public void testSpaceViewer () throws FileNotFoundException { Pipe pipe = TestMEMM.makeSpacePredictionPipe (); String[] data0 = { TestCRF.data[0] }; String[] data1 = { TestCRF.data[1] }; InstanceList training = new InstanceList (pipe); training.addThruPipe (new ArrayIterator (data0)); InstanceList testing = new InstanceList (pipe); testing.addThruPipe (new ArrayIterator (data1)); CRF crf = new CRF (pipe, null); crf.addFullyConnectedStatesForLabels (); CRFTrainerByLabelLikelihood crft = new CRFTrainerByLabelLikelihood (crf); crft.trainIncremental (training); CRFExtractor extor = hackCrfExtor (crf); Extraction extration = extor.extract (new ArrayIterator (data1)); PrintStream out = new PrintStream (new FileOutputStream (htmlFile)); LatticeViewer.extraction2html (extration, extor, out); out.close(); out = new PrintStream (new FileOutputStream (latticeFile)); LatticeViewer.extraction2html (extration, extor, out, true); out.close(); }
public void testSpaceViewer () throws FileNotFoundException { Pipe pipe = TestMEMM.makeSpacePredictionPipe (); String[] data0 = { TestCRF.data[0] }; String[] data1 = { TestCRF.data[1] }; InstanceList training = new InstanceList (pipe); training.addThruPipe (new ArrayIterator (data0)); InstanceList testing = new InstanceList (pipe); testing.addThruPipe (new ArrayIterator (data1)); CRF crf = new CRF (pipe, null); crf.addFullyConnectedStatesForLabels (); CRFTrainerByLabelLikelihood crft = new CRFTrainerByLabelLikelihood (crf); crft.trainIncremental (training); CRFExtractor extor = hackCrfExtor (crf); Extraction extration = extor.extract (new ArrayIterator (data1)); PrintStream out = new PrintStream (new FileOutputStream (htmlFile)); LatticeViewer.extraction2html (extration, extor, out); out.close(); out = new PrintStream (new FileOutputStream (latticeFile)); LatticeViewer.extraction2html (extration, extor, out, true); out.close(); }
savedCRF = crf; System.out.println("Training serialized crf."); crft.trainIncremental(lists[0]); double preTrainAcc = crf.averageTokenAccuracy(lists[0]); double preTestAcc = crf.averageTokenAccuracy(lists[1]);
savedCRF = crf; System.out.println("Training serialized crf."); crft.trainIncremental(lists[0]); double preTrainAcc = crf.averageTokenAccuracy(lists[0]); double preTestAcc = crf.averageTokenAccuracy(lists[1]);
while (!crft.trainIncremental(ilists[0])) { eval.evaluate(crft); if (crft.getIteration() % 5 == 0)
while (!crft.trainIncremental(ilists[0])) { eval.evaluate(crft); if (crft.getIteration() % 5 == 0)
while (!crft.trainIncremental(ilists[0])) { eval.evaluate(crft); if (crft.getIteration() % 5 == 0)
+ crf.averageTokenAccuracy(lists[1])); System.out.println("Training..."); crft.trainIncremental(lists[0]); System.out.println("Training Accuracy after training = " + crf.averageTokenAccuracy(lists[0]));
public void testSpaceViewer () throws IOException { Pipe pipe = TestMEMM.makeSpacePredictionPipe (); String[] data0 = { TestCRF.data[0] }; String[] data1 = { TestCRF.data[1] }; InstanceList training = new InstanceList (pipe); training.addThruPipe (new ArrayIterator (data0)); InstanceList testing = new InstanceList (pipe); testing.addThruPipe (new ArrayIterator (data1)); CRF crf = new CRF (pipe, null); crf.addFullyConnectedStatesForLabels (); CRFTrainerByLabelLikelihood crft = new CRFTrainerByLabelLikelihood (crf); crft.trainIncremental (training); CRFExtractor extor = TestLatticeViewer.hackCrfExtor (crf); Extraction extraction = extor.extract (new ArrayIterator (data1)); if (!outputDir.exists ()) outputDir.mkdir (); DocumentViewer.writeExtraction (outputDir, extraction); }
