public Alphabet getInputAlphabet () { return crf.getInputAlphabet (); }
public Alphabet getInputAlphabet () { return crf.getInputAlphabet (); }
public Alphabet getInputAlphabet () { return crf.getInputAlphabet (); }
public void trainFor(Collection<Alignment> inputs) { // this pipe is the default pipe with new alphabet Stopwatch watch = Stopwatch.createStarted(); trainRound(inputs, new Alphabet(), 0); crf.getInputAlphabet().stopGrowth(); crf.getOutputAlphabet().stopGrowth(); watch.stop(); log.info("Training took " + watch); }
private void setupClassifier(String trainingdata) { try { crf_input = new ObjectInputStream(ResourceUtils.loadResource( trainingdata, this.getClass())); crf = (CRF) crf_input.readObject(); crf_input.close(); } catch (FileNotFoundException e1) { e1.printStackTrace(); } catch (IOException e1) { e1.printStackTrace(); } catch (ClassNotFoundException e) { e.printStackTrace(); } crf.getInputAlphabet().stopGrowth(); crf.getOutputAlphabet().stopGrowth(); crf_pipe = crf.getInputPipe(); crf_pipe.setTargetProcessing(false); crf_estimator = new ViterbiConfidenceEstimator(crf); }
private void setupClassifier(String trainingdata) { try { crf_input = new ObjectInputStream(ResourceUtils.loadResource( trainingdata, this.getClass())); crf = (CRF) crf_input.readObject(); crf_input.close(); } catch (FileNotFoundException e1) { e1.printStackTrace(); } catch (IOException e1) { e1.printStackTrace(); } catch (ClassNotFoundException e) { e.printStackTrace(); } crf.getInputAlphabet().stopGrowth(); crf.getOutputAlphabet().stopGrowth(); crf_pipe = crf.getInputPipe(); crf_pipe.setTargetProcessing(false); crf_estimator = new ViterbiConfidenceEstimator(crf); }
private TransducerTrainer trainOnce(Pipe pipe, InstanceList trainData) { Stopwatch watch = Stopwatch.createStarted(); CRF crf = new CRF(pipe, null); crf.addOrderNStates(trainData, new int[]{1}, null, null, null, null, false); crf.addStartState(); log.info("Starting alignTag training..."); CRFTrainerByThreadedLabelLikelihood trainer = new CRFTrainerByThreadedLabelLikelihood(crf, 8); trainer.setGaussianPriorVariance(2); // trainer.setUseSomeUnsupportedTrick(false); trainer.train(trainData); trainer.shutdown(); watch.stop(); log.info("Align Tag CRF Training took " + watch.toString()); crf.getInputAlphabet().stopGrowth(); crf.getOutputAlphabet().stopGrowth(); return trainer; }
private TransducerTrainer trainOnce(Pipe pipe, InstanceList examples) { Stopwatch watch = Stopwatch.createStarted(); CRF crf = new CRF(pipe, null); crf.addOrderNStates(examples, new int[]{1}, null, null, null, null, false); crf.addStartState(); // crf.setWeightsDimensionAsIn(examples, false); log.info("Starting syllchain training..."); CRFTrainerByThreadedLabelLikelihood trainer = new CRFTrainerByThreadedLabelLikelihood(crf, 8); trainer.setGaussianPriorVariance(2); // trainer.setUseSomeUnsupportedTrick(false); // trainer.setAddNoFactors(true); trainer.train(examples); trainer.shutdown(); watch.stop(); log.info("SyllChain CRF Training took " + watch.toString()); crf.getInputAlphabet().stopGrowth(); crf.getOutputAlphabet().stopGrowth(); return trainer; }
new FeatureVector(crf.getInputAlphabet(), new int[] { 1, 2, 3 }), new FeatureVector(crf.getInputAlphabet(), new int[] { 1, 2, 3 }), new FeatureVector(crf.getInputAlphabet(), new int[] { 1, 2, 3 }), new FeatureVector(crf.getInputAlphabet(), new int[] { 1, 2, 3 }), }); FeatureSequence ss = new FeatureSequence(crf.getOutputAlphabet(),
