public static ACRF makeFactorial (Pipe p, int numLevels) { ArrayList t = new ArrayList (); for (int i = 0; i < numLevels; i++) { t.add (new BigramTemplate (i)); if (i+1 < numLevels) t.add (new PairwiseFactorTemplate (i, i+1)); } Template[] tmpls = (Template[]) t.toArray (new Template [t.size()]); return new ACRF (p, tmpls); }
new ACRF.BigramTemplate (0), new ACRF.BigramTemplate (1), new ACRF.PairwiseFactorTemplate (0,1), new CrossTemplate1 (0,1)
new ACRF.BigramTemplate (0), new ACRF.BigramTemplate (1), new ACRF.PairwiseFactorTemplate (0,1), new CrossTemplate1 (0,1)
new ACRF.BigramTemplate (0), new ACRF.BigramTemplate (1), new ACRF.PairwiseFactorTemplate (0,1), new CrossTemplate1 (0,1)
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); }
public static ACRF makeFactorial (Pipe p, int numLevels) { ArrayList t = new ArrayList (); for (int i = 0; i < numLevels; i++) { t.add (new BigramTemplate (i)); if (i+1 < numLevels) t.add (new PairwiseFactorTemplate (i, i+1)); } Template[] tmpls = (Template[]) t.toArray (new Template [t.size()]); return new ACRF (p, tmpls); }
public static ACRF makeFactorial (Pipe p, int numLevels) { ArrayList t = new ArrayList (); for (int i = 0; i < numLevels; i++) { t.add (new BigramTemplate (i)); if (i+1 < numLevels) t.add (new PairwiseFactorTemplate (i, i+1)); } Template[] tmpls = (Template[]) t.toArray (new Template [t.size()]); return new ACRF (p, tmpls); }