/// <summary>
/// Train a model using the GIS algorithm.
/// </summary>
/// <param name="eventStream">
/// The EventStream holding the data on which this model will be
/// trained. </param>
/// <param name="iterations">
/// The number of GIS iterations to perform. </param>
/// <param name="cutoff">
/// The number of times a feature must be seen in order to be relevant
/// for training. </param>
/// <param name="smoothing">
/// Defines whether the created trainer will use smoothing while
/// training the model. </param>
/// <param name="printMessagesWhileTraining">
/// Determines whether training status messages are written to STDOUT. </param>
/// <returns> The newly trained model, which can be used immediately or saved to
/// disk using an opennlp.maxent.io.GISModelWriter object. </returns>
public static GISModel trainModel(EventStream eventStream, int iterations, int cutoff, bool smoothing,
bool printMessagesWhileTraining)
{
GISTrainer trainer = new GISTrainer(printMessagesWhileTraining);
trainer.Smoothing = smoothing;
trainer.SmoothingObservation = SMOOTHING_OBSERVATION;
return(trainer.trainModel(eventStream, iterations, cutoff));
}
/// <summary>
/// Train a model using the GIS algorithm.
/// </summary>
/// <param name="eventStream">
/// The EventStream holding the data on which this model will be
/// trained. </param>
/// <param name="iterations">
/// The number of GIS iterations to perform. </param>
/// <param name="cutoff">
/// The number of times a feature must be seen in order to be relevant
/// for training. </param>
/// <param name="sigma">
/// The standard deviation for the gaussian smoother. </param>
/// <returns> The newly trained model, which can be used immediately or saved to
/// disk using an opennlp.maxent.io.GISModelWriter object. </returns>
public static GISModel trainModel(EventStream eventStream, int iterations, int cutoff, double sigma)
{
GISTrainer trainer = new GISTrainer(PRINT_MESSAGES);
if (sigma > 0)
{
trainer.GaussianSigma = sigma;
}
return(trainer.trainModel(eventStream, iterations, cutoff));
}
/// <summary>
/// Train a model using the GIS algorithm.
/// </summary>
/// <param name="eventStream">
/// The EventStream holding the data on which this model will be
/// trained. </param>
/// <param name="iterations">
/// The number of GIS iterations to perform. </param>
/// <param name="cutoff">
/// The number of times a feature must be seen in order to be relevant
/// for training. </param>
/// <returns> The newly trained model, which can be used immediately or saved to
/// disk using an opennlp.maxent.io.GISModelWriter object. </returns>
public static GISModel trainModel(EventStream eventStream, int iterations, int cutoff)
{
return(trainModel(eventStream, iterations, cutoff, false, PRINT_MESSAGES));
}
/// <summary>
/// Train a model using the GIS algorithm, assuming 100 iterations and no
/// cutoff.
/// </summary>
/// <param name="eventStream">
/// The EventStream holding the data on which this model will be
/// trained. </param>
/// <param name="smoothing">
/// Defines whether the created trainer will use smoothing while
/// training the model. </param>
/// <returns> The newly trained model, which can be used immediately or saved to
/// disk using an opennlp.maxent.io.GISModelWriter object. </returns>
public static GISModel trainModel(EventStream eventStream, bool smoothing)
{
return(trainModel(eventStream, 100, 0, smoothing, PRINT_MESSAGES));
}
/// <summary>
/// Train a model using the GIS algorithm, assuming 100 iterations and no
/// cutoff.
/// </summary>
/// <param name="eventStream">
/// The EventStream holding the data on which this model will be
/// trained. </param>
/// <returns> The newly trained model, which can be used immediately or saved to
/// disk using an opennlp.maxent.io.GISModelWriter object. </returns>
public static GISModel trainModel(EventStream eventStream)
{
return(trainModel(eventStream, 100, 0, false, PRINT_MESSAGES));
}
