opennlp.tools.ml.model.Sequence java code examples

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private void myMethod () {
 List l =new LinkedList()
Collections.emptyList()
new ArrayList()
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}

@Override
public Sequence read() throws IOException {
 ChunkSample sample = samples.read();
 if (sample != null) {
  String[] sentence = sample.getSentence();
  String[] tags = sample.getTags();
  Event[] events = new Event[sentence.length];
  for (int i = 0; i < sentence.length; i++) {
   // it is safe to pass the tags as previous tags because
   // the context generator does not look for non predicted tags
   String[] context = contextGenerator.getContext(i, sentence, tags, null);
   events[i] = new Event(tags[i], context);
  }
  return new Sequence<>(events,sample);
 }
 return null;
}

@Override
public Event read() throws IOException {
 while (!eventIt.hasNext()) {
  Sequence<?> sequence = sequenceStream.read();
  if (sequence == null) {
   return null;
  }
  eventIt = Arrays.asList(sequence.getEvents()).iterator();
 }
 return eventIt.next();
}

@SuppressWarnings("unchecked")
public Event[] updateContext(Sequence sequence, AbstractModel model) {
 Sequence<POSSample> pss = sequence;
 POSTagger tagger = new POSTaggerME(new POSModel("x-unspecified", model, null, new POSTaggerFactory()));
 String[] sentence = pss.getSource().getSentence();
 Object[] ac = pss.getSource().getAddictionalContext();
 String[] tags = tagger.tag(pss.getSource().getSentence());
 Event[] events = new Event[sentence.length];
 POSSampleEventStream.generateEvents(sentence, tags, ac, pcg)
   .toArray(events);
 return events;
}

@Override
public Sequence read() throws IOException {
 POSSample sample = psi.read();
 if (sample != null) {
  String[] sentence = sample.getSentence();
  String[] tags = sample.getTags();
  Event[] events = new Event[sentence.length];
  for (int i = 0; i < sentence.length; i++) {
   // it is safe to pass the tags as previous tags because
   // the context generator does not look for non predicted tags
   String[] context = pcg.getContext(i, sentence, tags, null);
   events[i] = new Event(tags[i], context);
  }
  Sequence<POSSample> sequence = new Sequence<POSSample>(events,sample);
  return sequence;
 }
 return null;
}

while ((sequence = sequenceStream.read()) != null) {
 Event[] taggerEvents = sequenceStream.updateContext(sequence, model);
 Event[] events = sequence.getEvents();
 boolean update = false;
 for (int ei = 0; ei < events.length; ei++, oei++) {

@SuppressWarnings("unchecked")
public Event[] updateContext(Sequence sequence, AbstractModel model) {
 TokenNameFinder tagger = new NameFinderME(new TokenNameFinderModel(
   "x-unspecified", model, Collections.emptyMap(), null));
 String[] sentence = ((Sequence<NameSample>) sequence).getSource().getSentence();
 String[] tags = seqCodec.encode(tagger.find(sentence), sentence.length);
 Event[] events = new Event[sentence.length];
 NameFinderEventStream.generateEvents(sentence,tags,pcg).toArray(events);
 return events;
}

@Override
public Sequence read() throws IOException {
 LemmaSample sample = samples.read();
 if (sample != null) {
  String[] sentence = sample.getTokens();
  String[] tags = sample.getTags();
  String[] preds = sample.getLemmas();
  Event[] events = new Event[sentence.length];
  for (int i = 0; i < sentence.length; i++) {
   // it is safe to pass the tags as previous tags because
   // the context generator does not look for non predicted tags
   String[] context = contextGenerator.getContext(i, sentence, tags, preds);
   events[i] = new Event(tags[i], context);
  }
  return new Sequence<>(events,sample);
 }
 return null;
}

@Override
public Event read() throws IOException {
 while (!eventIt.hasNext()) {
  Sequence<?> sequence = sequenceStream.read();
  if (sequence == null) {
   return null;
  }
  eventIt = Arrays.asList(sequence.getEvents()).iterator();
 }
 return eventIt.next();
}

@SuppressWarnings("unchecked")
public Event[] updateContext(Sequence sequence, AbstractModel model) {
 Sequence<POSSample> pss = sequence;
 POSTagger tagger = new POSTaggerME(new POSModel("x-unspecified", model, null, new POSTaggerFactory()));
 String[] sentence = pss.getSource().getSentence();
 Object[] ac = pss.getSource().getAddictionalContext();
 String[] tags = tagger.tag(pss.getSource().getSentence());
 Event[] events = new Event[sentence.length];
 POSSampleEventStream.generateEvents(sentence, tags, ac, pcg)
   .toArray(events);
 return events;
}

@Override
public Sequence read() throws IOException {
 NameSample sample = psi.read();
 if (sample != null) {
  String[] sentence = sample.getSentence();
  String[] tags = seqCodec.encode(sample.getNames(), sentence.length);
  Event[] events = new Event[sentence.length];
  for (int i = 0; i < sentence.length; i++) {
   // it is safe to pass the tags as previous tags because
   // the context generator does not look for non predicted tags
   String[] context;
   if (useOutcomes) {
    context = pcg.getContext(i, sentence, tags, null);
   }
   else {
    context = pcg.getContext(i, sentence, null, null);
   }
   events[i] = new Event(tags[i], context);
  }
  return new Sequence<>(events,sample);
 }
 else {
  return null;
 }
}

