import cc.mallet.optimize.Optimizable.ByGradientValue; //导入依赖的package包/类
/**
 * http://code.google.com/p/sequence-labeler/source/browse/trunk/src/edu/jhu/nlp/sequence/classify/CRFSequenceLearner.java?spec=svn6&r=6
 * @param crf
 * @param trainingInstances
 * @param testingInstances
 */
private void trainByValueGradients(CRF crf, InstanceList trainingInstances,
                                   InstanceList testingInstances) {

    CRFOptimizableByBatchLabelLikelihood batchOptLabel =
            new CRFOptimizableByBatchLabelLikelihood(crf, trainingInstances, 1);

    ThreadedOptimizable optLabel = new ThreadedOptimizable(
            batchOptLabel, trainingInstances, crf.getParameters().getNumFactors(),
            new CRFCacheStaleIndicator(crf));
    // crf trainer
    ByGradientValue[] opts =
            new ByGradientValue[]{optLabel};
    // by default, use L-BFGS as the optimizer
    CRFTrainerByValueGradients crfTrainer =
            new CRFTrainerByValueGradients(crf, opts);
    //crfTrainer.setMaxResets(0);
    // train till convergence
    crfTrainer.train(trainingInstances, 999);
    optLabel.shutdown();



    //zuofeng's code
    //save crf model
    //refer: /cc/mallet.fst.tests/TestCRF.java
    // Create a file to store the crf
    File f = new File("model.crf");
    //trainer.setUseSparseWeights(false);

    try {
        ObjectOutputStream oos = new ObjectOutputStream(
                new FileOutputStream(f));
        oos.writeObject(crf);
        oos.close();
    } catch (IOException e) {
        System.err.println("Exception writing file: " + e);
    }
    //end of zuofeng's code


    //		parameters

}