private static ACRFTrainer createTrainer () { if (usePiecewiseTraining.value) { return new PiecewiseACRFTrainer(); } else if (usePwplTraining.value) { return new PwplACRFTrainer(); } else if (usePlTraining.value) { return new PseudolikelihoodACRFTrainer (); } else { return new DefaultAcrfTrainer (); } }
private static ACRFTrainer createTrainer () { if (usePiecewiseTraining.value) { return new PiecewiseACRFTrainer(); } else if (usePwplTraining.value) { return new PwplACRFTrainer(); } else if (usePlTraining.value) { return new PseudolikelihoodACRFTrainer (); } else { return new DefaultAcrfTrainer (); } }
private static ACRFTrainer createTrainer () { if (usePiecewiseTraining.value) { return new PiecewiseACRFTrainer(); } else if (usePwplTraining.value) { return new PwplACRFTrainer(); } else if (usePlTraining.value) { return new PseudolikelihoodACRFTrainer (); } else { return new DefaultAcrfTrainer (); } }
ACRFTrainer trainer = new DefaultAcrfTrainer (); trainer.train (acrf, training, null, testing, 99999);
ACRFTrainer trainer = new DefaultAcrfTrainer (); trainer.train (acrf, training, null, testing, 99999);
ACRFTrainer trainer = new DefaultAcrfTrainer (); trainer.train (acrf, training, null, testing, 99999);
public void train(Collection<Alignment> examples) { Pipe pipe = makePipe(); InstanceList instances = makeExamplesFromAligns(examples, pipe); ACRF.Template[] tmpls = new ACRF.Template[]{ new ACRF.BigramTemplate(0) // new ACRF.BigramTemplate (1), // new ACRF.PairwiseFactorTemplate (0,1), // new CrossTemplate1(0,1) }; ACRF acrf = new ACRF(pipe, tmpls); ACRFTrainer trainer = new DefaultAcrfTrainer(); acrf.setSupportedOnly(true); acrf.setGaussianPriorVariance(2.0); DefaultAcrfTrainer.LogEvaluator eval = new DefaultAcrfTrainer.LogEvaluator(); eval.setNumIterToSkip(2); trainer.train(acrf, instances, null, null, eval, 9999); }
public void train(Collection<Alignment> examples) { Pipe pipe = makePipe(); InstanceList instances = makeExamplesFromAligns(examples, pipe); ACRF.Template[] tmpls = new ACRF.Template[]{ new ACRF.BigramTemplate(0), new ACRF.BigramTemplate (1), new ACRF.PairwiseFactorTemplate (0,1), new CrossTemplate1(0,1) }; ACRF acrf = new ACRF(pipe, tmpls); ACRFTrainer trainer = new DefaultAcrfTrainer(); acrf.setSupportedOnly(true); acrf.setGaussianPriorVariance(2.0); DefaultAcrfTrainer.LogEvaluator eval = new DefaultAcrfTrainer.LogEvaluator(); eval.setNumIterToSkip(2); trainer.train(acrf, instances, null, null, eval, 9999); }