Exemple #1
0
  public boolean predict(FeatureVector featureVector, SingleDecision decision)
      throws MaltChainedException {
    if (featureVector == null) {
      throw new LibException(
          "The learner cannot predict the next class, because the feature vector cannot be found. ");
    }

    final FeatureList featureList = new FeatureList();
    final int size = featureVector.size();
    for (int i = 1; i <= size; i++) {
      final FeatureValue featureValue = featureVector.getFeatureValue(i - 1);
      if (featureValue != null && !(excludeNullValues == true && featureValue.isNullValue())) {
        if (featureValue instanceof SingleFeatureValue) {
          SingleFeatureValue singleFeatureValue = (SingleFeatureValue) featureValue;
          int index = featureMap.getIndex(i, singleFeatureValue.getIndexCode());
          if (index != -1 && singleFeatureValue.getValue() != 0) {
            featureList.add(index, singleFeatureValue.getValue());
          }
        } else if (featureValue instanceof MultipleFeatureValue) {
          for (Integer value : ((MultipleFeatureValue) featureValue).getCodes()) {
            int v = featureMap.getIndex(i, value);
            if (v != -1) {
              featureList.add(v, 1);
            }
          }
        }
      }
    }
    try {
      decision.getKBestList().addList(model.predict(featureList.toArray()));
      //			decision.getKBestList().addList(prediction(featureList));
    } catch (OutOfMemoryError e) {
      throw new LibException("Out of memory. Please increase the Java heap size (-Xmx<size>). ", e);
    }
    return true;
  }
Exemple #2
0
 public int featureIndex(FeatureIdentifier fId) {
   return featureMap.getIndex(fId);
 }