/** Calculates the confidence in the tagging of a {@link Segment}. @return 0-1 confidence value. higher = more confident. */ public double estimateConfidenceFor (Segment segment, SumLatticeDefault cachedLattice) { Sequence predSequence = segment.getPredicted (); Sequence input = segment.getInput (); SumLatticeDefault lattice = (cachedLattice==null) ? new SumLatticeDefault (model, input) : cachedLattice; double confidence = 1; for (int i=segment.getStart(); i <= segment.getEnd(); i++) confidence *= lattice.getGammaProbability (i+1, model.getState (stateIndexOfString ((String)predSequence.get (i)))); return confidence; }
/** Calculates the confidence in the tagging of a {@link Segment}. @return 0-1 confidence value. higher = more confident. */ public double estimateConfidenceFor (Segment segment, SumLatticeDefault cachedLattice) { Sequence predSequence = segment.getPredicted (); Sequence input = segment.getInput (); SumLatticeDefault lattice = (cachedLattice==null) ? new SumLatticeDefault (model, input) : cachedLattice; double confidence = 1; for (int i=segment.getStart(); i <= segment.getEnd(); i++) confidence *= lattice.getGammaProbability (i+1, model.getState (stateIndexOfString ((String)predSequence.get (i)))); return confidence; }
/** Calculates the confidence in the tagging of a {@link Segment}. @return 0-1 confidence value. higher = more confident. */ public double estimateConfidenceFor (Segment segment, SumLatticeDefault cachedLattice) { Sequence predSequence = segment.getPredicted (); Sequence input = segment.getInput (); SumLatticeDefault lattice = (cachedLattice==null) ? new SumLatticeDefault (model, input) : cachedLattice; double confidence = 1; for (int i=segment.getStart(); i <= segment.getEnd(); i++) confidence *= lattice.getGammaProbability (i+1, model.getState (stateIndexOfString ((String)predSequence.get (i)))); return confidence; }
/** Calculates the confidence in the tagging of a {@link Segment}. @return 0-1 confidence value. higher = more confident. */ public double estimateConfidenceFor (Segment segment, SumLatticeDefault cachedLattice) { Sequence predSequence = segment.getPredicted (); Sequence input = segment.getInput (); SumLatticeDefault lattice = (cachedLattice==null) ? new SumLatticeDefault (model, input) : cachedLattice; double confidence = 0; for (int i=segment.getStart(); i <= segment.getEnd(); i++) { int stateIndex = stateIndexOfString((String)predSequence.get(i)); if (stateIndex == -1) // Unknown label. return 0.0; confidence += lattice.getGammaProbability(i+1, model.getState(stateIndex)); } return confidence/(double)segment.size(); }
/** Calculates the confidence in the tagging of a {@link Segment}. @return 0-1 confidence value. higher = more confident. */ public double estimateConfidenceFor (Segment segment, SumLatticeDefault cachedLattice) { Sequence predSequence = segment.getPredicted (); Sequence input = segment.getInput (); SumLatticeDefault lattice = (cachedLattice==null) ? new SumLatticeDefault (model, input) : cachedLattice; double confidence = 0; for (int i=segment.getStart(); i <= segment.getEnd(); i++) { int stateIndex = stateIndexOfString((String)predSequence.get(i)); if (stateIndex == -1) // Unknown label. return 0.0; confidence += lattice.getGammaProbability(i+1, model.getState(stateIndex)); } return confidence/(double)segment.size(); }
/** Calculates the confidence in the tagging of a {@link Segment}. @return 0-1 confidence value. higher = more confident. */ public double estimateConfidenceFor (Segment segment, SumLatticeDefault cachedLattice) { Sequence predSequence = segment.getPredicted (); Sequence input = segment.getInput (); SumLatticeDefault lattice = (cachedLattice==null) ? new SumLatticeDefault (model, input) : cachedLattice; double confidence = 0; for (int i=segment.getStart(); i <= segment.getEnd(); i++) { int stateIndex = stateIndexOfString((String)predSequence.get(i)); if (stateIndex == -1) // Unknown label. return 0.0; confidence += lattice.getGammaProbability(i+1, model.getState(stateIndex)); } return confidence/(double)segment.size(); }
public void testXis() { Pipe p = makeSpacePredictionPipe(); InstanceList instances = new InstanceList(p); instances.addThruPipe(new ArrayIterator(data)); CRF crf1 = new CRF(p, null); crf1.addFullyConnectedStatesForLabels(); CRFTrainerByLabelLikelihood crft1 = new CRFTrainerByLabelLikelihood( crf1); crft1.train(instances, 10); // Let's get some parameters Instance inst = instances.get(0); Sequence input = (Sequence) inst.getData(); SumLatticeDefault lattice = new SumLatticeDefault(crf1, input, (Sequence) inst.getTarget(), null, true); for (int ip = 0; ip < lattice.length() - 1; ip++) { for (int i = 0; i < crf1.numStates(); i++) { Transducer.State state = crf1.getState(i); Transducer.TransitionIterator it = state.transitionIterator( input, ip); double gamma = lattice.getGammaProbability(ip, state); double xiSum = 0; while (it.hasNext()) { Transducer.State dest = it.nextState(); double xi = lattice.getXiProbability(ip, state, dest); xiSum += xi; } assertEquals(gamma, xiSum, 1e-5); } } }
public void testXis() { Pipe p = makeSpacePredictionPipe(); InstanceList instances = new InstanceList(p); instances.addThruPipe(new ArrayIterator(data)); CRF crf1 = new CRF(p, null); crf1.addFullyConnectedStatesForLabels(); CRFTrainerByLabelLikelihood crft1 = new CRFTrainerByLabelLikelihood( crf1); crft1.train(instances, 10); // Let's get some parameters Instance inst = instances.get(0); Sequence input = (Sequence) inst.getData(); SumLatticeDefault lattice = new SumLatticeDefault(crf1, input, (Sequence) inst.getTarget(), null, true); for (int ip = 0; ip < lattice.length() - 1; ip++) { for (int i = 0; i < crf1.numStates(); i++) { Transducer.State state = crf1.getState(i); Transducer.TransitionIterator it = state.transitionIterator( input, ip); double gamma = lattice.getGammaProbability(ip, state); double xiSum = 0; while (it.hasNext()) { Transducer.State dest = it.nextState(); double xi = lattice.getXiProbability(ip, state, dest); xiSum += xi; } assertEquals(gamma, xiSum, 1e-5); } } }