private TransducerTrainer trainOnce(Pipe pipe, InstanceList examples) { Stopwatch watch = Stopwatch.createStarted(); CRF crf = new CRF(pipe, null); crf.addOrderNStates(examples, new int[]{1}, null, null, null, null, false); crf.addStartState(); crf.setWeightsDimensionAsIn(examples, true); if (initFrom != null) { crf.initializeApplicableParametersFrom(initFrom); } log.info("Starting syllchain training..."); CRFTrainerByThreadedLabelLikelihood trainer = new CRFTrainerByThreadedLabelLikelihood(crf, 8); trainer.setGaussianPriorVariance(2); trainer.setAddNoFactors(true); // trainer.setUseSomeUnsupportedTrick(true); trainer.train(examples); trainer.shutdown(); watch.stop(); log.info("SyllChain CRF Training took " + watch.toString()); crf.getInputAlphabet().stopGrowth(); crf.getOutputAlphabet().stopGrowth(); return trainer; }
private TransducerTrainer trainOnce(Pipe pipe, InstanceList trainData) { Stopwatch watch = Stopwatch.createStarted(); CRF crf = new CRF(pipe, null); crf.addOrderNStates(trainData, new int[]{1}, null, null, null, null, false); crf.addStartState(); crf.setWeightsDimensionAsIn(trainData, false); if (initFrom != null) { crf.initializeApplicableParametersFrom(initFrom); } log.info("Starting alignTag training..."); CRFTrainerByThreadedLabelLikelihood trainer = new CRFTrainerByThreadedLabelLikelihood(crf, 8); trainer.setGaussianPriorVariance(2); trainer.setAddNoFactors(true); trainer.setUseSomeUnsupportedTrick(false); trainer.train(trainData); trainer.shutdown(); watch.stop(); log.info("Syll align Tag CRF Training took " + watch.toString()); crf.getInputAlphabet().stopGrowth(); crf.getOutputAlphabet().stopGrowth(); return trainer; }
new FeatureVector(crf.getInputAlphabet(), new int[] { 1, 2, 3 }), new FeatureVector(crf.getInputAlphabet(), new int[] { 1, 2, 3 }), new FeatureVector(crf.getInputAlphabet(), new int[] { 1, 2, 3 }), new FeatureVector(crf.getInputAlphabet(), new int[] { 1, 2, 3 }), }); FeatureSequence ss = new FeatureSequence(crf.getOutputAlphabet(),
int numFeatures = crf.getInputAlphabet().size(); SparseVector w = new SparseVector(new double[numFeatures]); w.setAll(Double.NEGATIVE_INFINITY);
int numFeatures = crf.getInputAlphabet().size(); SparseVector w = new SparseVector(new double[numFeatures]); w.setAll(Double.NEGATIVE_INFINITY);
new FeatureVector((Alphabet) crf.getInputAlphabet(), new double[] { 1 }), new FeatureVector((Alphabet) crf.getInputAlphabet(), new double[] { 1 }), new FeatureVector((Alphabet) crf.getInputAlphabet(), new double[] { 1 }), });
new FeatureVector((Alphabet) crf.getInputAlphabet(), new double[] { 1 }), new FeatureVector((Alphabet) crf.getInputAlphabet(), new double[] { 1 }), new FeatureVector((Alphabet) crf.getInputAlphabet(), new double[] { 1 }), });
new FeatureVector((Alphabet) crf.getInputAlphabet(), new double[] { 1 }), new FeatureVector((Alphabet) crf.getInputAlphabet(), new double[] { 1 }), new FeatureVector((Alphabet) crf.getInputAlphabet(), new double[] { 1 }), });
new FeatureVector((Alphabet) crf.getInputAlphabet(), new double[] { 1 }), new FeatureVector((Alphabet) crf.getInputAlphabet(), new double[] { 1 }), new FeatureVector((Alphabet) crf.getInputAlphabet(), new double[] { 1 }), });