public boolean predict(FeatureVector featureVector, SingleDecision decision) throws MaltChainedException {
		if (model == null) {
			try {
				model = Linear.loadModel(new BufferedReader(getInstanceInputStreamReaderFromConfigFile(".mod")));
			} catch (IOException e) {
				throw new LiblinearException("The model cannot be loaded. ", e);
			}
		}
		if (model == null) { 
			throw new LiblinearException("The Liblinear learner cannot predict the next class, because the learning model cannot be found. ");
		} else if (featureVector == null) {
			throw new LiblinearException("The Liblinear learner cannot predict the next class, because the feature vector cannot be found. ");
		}
		if (featurePruning) {
			return predictWithFeaturePruning(featureVector, decision);
		}
		
//		if (cardinalities == null) {
//			if (getConfigFileEntry(".car") != null) {
//				cardinalities = loadCardinalities(getInstanceInputStreamReaderFromConfigFile(".car"));
//			} else {
//				cardinalities = getCardinalities(featureVector);
//			}
//		}

		//System.out.println("METHOD PREDICT CARDINALITIES SIZE" + cardinalities.length + " FEATURE VECTOR SIZE " +featureVector.size());
		if (xlist == null) {
			xlist = new ArrayList<FeatureNode>(featureVector.size()); 
		}


//		int offset = 1;
//		int i = 0;
//		for (FeatureFunction feature : featureVector) {
//			final FeatureValue featureValue = feature.getFeatureValue();
//			if (!(excludeNullValues == true && featureValue.isNullValue())) {
//				if (featureValue instanceof SingleFeatureValue) {
//					if (((SingleFeatureValue)featureValue).getCode() < cardinalities[i]) {
//						xlist.add(new FeatureNode(((SingleFeatureValue)featureValue).getCode() + offset, 1));
//					}
//				} else if (featureValue instanceof MultipleFeatureValue) {
//					for (Integer value : ((MultipleFeatureValue)featureValue).getCodes()) {
//						if (value < cardinalities[i]) {
//							xlist.add(new FeatureNode(value + offset, 1));
//						}
//					}
//				}
//			}
//			offset += cardinalities[i];
//			i++;
//		}
		FeatureNode[] xarray = new FeatureNode[xlist.size()];
		for (int k = 0; k < xlist.size(); k++) {
			xarray[k] = xlist.get(k);
		}

		if (decision.getKBestList().getK() == 1) {
			decision.getKBestList().add((int) Linear.predict(model, xarray));
		} else {
			liblinear_predict_with_kbestlist(model, xarray, decision.getKBestList());
		}
		
		xlist.clear();

		return true;
	}