public List<RerankerResult> probabilities(List<RerankExample> ex) { Classification classify = model.classify(ex); LabelVector labeling = (LabelVector) classify.getLabeling(); List<RerankerResult> result = Lists.newArrayListWithCapacity(ex.size()); for (int i = 0; i < ex.size(); i++) { Label rankLabel = labeling.getLabelAlphabet().lookupLabel(Integer.toString(i)); result.add(new RerankerResult(ex.get(i), labeling.value(rankLabel))); } Collections.sort(result, Ordering.<RerankerResult>natural().reverse()); return result; }
static private void internalTest() throws IOException { Classifier classifier = train(internalData, internalTargets, 1.0, null); System.out.println("Training accuracy = " + test(classifier, internalData, internalTargets)); Classification cl = classify(classifier, internalInstance); Labeling lab = cl.getLabeling(); LabelAlphabet labels = lab.getLabelAlphabet(); for (int c = 0; c < labels.size(); c++) System.out.print(labels.lookupObject(c) + " " + lab.value(c) + " "); System.out.println(); }
private static void printTrialClassification(Trial trial) { for (Classification c : trial) { Instance instance = c.getInstance(); System.out.print(instance.getName() + " " + instance.getTarget() + " "); Labeling labeling = c.getLabeling(); for (int j = 0; j < labeling.numLocations(); j++){ System.out.print(labeling.getLabelAtRank(j).toString() + ":" + labeling.getValueAtRank(j) + " "); } System.out.println(); } }
private static void printTrialClassification(Trial trial) { for (Classification c : trial) { Instance instance = c.getInstance(); System.out.print(instance.getName() + " " + instance.getTarget() + " "); Labeling labeling = c.getLabeling(); for (int j = 0; j < labeling.numLocations(); j++){ System.out.print(labeling.getLabelAtRank(j).toString() + ":" + labeling.getValueAtRank(j) + " "); } System.out.println(); } }
private static void printTrialClassification(Trial trial) { for (Classification c : trial) { Instance instance = c.getInstance(); System.out.print(instance.getName() + " " + instance.getTarget() + " "); Labeling labeling = c.getLabeling(); for (int j = 0; j < labeling.numLocations(); j++){ System.out.print(labeling.getLabelAtRank(j).toString() + ":" + labeling.getValueAtRank(j) + " "); } System.out.println(); } }
static private void internalTest() throws IOException { Classifier classifier = train(internalData, internalTargets, 1.0, null); System.out.println("Training accuracy = " + test(classifier, internalData, internalTargets)); Classification cl = classify(classifier, internalInstance); Labeling lab = cl.getLabeling(); LabelAlphabet labels = lab.getLabelAlphabet(); for (int c = 0; c < labels.size(); c++) System.out.print(labels.lookupObject(c) + " " + lab.value(c) + " "); System.out.println(); }
@Override public Map<OUTCOME_TYPE, Double> score(List<Feature> features) throws CleartkProcessingException { Classification classification = classifier.classify(toInstance(features)); Labeling labeling = classification.getLabeling(); Map<OUTCOME_TYPE, Double> returnValues = Maps.newHashMap(); for (int i = 0; i < labeling.numLocations(); i++) { String label = labeling.getLabelAtRank(i).toString(); OUTCOME_TYPE outcome = outcomeEncoder.decode(label); double score = labeling.getValueAtRank(i); returnValues.put(outcome, score); } return returnValues; }
private static void printTrialClassification(Trial trial) { for (int i = 0; i < trial.size(); i++) { Instance instance = trial.get(i).getInstance(); System.out.print(instance.getName() + " " + instance.getTarget() + " "); Labeling labeling = trial.get(i).getLabeling(); for (int j = 0; j < labeling.numLocations(); j++){ System.out.print(labeling.getLabelAtRank(j).toString() + ":" + labeling.getValueAtRank(j) + " "); } System.out.println(); } }
private static void printTrialClassification(Trial trial) { for (int i = 0; i < trial.size(); i++) { Instance instance = trial.get(i).getInstance(); System.out.print(instance.getName() + " " + instance.getTarget() + " "); Labeling labeling = trial.get(i).getLabeling(); for (int j = 0; j < labeling.numLocations(); j++){ System.out.print(labeling.getLabelAtRank(j).toString() + ":" + labeling.getValueAtRank(j) + " "); } System.out.println(); } }
