public void TestMaxentOnPrepAttachData2Threads() {
var trainer = new GISTrainer();
var di = new TwoPassDataIndexer(PrepAttachDataUtility.CreateTrainingStream(), 1);
var model = trainer.TrainModel(100, di, new UniformPrior(), 1, 2);
PrepAttachDataUtility.TestModel(model, 0.7997028967566229d);
}
/// <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="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>
/// <param name="threads">The number of threads to use during the training.</param>
/// <param name="monitor">
/// A evaluation monitor that can be used to listen the messages during the training or it can cancel the training operation.
/// This argument can be a <c>null</c> value.
/// </param>
/// <returns>The newly trained model, which can be used immediately or saved to disk using a <see cref="GISModelWriter"/> object.</returns>
public static GISModel TrainModel(int iterations, IDataIndexer indexer, bool smoothing, IPrior modelPrior, int cutoff, int threads, Monitor monitor)
{
var trainer = new GISTrainer(monitor)
{
Smoothing = smoothing,
SmoothingObservation = SmoothingObservation
};
if (modelPrior == null)
{
modelPrior = new UniformPrior();
}
return(trainer.TrainModel(iterations, indexer, modelPrior, cutoff, threads));
}
public void TestMaxentOnPrepAttachData() {
var model = new GISTrainer().TrainModel(100,
new TwoPassDataIndexer(PrepAttachDataUtility.CreateTrainingStream(), 1), 1);
PrepAttachDataUtility.TestModel(model, 0.7997028967566229d);
}
/// <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="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>
/// <param name="threads">The number of threads to use during the training.</param>
/// <param name="monitor">
/// A evaluation monitor that can be used to listen the messages during the training or it can cancel the training operation.
/// This argument can be a <c>null</c> value.
/// </param>
/// <returns>The newly trained model, which can be used immediately or saved to disk using a <see cref="GISModelWriter"/> object.</returns>
public static GISModel TrainModel(int iterations, IDataIndexer indexer, bool smoothing, IPrior modelPrior, int cutoff, int threads, Monitor monitor) {
var trainer = new GISTrainer(monitor) {
Smoothing = smoothing,
SmoothingObservation = SmoothingObservation
};
if (modelPrior == null) {
modelPrior = new UniformPrior();
}
return trainer.TrainModel(iterations, indexer, modelPrior, cutoff, threads);
}
