/** * Trains a GIS model on the event in the specified event stream, using the specified number * of iterations and the specified count cutoff. * @param eventStream A stream of all events. * @param iterations The number of iterations to use for GIS. * @param cutoff The number of times a feature must occur to be included. * @return A GIS model trained with specified */ public GISModel trainModel(EventStream eventStream, int iterations, int cutoff) throws IOException { return trainModel(iterations, new OnePassDataIndexer(eventStream,cutoff),cutoff); }
/** * Trains a GIS model on the event in the specified event stream, using the specified number * of iterations and the specified count cutoff. * @param eventStream A stream of all events. * @param iterations The number of iterations to use for GIS. * @param cutoff The number of times a feature must occur to be included. * @return A GIS model trained with specified */ public GISModel trainModel(EventStream eventStream, int iterations, int cutoff) throws IOException { return trainModel(iterations, new OnePassDataIndexer(eventStream,cutoff),cutoff); }
/** * Trains a GIS model on the event in the specified event stream, using the * specified number of iterations and the specified count cutoff. * * @param eventStream * A stream of all events. * @param iterations * The number of iterations to use for GIS. * @param cutoff * The number of times a feature must occur to be included. * @return A GIS model trained with specified */ public GISModel trainModel(EventStream eventStream, int iterations, int cutoff) throws IOException { return trainModel(iterations, new OnePassDataIndexer(eventStream, cutoff), cutoff); }
model = new PerceptronTrainer().trainModel(maxit, new OnePassDataIndexer(es, cutoff), cutoff);
model = new PerceptronTrainer().trainModel(maxit, new OnePassDataIndexer(es, cutoff), cutoff);
indexer = new OnePassDataIndexer(hses, cutoff, sortAndMerge);
indexer = new OnePassDataIndexer(hses, cutoff, sortAndMerge);
public AbstractModel trainModel(int iterations, SequenceStream sequenceStream, int cutoff, boolean useAverage) throws IOException { this.iterations = iterations; this.sequenceStream = sequenceStream; DataIndexer di = new OnePassDataIndexer(new SequenceStreamEventStream(sequenceStream),cutoff,false); numSequences = 0; for (Sequence s : sequenceStream) {
public AbstractModel trainModel(int iterations, SequenceStream sequenceStream, int cutoff, boolean useAverage) throws IOException { this.iterations = iterations; this.sequenceStream = sequenceStream; DataIndexer di = new OnePassDataIndexer(new SequenceStreamEventStream(sequenceStream),cutoff,false); numSequences = 0; for (Sequence s : sequenceStream) {