public static void Run_Classify()
{
List <CDataRecord> records = LoadSample();
DataTransformer <CDataRecord> dt = new DataTransformer <CDataRecord>();
dt.DoFeaturesScaling(records);
KernelSVM <CDataRecord> algorithm = new KernelSVM <CDataRecord>();
algorithm.C = 20;
((GaussianKernel)algorithm.Kernel).Sigma = 0.01;
algorithm.Train(records);
Console.WriteLine("SVM(Gaussian Kernel) Model Built!");
for (int i = 0; i < records.Count; i++)
{
CDataRecord rec = records[i] as CDataRecord;
Console.WriteLine("rec: ");
for (int j = 0; j < rec.Dimension; ++j)
{
Console.WriteLine("X[" + j + "] = " + "[" + rec[j] + "] ");
}
Console.WriteLine("Label: " + "[" + rec.Label + "] ");
Console.WriteLine("Predicted Label: " + algorithm.Predict(records[i]));
}
}
public static void Run_Rank()
{
List <CDataRecord> records = LoadSample();
DataTransformer <CDataRecord> dt = new DataTransformer <CDataRecord>();
dt.DoFeaturesScaling(records);
KernelSVM <CDataRecord> algorithm = new KernelSVM <CDataRecord>();
algorithm.C = 20; //large value, high bias
((GaussianKernel)algorithm.Kernel).Sigma = 0.01; //low value, high bias
algorithm.Train(records);
Console.WriteLine("SVM (Gaussian Kernel) Model Built!");
for (int i = 0; i < records.Count; i++)
{
CDataRecord rec = records[i] as CDataRecord;
Console.WriteLine("rec: ");
for (int j = 0; j < rec.Dimension; ++j)
{
Console.WriteLine("X[" + j + " = " + rec[j] + "]");
}
Console.WriteLine("Label: " + rec.Label);
List <KeyValuePair <string, double> > ranks = algorithm.Rank(records, rec);
for (int k = 0; k < ranks.Count; ++k)
{
Console.WriteLine("{0}: score = {1}", ranks[k].Key, ranks[k].Value);
}
}
}
public static Glm FitModel(GlmSolverFactory solverFactory, List <RDataRecord> records)
{
DataTransformer dt = new DataTransformer();
dt.DoFeaturesScaling(records);
Glm solver = solverFactory.CreateSolver(records);
solver.Solve();
return(solver);
}