/// <summary>
/// Main method. Call as follows:
/// <para>
/// java ModelTrainer dataFile modelFile
/// </para>
/// </summary>
public static void Main(string[] args)
{
int ai = 0;
bool real = false;
string type = "maxent";
int maxit = 100;
int cutoff = 1;
double sigma = 1.0;
if (args.Length == 0)
{
usage();
}
while (args[ai].StartsWith("-", StringComparison.Ordinal))
{
if (args[ai].Equals("-real"))
{
real = true;
}
else if (args[ai].Equals("-perceptron"))
{
type = "perceptron";
}
else if (args[ai].Equals("-maxit"))
{
maxit = Convert.ToInt32(args[++ai]);
}
else if (args[ai].Equals("-cutoff"))
{
cutoff = Convert.ToInt32(args[++ai]);
}
else if (args[ai].Equals("-sigma"))
{
sigma = Convert.ToDouble(args[++ai]);
}
else
{
Console.Error.WriteLine("Unknown option: " + args[ai]);
usage();
}
ai++;
}
string dataFileName = args[ai++];
string modelFileName = args[ai];
try
{
FileReader datafr = new FileReader(new Jfile(dataFileName));
EventStream es;
if (!real)
{
es = new BasicEventStream(new PlainTextByLineDataStream(datafr), ",");
}
else
{
es = new RealBasicEventStream(new PlainTextByLineDataStream(datafr));
}
Jfile outputFile = new Jfile(modelFileName);
AbstractModelWriter writer;
AbstractModel model;
if (type.Equals("maxent"))
{
GIS.SMOOTHING_OBSERVATION = SMOOTHING_OBSERVATION;
if (!real)
{
model = GIS.trainModel(es, maxit, cutoff, sigma);
}
else
{
model = GIS.trainModel(maxit, new OnePassRealValueDataIndexer(es, cutoff), USE_SMOOTHING);
}
writer = new SuffixSensitiveGISModelWriter(model, outputFile);
}
else if (type.Equals("perceptron"))
{
//System.err.println("Perceptron training");
model = (new PerceptronTrainer()).trainModel(maxit, new OnePassDataIndexer(es, cutoff), cutoff);
writer = new SuffixSensitivePerceptronModelWriter(model, outputFile);
}
else
{
throw new Exception("Unknown model type: " + type);
}
writer.persist();
}
catch (Exception e)
{
Console.Write("Unable to create model due to exception: ");
Console.WriteLine(e);
Console.WriteLine(e.ToString());
Console.Write(e.StackTrace);
}
}
public static MaxentModel train(EventStream events, int cutoff)
{
return(GIS.trainModel(events, 100, cutoff));
}