//[Fact]
public void Test_SVM_Email_Classification()
{
var training_Data = System.IO.File.ReadAllLines("Data\\Emails\\Training_Data.txt")
.Select(s => s.Split(new char[] { ' ' }, StringSplitOptions.RemoveEmptyEntries).ToVector(f => double.Parse(f.Trim()))).ToMatrix();
var training_Labels = System.IO.File.ReadAllLines("Data\\Emails\\Training_Labels.txt")
.Select(s => double.Parse(s.Trim())).ToVector();
var test_Data = System.IO.File.ReadAllLines("Data\\Emails\\Test_Data.txt")
.Select(s => s.Split(new char[] { ' ' }, StringSplitOptions.RemoveEmptyEntries).ToVector(f => double.Parse(f.Trim()))).ToMatrix();
var test_Labels = System.IO.File.ReadAllLines("Data\\Emails\\Test_Labels.txt")
.Select(s => double.Parse(s.Trim())).ToVector();
var generator = new Supervised.SVM.SVMGenerator()
{
C = 0.1,
MaxIterations = 5,
SelectionFunction = new Supervised.SVM.Selection.RandomSetSelection()
};
var model = generator.Generate(training_Data, training_Labels);
Vector predictions = new Vector(test_Labels.Count());
for (int x = 0; x < test_Labels.Count(); x++)
{
predictions[x] = model.Predict(test_Data[x]);
}
var score = numl.Supervised.Score.ScorePredictions(predictions, test_Labels);
Console.WriteLine($"SVM Model\n: { score }");
}
//[Fact]
public void Test_SVM_Email_Classification()
{
var training_Data = System.IO.File.ReadAllLines("Data\\Emails\\Training_Data.txt")
.Select(s => s.Split(new char[] { ' ' }, StringSplitOptions.RemoveEmptyEntries).ToVector(f => double.Parse(f.Trim()))).ToMatrix();
var training_Labels = System.IO.File.ReadAllLines("Data\\Emails\\Training_Labels.txt")
.Select(s => double.Parse(s.Trim())).ToVector();
var test_Data = System.IO.File.ReadAllLines("Data\\Emails\\Test_Data.txt")
.Select(s => s.Split(new char[] { ' ' }, StringSplitOptions.RemoveEmptyEntries).ToVector(f => double.Parse(f.Trim()))).ToMatrix();
var test_Labels = System.IO.File.ReadAllLines("Data\\Emails\\Test_Labels.txt")
.Select(s => double.Parse(s.Trim())).ToVector();
var generator = new Supervised.SVM.SVMGenerator()
{
C = 0.1,
MaxIterations = 5,
SelectionFunction = new Supervised.SVM.Selection.RandomSetSelection()
};
var model = generator.Generate(training_Data, training_Labels);
Vector predictions = new Vector(test_Labels.Count());
for (int x = 0; x < test_Labels.Count(); x++)
{
predictions[x] = model.Predict(test_Data[x]);
}
var score = numl.Supervised.Score.ScorePredictions(predictions, test_Labels);
Console.WriteLine($"SVM Model\n: { score }");
}
