// startIndex to compute, number of events to compute
internal ModelExpactationComputeTask(GISTrainer outerInstance, int threadIndex, int startIndex, int length)
{
this.outerInstance = outerInstance;
this.startIndex = startIndex;
this.length = length;
this.threadIndex = threadIndex;
}
/// <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="iterations">
/// The number of GIS iterations to perform. </param>
/// <param name="indexer">
/// The object which will be used for event compilation. </param>
/// <param name="printMessagesWhileTraining">
/// Determines whether training status messages are written to STDOUT. </param>
/// <param name="smoothing">
/// Defines whether the created trainer will use smoothing while
/// training the model. </param>
/// <param name="modelPrior">
/// The prior distribution for the model. </param>
/// <param name="cutoff">
/// The number of times a predicate must occur to be used in a 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(int iterations, DataIndexer indexer, bool printMessagesWhileTraining,
bool smoothing, Prior modelPrior, int cutoff, int threads)
{
GISTrainer trainer = new GISTrainer(printMessagesWhileTraining);
trainer.Smoothing = smoothing;
trainer.SmoothingObservation = SMOOTHING_OBSERVATION;
if (modelPrior == null)
{
modelPrior = new UniformPrior();
}
return(trainer.trainModel(iterations, indexer, modelPrior, cutoff, threads));
}
