public DataIndexer getDataIndexer(ObjectStream<Event> events) throws IOException { trainingParameters.put(AbstractDataIndexer.SORT_PARAM, isSortAndMerge()); // If the cutoff was set, don't overwrite the value. if (trainingParameters.getIntParameter(CUTOFF_PARAM, -1) == -1) { trainingParameters.put(CUTOFF_PARAM, 5); } DataIndexer indexer = DataIndexerFactory.getDataIndexer(trainingParameters, reportMap); indexer.index(events); return indexer; }
DataIndexer di = DataIndexerFactory.getDataIndexer(parameters, myReportMap); Assert.assertEquals("opennlp.tools.ml.model.OnePassDataIndexer", di.getClass().getName()); di.index(eventStream); di = DataIndexerFactory.getDataIndexer(parameters, myReportMap); Assert.assertEquals("opennlp.tools.ml.model.TwoPassDataIndexer", di.getClass().getName()); di.index(eventStream); di = DataIndexerFactory.getDataIndexer(parameters, myReportMap); Assert.assertEquals("opennlp.tools.ml.model.OnePassRealValueDataIndexer", di.getClass().getName()); di = DataIndexerFactory.getDataIndexer(parameters, myReportMap); Assert.assertEquals("opennlp.tools.ml.maxent.MockDataIndexer", di.getClass().getName());
public DataIndexer getDataIndexer(ObjectStream<Event> events) throws IOException { trainingParameters.put(AbstractDataIndexer.SORT_PARAM, isSortAndMerge()); // If the cutoff was set, don't overwrite the value. if (trainingParameters.getIntParameter(CUTOFF_PARAM, -1) == -1) { trainingParameters.put(CUTOFF_PARAM, 5); } DataIndexer indexer = DataIndexerFactory.getDataIndexer(trainingParameters, reportMap); indexer.index(events); return indexer; }
public DataIndexer getDataIndexer(ObjectStream<Event> events) throws IOException { trainingParameters.put(AbstractDataIndexer.SORT_PARAM, isSortAndMerge()); // If the cutoff was set, don't overwrite the value. if (trainingParameters.getIntParameter(CUTOFF_PARAM, -1) == -1) { trainingParameters.put(CUTOFF_PARAM, 5); } DataIndexer indexer = DataIndexerFactory.getDataIndexer(trainingParameters, reportMap); indexer.index(events); return indexer; }