import cc.mallet.optimize.OptimizationException; //导入依赖的package包/类
public Transducer trainModel(InstanceList trainingData, String options) {

    TransducerTrainer trainer = createTrainer(trainingData, info, options);
    Parms parms = new Parms(options,"i:iterations:i","V:verbose:b");
    boolean verbose = (boolean)parms.getValueOrElse("verbose", false);
    int iters = (int) parms.getValueOrElse("iterations", 0);
    if(iters==0) iters = Integer.MAX_VALUE;
    try {
      trainer.train(trainingData, iters);
    } catch(OptimizationException ex) {
      System.err.println("Encountered an OptimizationException during training (CONTINUING!): "+ex.getMessage());
      ex.printStackTrace(System.err);
      System.err.println("We ignore this exception and try to use the model so far ...");
    }
    if(verbose) 
      trainer.getTransducer().print();
    Transducer td = trainer.getTransducer();
    return td;
  }
 

import cc.mallet.optimize.OptimizationException; //导入依赖的package包/类
/**
	 * Trains a CRF until convergence or specified number of iterations, whichever is earlier.
	 * <p>
	 * Also creates an optimizable CRF and an optmizer if required.
	 */
	public boolean train (InstanceList trainingSet, int numIterations) {
		if (numIterations <= 0)
			return false;
		assert (trainingSet.size() > 0);

		getOptimizableCRF(trainingSet); // This will set this.mcrf if necessary
		getOptimizer(trainingSet); // This will set this.opt if necessary

		int numResets = 0;
		boolean converged = false;
		logger.info ("CRF about to train with "+numIterations+" iterations");
		for (int i = 0; i < numIterations; i++) {
			try {
        // gsc: timing each iteration
        long startTime = System.currentTimeMillis();
				converged = opt.optimize (1);
				logger.info ("CRF finished one iteration of maximizer, i="+i+", "+
                     +(System.currentTimeMillis()-startTime)/1000 + " secs.");
				iterationCount++;
				runEvaluators();
			} catch (OptimizationException e) {
        		// gsc: resetting the optimizer for specified number of times
				e.printStackTrace();
				logger.info ("Catching exception.");
				if (numResets < maxResets) {
					// reset the optimizer and get a new one
					logger.info("Resetting optimizer.");
					++numResets;
					opt = null;
					getOptimizer(trainingSet);
//				logger.info ("Catching exception; saying converged.");
//				converged = true;
				} else {
					logger.info("Saying converged.");
					converged = true;
				}
			}
			if (converged) {
				logger.info ("CRF training has converged, i="+i);
				break;
			}
		}
		return converged;
	}