private static CRFTrainerByLabelLikelihood makeNewTrainerSingleThreaded(CRF crf) { CRFTrainerByLabelLikelihood trainer = new CRFTrainerByLabelLikelihood(crf); trainer.setGaussianPriorVariance(2); // trainer.setUseHyperbolicPrior(true); trainer.setAddNoFactors(true); trainer.setUseSomeUnsupportedTrick(false); return trainer; }
trainer.setGaussianPriorVariance(10.0);
crf.addStatesForThreeQuarterLabelsConnectedAsIn (ilists[0]); CRFTrainerByLabelLikelihood crft = new CRFTrainerByLabelLikelihood (crf); crft.setGaussianPriorVariance (100.0);
crf.addStatesForThreeQuarterLabelsConnectedAsIn (ilists[0]); CRFTrainerByLabelLikelihood crft = new CRFTrainerByLabelLikelihood (crf); crft.setGaussianPriorVariance (100.0);
crf.addStatesForThreeQuarterLabelsConnectedAsIn (ilists[0]); CRFTrainerByLabelLikelihood crft = new CRFTrainerByLabelLikelihood (crf); crft.setGaussianPriorVariance (100.0);
crft.setHyperbolicPriorSharpness (hyperbolicSharpnessOption.value); } else { crft.setGaussianPriorVariance (gaussianVarianceOption.value);
crft.setHyperbolicPriorSharpness (hyperbolicSharpnessOption.value); } else { crft.setGaussianPriorVariance (gaussianVarianceOption.value);
crft.setHyperbolicPriorSharpness (hyperbolicSharpnessOption.value); } else { crft.setGaussianPriorVariance (gaussianVarianceOption.value);
CRFTrainerByLabelLikelihood trainer = new CRFTrainerByLabelLikelihood(crf); trainer.setAddNoFactors(true); trainer.setGaussianPriorVariance(gpv); trainer.train(trainingSet,supIterations);
CRFTrainerByLabelLikelihood trainer = new CRFTrainerByLabelLikelihood(crf); trainer.setAddNoFactors(true); trainer.setGaussianPriorVariance(gpv); trainer.train(trainingSet,supIterations);
CRFTrainerByLabelLikelihood trainer = new CRFTrainerByLabelLikelihood(crf); trainer.setAddNoFactors(true); trainer.setGaussianPriorVariance(gpv); trainer.train(trainingSet,supIterations);