@Override
public Event read() throws IOException {
 while (!eventIt.hasNext()) {
  Sequence<?> sequence = sequenceStream.read();
  if (sequence == null) {
   return null;
  }
  eventIt = Arrays.asList(sequence.getEvents()).iterator();
 }
 return eventIt.next();
}

@SuppressWarnings("unchecked")
public Event[] updateContext(Sequence sequence, AbstractModel model) {
 Sequence<POSSample> pss = sequence;
 POSTagger tagger = new POSTaggerME(new POSModel("x-unspecified", model, null, new POSTaggerFactory()));
 String[] sentence = pss.getSource().getSentence();
 Object[] ac = pss.getSource().getAddictionalContext();
 String[] tags = tagger.tag(pss.getSource().getSentence());
 Event[] events = new Event[sentence.length];
 POSSampleEventStream.generateEvents(sentence, tags, ac, pcg)
   .toArray(events);
 return events;
}

@Override
public Sequence read() throws IOException {
 ChunkSample sample = samples.read();
 if (sample != null) {
  String[] sentence = sample.getSentence();
  String[] tags = sample.getTags();
  Event[] events = new Event[sentence.length];
  for (int i = 0; i < sentence.length; i++) {
   // it is safe to pass the tags as previous tags because
   // the context generator does not look for non predicted tags
   String[] context = contextGenerator.getContext(i, sentence, tags, null);
   events[i] = new Event(tags[i], context);
  }
  return new Sequence<>(events,sample);
 }
 return null;
}

while ((sequence = sequenceStream.read()) != null) {
 Event[] taggerEvents = sequenceStream.updateContext(sequence, model);
 Event[] events = sequence.getEvents();
 boolean update = false;
 for (int ei = 0; ei < events.length; ei++, oei++) {

@SuppressWarnings("unchecked")
public Event[] updateContext(Sequence sequence, AbstractModel model) {
 TokenNameFinder tagger = new NameFinderME(new TokenNameFinderModel(
   "x-unspecified", model, Collections.emptyMap(), null));
 String[] sentence = ((Sequence<NameSample>) sequence).getSource().getSentence();
 String[] tags = seqCodec.encode(tagger.find(sentence), sentence.length);
 Event[] events = new Event[sentence.length];
 NameFinderEventStream.generateEvents(sentence,tags,pcg).toArray(events);
 return events;
}

@Override
public Sequence read() throws IOException {
 POSSample sample = psi.read();
 if (sample != null) {
  String[] sentence = sample.getSentence();
  String[] tags = sample.getTags();
  Event[] events = new Event[sentence.length];
  for (int i = 0; i < sentence.length; i++) {
   // it is safe to pass the tags as previous tags because
   // the context generator does not look for non predicted tags
   String[] context = pcg.getContext(i, sentence, tags, null);
   events[i] = new Event(tags[i], context);
  }
  Sequence<POSSample> sequence = new Sequence<POSSample>(events,sample);
  return sequence;
 }
 return null;
}

while ((sequence = sequenceStream.read()) != null) {
 Event[] taggerEvents = sequenceStream.updateContext(sequence, model);
 Event[] events = sequence.getEvents();
 boolean update = false;
 for (int ei = 0; ei < events.length; ei++, oei++) {

@SuppressWarnings("unchecked")
public Event[] updateContext(Sequence sequence, AbstractModel model) {
 TokenNameFinder tagger = new NameFinderME(new TokenNameFinderModel(
   "x-unspecified", model, Collections.emptyMap(), null));
 String[] sentence = ((Sequence<NameSample>) sequence).getSource().getSentence();
 String[] tags = seqCodec.encode(tagger.find(sentence), sentence.length);
 Event[] events = new Event[sentence.length];
 NameFinderEventStream.generateEvents(sentence,tags,pcg).toArray(events);
 return events;
}

@Override
public Sequence read() throws IOException {
 ChunkSample sample = samples.read();
 if (sample != null) {
  String[] sentence = sample.getSentence();
  String[] tags = sample.getTags();
  Event[] events = new Event[sentence.length];
  for (int i = 0; i < sentence.length; i++) {
   // it is safe to pass the tags as previous tags because
   // the context generator does not look for non predicted tags
   String[] context = contextGenerator.getContext(i, sentence, tags, null);
   events[i] = new Event(tags[i], context);
  }
  return new Sequence<>(events,sample);
 }
 return null;
}

@Override
public Sequence read() throws IOException {
 POSSample sample = psi.read();
 if (sample != null) {
  String[] sentence = sample.getSentence();
  String[] tags = sample.getTags();
  Event[] events = new Event[sentence.length];
  for (int i = 0; i < sentence.length; i++) {
   // it is safe to pass the tags as previous tags because
   // the context generator does not look for non predicted tags
   String[] context = pcg.getContext(i, sentence, tags, null);
   events[i] = new Event(tags[i], context);
  }
  Sequence<POSSample> sequence = new Sequence<POSSample>(events,sample);
  return sequence;
 }
 return null;
}

Javadoc

Class which models a sequence.

Most used methods

<init>
Creates a new sequence made up of the specified events and derived from the specified source.
getEvents
Returns the events which make up this sequence.
getSource
Returns an object from which this sequence can be derived. This object is used when the events for t

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How to useSequence in opennlp.tools.ml.model

Best Java code snippets using opennlp.tools.ml.model.Sequence (Showing top 20 results out of 315)

How to use
Sequence
in
opennlp.tools.ml.model