public static void Predict(params string[] args)
{
int i = 0;
bool predictProbability = false;
// parse options
for (i = 0; i < args.Length; i++)
{
if (args[i][0] != '-')
{
break;
}
++i;
switch (args[i - 1][1])
{
case 'b':
predictProbability = int.Parse(args[i]) == 1;
break;
default:
throw new ArgumentException("Unknown option");
}
}
if (i >= args.Length)
{
throw new ArgumentException("No input, model and output files provided");
}
Problem problem = Problem.Read(args[i]);
Model model = Model.Read(args[i + 1]);
Predict(problem, args[i + 2], model, predictProbability);
}
public static void Main(string[] args)
{
Problem train = Problem.Read("a1a.train.txt");
Problem test = Problem.Read("a1a.test.txt");
//For this example (and indeed, many scenarios), the default
//parameters will suffice.
Parameter parameters = new Parameter();
double C;
double Gamma;
//This will do a grid optimization to find the best parameters
//and store them in C and Gamma, outputting the entire
//search to params.txt.
ParameterSelection.Grid(train, parameters, "params.txt", out C, out Gamma);
parameters.C = C;
parameters.Gamma = Gamma;
//Train the model using the optimal parameters.
Model model = Training.Train(train, parameters);
//Perform classification on the test data, putting the
//results in results.txt.
Prediction.Predict(test, "results.txt", model, false);
}
private static void parseCommandLine(string[] args, out Parameter parameters, out Problem problem, out bool crossValidation, out int nrfold, out string modelFilename)
{
int i;
parameters = new Parameter();
// default values
crossValidation = false;
nrfold = 0;
// parse options
for (i = 0; i < args.Length; i++)
{
if (args[i][0] != '-')
{
break;
}
++i;
switch (args[i - 1][1])
{
case 's':
parameters.SvmType = (SvmType)int.Parse(args[i]);
break;
case 't':
parameters.KernelType = (KernelType)int.Parse(args[i]);
break;
case 'd':
parameters.Degree = int.Parse(args[i]);
break;
case 'g':
parameters.Gamma = double.Parse(args[i]);
break;
case 'r':
parameters.Coefficient0 = double.Parse(args[i]);
break;
case 'n':
parameters.Nu = double.Parse(args[i]);
break;
case 'm':
parameters.CacheSize = double.Parse(args[i]);
break;
case 'c':
parameters.C = double.Parse(args[i]);
break;
case 'e':
parameters.EPS = double.Parse(args[i]);
break;
case 'p':
parameters.P = double.Parse(args[i]);
break;
case 'h':
parameters.Shrinking = int.Parse(args[i]) == 1;
break;
case 'b':
parameters.Probability = int.Parse(args[i]) == 1;
break;
case 'v':
crossValidation = true;
nrfold = int.Parse(args[i]);
if (nrfold < 2)
{
throw new ArgumentException("n-fold cross validation: n must >= 2");
}
break;
case 'w':
parameters.Weights[int.Parse(args[i - 1].Substring(2))] = double.Parse(args[1]);
break;
default:
throw new ArgumentException("Unknown Parameter");
}
}
// determine filenames
if (i >= args.Length)
{
throw new ArgumentException("No input file specified");
}
problem = Problem.Read(args[i]);
if (parameters.Gamma == 0)
{
parameters.Gamma = 1.0 / problem.MaxIndex;
}
if (i < args.Length - 1)
{
modelFilename = args[i + 1];
}
else
{
int p = args[i].LastIndexOf('/') + 1;
modelFilename = args[i].Substring(p) + ".model";
}
}
