public static NS.Tuple <Machine, Solver, LibSvmModel> train(Problem prob, Parameters parameters)
{
double[] w;
double Cp = parameters.Complexity;
double Cn = parameters.Complexity;
if (parameters.ClassWeights != null)
{
for (int i = 0; i < parameters.ClassLabels.Count; i++)
{
if (parameters.ClassLabels[i] == -1)
{
Cn *= parameters.ClassWeights[i];
}
else if (parameters.ClassLabels[i] == +1)
{
Cn *= parameters.ClassWeights[i];
}
}
}
NS.Tuple <Machine, Solver> result = train_one(prob, parameters, out w, Cp, Cn);
return(NS.Tuple.Create(result.Item1, result.Item2, new LibSvmModel()
{
Dimension = prob.Dimensions,
Classes = 2,
Labels = new[] { +1, -1 },
Solver = parameters.Solver,
Weights = w,
Bias = 0
}));
}
public void Main(params string[] args)
{
#if NETSTANDARD1_4 || NETCORE
CultureInfo.DefaultThreadCurrentCulture = CultureInfo.InvariantCulture;
CultureInfo.DefaultThreadCurrentUICulture = CultureInfo.InvariantCulture;
#else
Thread.CurrentThread.CurrentCulture = CultureInfo.InvariantCulture;
#endif
if (args.Length == 0)
{
return;
}
if (args[0] == "--self")
{
ExecuteTestAndExit(args);
}
string input_file_name;
string model_file_name;
// The parameters object contains information about which parameters we passed to
// the command line and that should be used to control the training of the machine,
// such as which solver to use, the model complexity C, etc.
this.Parameters = parse_command_line(args, out input_file_name, out model_file_name);
// The problem object contains the input and output data
this.Problem = read_problem(input_file_name, Parameters.Bias);
this.ErrorMessage = check_parameter(Problem, Parameters);
if (!String.IsNullOrEmpty(ErrorMessage))
{
Console.WriteLine("ERROR:" + ErrorMessage);
return;
}
// Learn the specified problem and register steps and obtained results
NS.Tuple <Machine, Solver, LibSvmModel> result = train(Problem, Parameters);
this.Machine = result.Item1; // The SVM actually created by Accord.NET
this.Solver = result.Item2; // The Accord.NET learning algorithm used.
this.Model = result.Item3; // LIBLINEAR's definition of what a SVM is
try
{
// Save the model to disk
Model.Save(model_file_name);
}
catch (Exception ex)
{
this.ErrorMessage = "can't save model to file " + model_file_name;
Console.WriteLine(this.ErrorMessage);
Console.WriteLine(ex.Message);
}
}
/// <summary>
/// Reads a problem specified in LibSVM's sparse format.
/// </summary>
///
public static Problem read_problem(string filename, double bias)
{
// Create a LibSVM's sparse data reader
var reader = new SparseReader(filename);
if (bias > 0)
{
reader.Intercept = bias;
}
NS.Tuple <Sparse <double>[], double[]> r = reader.ReadSparseToEnd();
Sparse <double>[] x = r.Item1;
double[] y = r.Item2;
return(new Problem()
{
Dimensions = reader.NumberOfInputs,
Inputs = x,
Outputs = y,
});
}