public Overlord() {
try {
selector = Selector.open();
queue = new ConcurrentLinkedQueue<Communicator>();
// open the pipe and register it with our selector
pipe = Pipe.open();
pipe.sink().configureBlocking(false);
pipe.source().configureBlocking(false);
pipe.source().register(selector, SelectionKey.OP_READ);
} catch (IOException e) {
throw new RuntimeException("select() failed");
}
}
public static CRF4 createCRF(File trainingFile, CRFInfo crfInfo) throws FileNotFoundException {
Reader trainingFileReader = new FileReader(trainingFile);
// Create a pipe that we can use to convert the training
// file to a feature vector sequence.
Pipe p = new SimpleTagger.SimpleTaggerSentence2FeatureVectorSequence();
// The training file does contain tags (aka targets)
p.setTargetProcessing(true);
// Register the default tag with the pipe, by looking it up
// in the targetAlphabet before we look up any other tag.
p.getTargetAlphabet().lookupIndex(crfInfo.defaultLabel);
// Create a new instancelist to hold the training data.
InstanceList trainingData = new InstanceList(p);
// Read in the training data.
trainingData.add(new LineGroupIterator(trainingFileReader, Pattern.compile("^\\s*$"), true));
// Create the CRF model.
CRF4 crf = new CRF4(p, null);
// Set various config options
crf.setGaussianPriorVariance(crfInfo.gaussianVariance);
crf.setTransductionType(crfInfo.transductionType);
// Set up the model's states.
if (crfInfo.stateInfoList != null) {
Iterator stateIter = crfInfo.stateInfoList.iterator();
while (stateIter.hasNext()) {
CRFInfo.StateInfo state = (CRFInfo.StateInfo) stateIter.next();
crf.addState(
state.name,
state.initialCost,
state.finalCost,
state.destinationNames,
state.labelNames,
state.weightNames);
}
} else if (crfInfo.stateStructure == CRFInfo.FULLY_CONNECTED_STRUCTURE)
crf.addStatesForLabelsConnectedAsIn(trainingData);
else if (crfInfo.stateStructure == CRFInfo.HALF_CONNECTED_STRUCTURE)
crf.addStatesForHalfLabelsConnectedAsIn(trainingData);
else if (crfInfo.stateStructure == CRFInfo.THREE_QUARTERS_CONNECTED_STRUCTURE)
crf.addStatesForThreeQuarterLabelsConnectedAsIn(trainingData);
else if (crfInfo.stateStructure == CRFInfo.BILABELS_STRUCTURE)
crf.addStatesForBiLabelsConnectedAsIn(trainingData);
else throw new RuntimeException("Unexpected state structure " + crfInfo.stateStructure);
// Set up the weight groups.
if (crfInfo.weightGroupInfoList != null) {
Iterator wgIter = crfInfo.weightGroupInfoList.iterator();
while (wgIter.hasNext()) {
CRFInfo.WeightGroupInfo wg = (CRFInfo.WeightGroupInfo) wgIter.next();
FeatureSelection fs =
FeatureSelection.createFromRegex(
crf.getInputAlphabet(), Pattern.compile(wg.featureSelectionRegex));
crf.setFeatureSelection(crf.getWeightsIndex(wg.name), fs);
}
}
// Train the CRF.
crf.train(trainingData, null, null, null, crfInfo.maxIterations);
return crf;
}