private static void printTrialClassification(Trial trial) { for (int i = 0; i < trial.size(); i++) { Instance instance = trial.get(i).getInstance(); System.out.print(instance.getName() + " " + instance.getTarget() + " "); Labeling labeling = trial.get(i).getLabeling(); for (int j = 0; j < labeling.numLocations(); j++){ System.out.print(labeling.getLabelAtRank(j).toString() + ":" + labeling.getValueAtRank(j) + " "); } System.out.println(); } }
public OUTCOME_TYPE classify(List<Feature> features) throws CleartkProcessingException { Classification classification = classifier.classify(toInstance(features)); String returnValue = classification.getLabeling().getBestLabel().toString(); return outcomeEncoder.decode(returnValue); }
public String classify(String txt) { Classification classification = classifier.classify(pipes .instanceFrom(new Instance(txt, null, null, null))); return classification.getLabeling().getBestLabel().toString(); } }
public OUTCOME_TYPE classify(List<Feature> features) throws CleartkProcessingException { Classification classification = classifier.classify(toInstance(features)); String returnValue = classification.getLabeling().getBestLabel().toString(); return outcomeEncoder.decode(returnValue); }
private void writeClassificationToMongo(List<Classification> classify) { classify.forEach( classification -> { Instance instance = classification.getInstance(); documentsCollection.findOneAndUpdate( Filters.eq(new ObjectId((String) instance.getName())), Updates.set( CLASSIFICATION_FIELD, classification.getLabeling().getBestLabel().toString())); }); }
private void writeClassificationToMongo(List<Classification> classify) { classify.forEach( classification -> { Instance instance = classification.getInstance(); documentsCollection.findOneAndUpdate( Filters.eq(new ObjectId((String) instance.getName())), Updates.set( CLASSIFICATION_FIELD, classification.getLabeling().getBestLabel().toString())); }); }
public Matrix predictOne(Matrix input) { Instance instance = new Sample2Instance(input, null, classifier.getAlphabet(), classifier.getLabelAlphabet(), classifier.getInstancePipe(), cumSum); Classification c = classifier.classify(instance); return new Labeling2Matrix(c.getLabeling()); }
public Classification classify (Instance inst) { int numClasses = getLabelAlphabet().size(); double[] scores = new double[numClasses]; int bestIndex; double sum = 0; for (int i = 0; i < baggedClassifiers.length; i++) { Labeling labeling = baggedClassifiers[i].classify(inst).getLabeling(); labeling.addTo (scores); } MatrixOps.normalize (scores); return new Classification (inst, this, new LabelVector (getLabelAlphabet(), scores)); }
public Classification classify (Instance inst) { int numClasses = getLabelAlphabet().size(); double[] scores = new double[numClasses]; int bestIndex; double sum = 0; for (int i = 0; i < baggedClassifiers.length; i++) { Labeling labeling = baggedClassifiers[i].classify(inst).getLabeling(); labeling.addTo (scores); } MatrixOps.normalize (scores); return new Classification (inst, this, new LabelVector (getLabelAlphabet(), scores)); }
@Override protected void doProcess(JCas jCas) throws AnalysisEngineProcessException { InstanceList instances = new InstanceList(classifierModel.getInstancePipe()); instances.addThruPipe(new Instance(jCas.getDocumentText(), "", "from jcas", null)); Classification classify = classifierModel.classify(instances.get(0)); Metadata md = new Metadata(jCas); md.setKey(metadataKey); md.setValue(classify.getLabeling().getBestLabel().toString()); addToJCasIndex(md); }
@Override protected void doProcess(JCas jCas) throws AnalysisEngineProcessException { InstanceList instances = new InstanceList(classifierModel.getInstancePipe()); instances.addThruPipe(new Instance(jCas.getDocumentText(), "", "from jcas", null)); Classification classify = classifierModel.classify(instances.get(0)); Metadata md = new Metadata(jCas); md.setKey(metadataKey); md.setValue(classify.getLabeling().getBestLabel().toString()); addToJCasIndex(md); }