public boolean predict(SingleDecision decision) throws MaltChainedException {
if (getGuide().getGuideMode() == ClassifierGuide.GuideMode.BATCH) {
throw new GuideException("Can only predict during parsing. ");
} else if (!(divideFeature.getFeatureValue() instanceof SingleFeatureValue)) {
throw new GuideException("The divide feature does not have a single value. ");
}
// divideFeature.update();
if (divideModels != null
&& divideModels.containsKey(
((SingleFeatureValue) divideFeature.getFeatureValue()).getCode())) {
return divideModels
.get(((SingleFeatureValue) divideFeature.getFeatureValue()).getCode())
.predict(decision);
} else if (masterModel != null && masterModel.getFrequency() > 0) {
return masterModel.predict(decision);
} else {
getGuide()
.getConfiguration()
.getConfigLogger()
.info(
"Could not predict the next parser decision because there is "
+ "no divide or master model that covers the divide value '"
+ ((SingleFeatureValue) divideFeature.getFeatureValue()).getCode()
+ "', as default"
+ " class code '1' is used. ");
decision.addDecision(1); // default prediction
// classCodeTable.getEmptyKBestList().addKBestItem(1);
}
return true;
}
private AtomicModel getAtomicModel() throws MaltChainedException {
if (getGuide().getGuideMode() == ClassifierGuide.GuideMode.BATCH) {
throw new GuideException("Can only predict during parsing. ");
} else if (!(divideFeature.getFeatureValue() instanceof SingleFeatureValue)) {
throw new GuideException("The divide feature does not have a single value. ");
}
if (divideModels != null
&& divideModels.containsKey(
((SingleFeatureValue) divideFeature.getFeatureValue()).getCode())) {
return divideModels.get(((SingleFeatureValue) divideFeature.getFeatureValue()).getCode());
} else if (masterModel != null && masterModel.getFrequency() > 0) {
return masterModel;
} else {
getGuide()
.getConfiguration()
.getConfigLogger()
.info(
"Could not predict the next parser decision because there is "
+ "no divide or master model that covers the divide value '"
+ ((SingleFeatureValue) divideFeature.getFeatureValue()).getCode()
+ "', as default"
+ " class code '1' is used. ");
}
return null;
}
public void noMoreInstances() throws MaltChainedException {
// if (getGuide().getGuideMode() == Guide.GuideMode.CLASSIFY) {
// throw new GuideException("Can only finish all data during learning. ");
// }
if (divideModels != null) {
divideFeature.updateCardinality();
for (Integer index : divideModels.keySet()) {
divideModels.get(index).noMoreInstances();
}
final TreeSet<Integer> removeSet = new TreeSet<Integer>();
for (Integer index : divideModels.keySet()) {
if (divideModels.get(index).getFrequency() <= divideThreshold) {
divideModels
.get(index)
.moveAllInstances(masterModel, divideFeature, divideFeatureIndexVector);
removeSet.add(index);
}
}
for (Integer index : removeSet) {
divideModels.remove(index);
}
masterModel.noMoreInstances();
} else {
throw new GuideException("The feature divide models cannot be found. ");
}
}
public void addInstance(SingleDecision decision) throws MaltChainedException {
if (getGuide().getGuideMode() == ClassifierGuide.GuideMode.CLASSIFY) {
throw new GuideException("Can only add instance during learning. ");
} else if (!(divideFeature.getFeatureValue() instanceof SingleFeatureValue)) {
throw new GuideException("The divide feature does not have a single value. ");
}
divideFeature.update();
if (divideModels != null) {
if (!divideModels.containsKey(
((SingleFeatureValue) divideFeature.getFeatureValue()).getCode())) {
divideModels.put(
((SingleFeatureValue) divideFeature.getFeatureValue()).getCode(),
new AtomicModel(
((SingleFeatureValue) divideFeature.getFeatureValue()).getCode(),
divideFeatureVector,
this));
}
divideModels
.get(((SingleFeatureValue) divideFeature.getFeatureValue()).getCode())
.addInstance(decision);
} else {
throw new GuideException("The feature divide models cannot be found. ");
}
}
public void moveAllInstances(
LearningMethod method,
FeatureFunction divideFeature,
ArrayList<Integer> divideFeatureIndexVector)
throws MaltChainedException {
if (method == null) {
throw new LibException("The learning method cannot be found. ");
} else if (divideFeature == null) {
throw new LibException("The divide feature cannot be found. ");
}
try {
final BufferedReader in = new BufferedReader(getInstanceInputStreamReader(".ins"));
final BufferedWriter out = method.getInstanceWriter();
final StringBuilder sb = new StringBuilder(6);
int l = in.read();
char c;
int j = 0;
while (true) {
if (l == -1) {
sb.setLength(0);
break;
}
c = (char) l;
l = in.read();
if (c == '\t') {
if (divideFeatureIndexVector.contains(j - 1)) {
out.write(
Integer.toString(
((SingleFeatureValue) divideFeature.getFeatureValue()).getIndexCode()));
out.write('\t');
}
out.write(sb.toString());
j++;
out.write('\t');
sb.setLength(0);
} else if (c == '\n') {
out.write(sb.toString());
if (divideFeatureIndexVector.contains(j - 1)) {
out.write('\t');
out.write(
Integer.toString(
((SingleFeatureValue) divideFeature.getFeatureValue()).getIndexCode()));
}
out.write('\n');
sb.setLength(0);
method.increaseNumberOfInstances();
this.decreaseNumberOfInstances();
j = 0;
} else {
sb.append(c);
}
}
in.close();
getFile(".ins").delete();
out.flush();
} catch (SecurityException e) {
throw new LibException("The learner cannot remove the instance file. ", e);
} catch (NullPointerException e) {
throw new LibException("The instance file cannot be found. ", e);
} catch (FileNotFoundException e) {
throw new LibException("The instance file cannot be found. ", e);
} catch (IOException e) {
throw new LibException("The learner read from the instance file. ", e);
}
}
