_LEARNING\SVM\Tutorial\model_{0}_accuracy.model", accuracy));
Console.WriteLine(new string('=', 50));
Console.WriteLine("The model is trained. \r\nEnter a sentence to make a prediction. (ex: sunny rainy sunny)");
Console.WriteLine(new string('=', 50));
string userInput;
_predictionDictionary = new Dictionary<int, string> { { -1, "Rainy" }, { 1, "Sunny" } };
do
{
userInput = Console.ReadLine();
var newX = TextClassificationProblemBuilder.CreateNode(userInput, vocabulary);
var predictedY = model.Predict(newX);
Console.WriteLine("The prediction is {0}", _predictionDictionary[(int)predictedY]);
Console.WriteLine(new string('=', 50));
} while (userInput != "quit");
Console.WriteLine("");
}
public void C_SVC_should_enable_to_export_and_import_svm_models()
{
// note : K(u; v) = (u v + 1)^2 kernel is able to feet exactly the xor function
// see http://www.doc.ic.ac.uk/~dfg/ProbabilisticInference/IDAPILecture18.pdf for more infos
var svm = new C_SVC(xor_problem, KernelHelper.PolynomialKernel(2, 1, 1), 1);
var file_name = System.IO.Path.Combine(base_path, "test_export_temp.xml");
// make sure directory is clean
if (File.Exists(file_name))
File.Delete(file_name);
svm.Export(file_name);
Assert.IsTrue(File.Exists(file_name));
var new_svm = new C_SVC(file_name);
checkXOR(new_svm);
File.Delete(file_name); // cleanup
}