private double testMulticlassModel(int numberOfClasses, int count, SvmType svm, KernelType kernel, bool probability = false, string outputFile = null)
{
Problem train = SVMUtilities.CreateMulticlassProblem(numberOfClasses, count);
Parameter param = new Parameter();
RangeTransform transform = RangeTransform.Compute(train);
Problem scaled = transform.Scale(train);
param.Gamma = 1.0 / 3;
param.SvmType = svm;
param.KernelType = kernel;
param.Probability = probability;
if (svm == SvmType.C_SVC)
{
for (int i = 0; i < numberOfClasses; i++)
{
param.Weights[i] = 1;
}
}
Model model = Training.Train(scaled, param);
Problem test = SVMUtilities.CreateMulticlassProblem(numberOfClasses, count, false);
scaled = transform.Scale(test);
return(Prediction.Predict(scaled, outputFile, model, false));
}
public void ReadProblem()
{
Problem expected = SVMUtilities.CreateTwoClassProblem(100);
Problem actual = Problem.Read("train0.problem");
Assert.AreEqual(expected, actual);
}
public void WriteProblem()
{
Problem prob = SVMUtilities.CreateTwoClassProblem(100);
using (MemoryStream stream = new MemoryStream())
using (StreamReader input = new StreamReader("train0.problem"))
{
Problem.Write(stream, prob);
string expected = input.ReadToEnd().Replace("\r\n", "\n");
string actual = Encoding.ASCII.GetString(stream.ToArray());
Assert.AreEqual(expected, actual);
}
}
public void ReadModel()
{
Problem train = SVMUtilities.CreateTwoClassProblem(100);
Parameter param = new Parameter();
RangeTransform transform = RangeTransform.Compute(train);
Problem scaled = transform.Scale(train);
param.KernelType = KernelType.LINEAR;
Training.SetRandomSeed(SVMUtilities.TRAINING_SEED);
Model expected = Training.Train(scaled, param);
Model actual = Model.Read("svm0.model");
Assert.AreEqual(expected, actual);
}
private double testRegressionModel(int count, SvmType svm, KernelType kernel, string outputFile = null)
{
Problem train = SVMUtilities.CreateRegressionProblem(count);
Parameter param = new Parameter();
RangeTransform transform = RangeTransform.Compute(train);
Problem scaled = transform.Scale(train);
param.Gamma = 1.0 / 2;
param.SvmType = svm;
param.KernelType = kernel;
param.Degree = 2;
Model model = Training.Train(scaled, param);
Problem test = SVMUtilities.CreateRegressionProblem(count, false);
scaled = transform.Scale(test);
return(Prediction.Predict(scaled, outputFile, model, false));
}
public void WriteModel()
{
Problem train = SVMUtilities.CreateTwoClassProblem(100);
Parameter param = new Parameter();
RangeTransform transform = RangeTransform.Compute(train);
Problem scaled = transform.Scale(train);
param.KernelType = KernelType.LINEAR;
Training.SetRandomSeed(SVMUtilities.TRAINING_SEED);
Model model = Training.Train(scaled, param);
using (MemoryStream stream = new MemoryStream())
using (StreamReader input = new StreamReader("svm0.model"))
{
Model.Write(stream, model);
string expected = input.ReadToEnd().Replace("\r\n", "\n");
string actual = Encoding.ASCII.GetString(stream.ToArray());
Assert.AreEqual(expected, actual);
}
}
private double testTwoClassModel(int count, SvmType svm, KernelType kernel, bool probability = false, string outputFile = null)
{
Problem train = SVMUtilities.CreateTwoClassProblem(count);
Parameter param = new Parameter();
RangeTransform transform = RangeTransform.Compute(train);
Problem scaled = transform.Scale(train);
param.Gamma = .5;
param.SvmType = svm;
param.KernelType = kernel;
param.Probability = probability;
if (svm == SvmType.C_SVC)
{
param.Weights[-1] = 1;
param.Weights[1] = 1;
}
Model model = Training.Train(scaled, param);
Problem test = SVMUtilities.CreateTwoClassProblem(count, false);
scaled = transform.Scale(test);
return(Prediction.Predict(scaled, outputFile, model, false));
}
