/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package opennlp.tools.chunker;
import java.io.IOException;
import opennlp.tools.ml.model.AbstractModel;
import opennlp.tools.ml.model.Event;
import opennlp.tools.ml.model.Sequence;
import opennlp.tools.ml.model.SequenceStream;
import opennlp.tools.util.ObjectStream;
public class ChunkSampleSequenceStream implements SequenceStream {
private final ObjectStream<ChunkSample> samples;
private final ChunkerContextGenerator contextGenerator;
public ChunkSampleSequenceStream(ObjectStream<ChunkSample> samples,
ChunkerContextGenerator contextGenerator) {
this.samples = samples;
this.contextGenerator = contextGenerator;
}
@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<ChunkSample>(events,sample);
}
return null;
}
@Override
public Event[] updateContext(Sequence sequence, AbstractModel model) {
// TODO: Should be implemented for Perceptron sequence learning ...
return null;
}
@Override
public void reset() throws IOException, UnsupportedOperationException {
samples.reset();
}
@Override
public void close() throws IOException {
samples.close();
}
}