public static void Main()
{
// STEP 4: Read the data
string dataFilePath = HttpContext.Current.Server.MapPath("~/DAL/svm/");
var dataTable = DataTable.New.ReadCsv(dataFilePath + "Data.csv");
List <string> x = dataTable.Rows.Select(row => row["Text"]).ToList();
double[] y = dataTable.Rows.Select(row => double.Parse(row["Category"]))
.ToArray();
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;
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(dataFilePath + "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));
}
public static string Predict(string userInput)
{
var newX = TextClassificationProblemBuilder.CreateNode(userInput, vocabulary);
var predictedY = model.Predict(newX);
Console.WriteLine("The prediction is {0}", _predictionDictionary[(int)predictedY]);
return(_predictionDictionary[(int)predictedY]);
}
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("");
}
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("");
}