+ crf.averageTokenAccuracy(lists[1])); System.out.println("Training..."); crft.trainIncremental(lists[0]); System.out.println("Training Accuracy after training = " + crf.averageTokenAccuracy(lists[0]));
public void testSpaceViewer () throws IOException { Pipe pipe = TestMEMM.makeSpacePredictionPipe (); String[] data0 = { TestCRF.data[0] }; String[] data1 = { TestCRF.data[1] }; InstanceList training = new InstanceList (pipe); training.addThruPipe (new ArrayIterator (data0)); InstanceList testing = new InstanceList (pipe); testing.addThruPipe (new ArrayIterator (data1)); CRF crf = new CRF (pipe, null); crf.addFullyConnectedStatesForLabels (); CRFTrainerByLabelLikelihood crft = new CRFTrainerByLabelLikelihood (crf); crft.trainIncremental (training); CRFExtractor extor = TestLatticeViewer.hackCrfExtor (crf); Extraction extraction = extor.extract (new ArrayIterator (data1)); if (!outputDir.exists ()) outputDir.mkdir (); DocumentViewer.writeExtraction (outputDir, extraction); }
public void testTokenAccuracy() { Pipe p = makeSpacePredictionPipe(); InstanceList instances = new InstanceList(p); instances.addThruPipe(new ArrayIterator(data)); InstanceList[] lists = instances.split(new Random(777), new double[] { .5, .5 }); CRF crf = new CRF(p.getDataAlphabet(), p.getTargetAlphabet()); crf.addFullyConnectedStatesForLabels(); CRFTrainerByLabelLikelihood crft = new CRFTrainerByLabelLikelihood(crf); crft.setUseSparseWeights(true); crft.trainIncremental(lists[0]); TokenAccuracyEvaluator eval = new TokenAccuracyEvaluator(lists, new String[] { "Train", "Test" }); eval.evaluateInstanceList(crft, lists[1], "Test"); assertEquals(0.9409, eval.getAccuracy("Test"), 0.001); }
crf1.addOrderNStates(lists[0], new int[] { 1, }, new boolean[] { false, }, "START", null, null, false); new CRFTrainerByLabelLikelihood(crf1).trainIncremental(lists[0]); new CRFTrainerByLabelLikelihood(crf2).trainIncremental(lists[0]); new CRFTrainerByLabelLikelihood(crf3).trainIncremental(lists[0]);
crf1.addOrderNStates(lists[0], new int[] { 1, }, new boolean[] { false, }, "START", null, null, false); new CRFTrainerByLabelLikelihood(crf1).trainIncremental(lists[0]); new CRFTrainerByLabelLikelihood(crf2).trainIncremental(lists[0]); new CRFTrainerByLabelLikelihood(crf3).trainIncremental(lists[0]);
public void testTokenAccuracy() { Pipe p = makeSpacePredictionPipe(); InstanceList instances = new InstanceList(p); instances.addThruPipe(new ArrayIterator(data)); InstanceList[] lists = instances.split(new Random(777), new double[] { .5, .5 }); CRF crf = new CRF(p.getDataAlphabet(), p.getTargetAlphabet()); crf.addFullyConnectedStatesForLabels(); CRFTrainerByLabelLikelihood crft = new CRFTrainerByLabelLikelihood(crf); crft.setUseSparseWeights(true); crft.trainIncremental(lists[0]); TokenAccuracyEvaluator eval = new TokenAccuracyEvaluator(lists, new String[] { "Train", "Test" }); eval.evaluateInstanceList(crft, lists[1], "Test"); assertEquals(0.9409, eval.getAccuracy("Test"), 0.001); }
crf.addFullyConnectedStatesForLabels(); CRFTrainerByLabelLikelihood crft = new CRFTrainerByLabelLikelihood(crf); crft.trainIncremental(training);
crf.addFullyConnectedStatesForLabels(); CRFTrainerByLabelLikelihood crft = new CRFTrainerByLabelLikelihood(crf); crft.trainIncremental(training);
CRFTrainerByLabelLikelihood crft1 = new CRFTrainerByLabelLikelihood( crf1); crft1.trainIncremental(instances); CRFTrainerByLabelLikelihood crft2 = new CRFTrainerByLabelLikelihood( crf2); crft2.trainIncremental(instances);
CRFTrainerByLabelLikelihood crft1 = new CRFTrainerByLabelLikelihood( crf1); crft1.trainIncremental(instances); CRFTrainerByLabelLikelihood crft2 = new CRFTrainerByLabelLikelihood( crf2); crft2.trainIncremental(instances);