package opennlp.maxent;import gnu.trove.TIntArrayList;import gnu.trove.TLinkedList;import gnu.trove.TObjectIntHashMap;import java.util.ArrayList;import java.util.Arrays;import java.util.List;/** * An indexer for maxent model data which handles cutoffs for uncommon * contextual predicates and provides a unique integer index for each of the * predicates and maintains event values.   * @author Tom Morton */public class OnePassRealValueDataIndexer extends OnePassDataIndexer {  float[][] values;    /**   * Two argument constructor for DataIndexer.   * @param eventStream An Event[] which contains the a list of all the Events   *               seen in the training data.   * @param cutoff The minimum number of times a predicate must have been   *               observed in order to be included in the model.   */  public OnePassRealValueDataIndexer(EventStream eventStream, int cutoff) {    super(eventStream,cutoff);  }    public float[][] getValues() {    return values;  }  protected int sortAndMerge(List eventsToCompare) {    int numUniqueEvents = super.sortAndMerge(eventsToCompare);    values = new float[numUniqueEvents][];    int numEvents = eventsToCompare.size();    for (int i = 0, j = 0; i < numEvents; i++) {      ComparableEvent evt = (ComparableEvent) eventsToCompare.get(i);      if (null == evt) {        continue; // this was a dupe, skip over it.      }      values[j++] = evt.values;    }    return numUniqueEvents;  }    protected List index(TLinkedList events,      TObjectIntHashMap predicateIndex) {    TObjectIntHashMap omap = new TObjectIntHashMap();        int numEvents = events.size();    int outcomeCount = 0;    List eventsToCompare = new ArrayList(numEvents);    TIntArrayList indexedContext = new TIntArrayList();        for (int eventIndex=0; eventIndex<numEvents; eventIndex++) {      Event ev = (Event)events.removeFirst();      String[] econtext = ev.getContext();      ComparableEvent ce;            int ocID;      String oc = ev.getOutcome();            if (omap.containsKey(oc)) {        ocID = omap.get(oc);      } else {        ocID = outcomeCount++;        omap.put(oc, ocID);      }            for (int i=0; i<econtext.length; i++) {        String pred = econtext[i];        if (predicateIndex.containsKey(pred)) {          indexedContext.add(predicateIndex.get(pred));        }      }      //    drop events with no active features      if (indexedContext.size() > 0) {        ce = new ComparableEvent(ocID, indexedContext.toNativeArray(), ev.getValues());        eventsToCompare.add(ce);      }      else {        System.err.println("Dropped event "+ev.getOutcome()+":"+Arrays.asList(ev.getContext()));      }//    recycle the TIntArrayList      indexedContext.resetQuick();    }    outcomeLabels = toIndexedStringArray(omap);    predLabels = toIndexedStringArray(predicateIndex);    return eventsToCompare;  }}