/**
* Invokes the train() of the learning method
*
* @throws MaltChainedException
*/
public void train() throws MaltChainedException {
try {
method.train(featureVector);
method.terminate();
method = null;
} catch (NullPointerException e) {
throw new GuideException("The learner cannot be found. ", e);
}
}
public void finalizeSentence(DependencyStructure dependencyGraph) throws MaltChainedException {
try {
method.finalizeSentence(dependencyGraph);
} catch (NullPointerException e) {
throw new GuideException("The learner cannot be found. ", e);
}
}
public void noMoreInstances() throws MaltChainedException {
try {
method.noMoreInstances();
} catch (NullPointerException e) {
throw new GuideException("The learner cannot be found. ", e);
}
}
public void addInstance(SingleDecision decision) throws MaltChainedException {
try {
method.addInstance(decision, featureVector);
} catch (NullPointerException e) {
throw new GuideException("The learner cannot be found. ", e);
}
}
/**
* Constructs an atomic model.
*
* @param index the index of the atomic model (-1..n), where -1 is special value (used by a single
* model or the master divide model) and n is number of divide models.
* @param features the feature vector used by the atomic model.
* @param parent the parent guide model.
* @throws MaltChainedException
*/
public AtomicModel(int index, FeatureVector features, Model parent) throws MaltChainedException {
setParent(parent);
setIndex(index);
if (index == -1) {
setModelName(parent.getModelName() + ".");
} else {
setModelName(parent.getModelName() + "." + new Formatter().format("%03d", index) + ".");
}
setFeatures(features);
setFrequency(0);
initMethod();
if (getGuide().getGuideMode() == ClassifierGuide.GuideMode.BATCH
&& index == -1
&& getGuide().getConfiguration().getConfigurationDir().getInfoFileWriter() != null) {
try {
getGuide()
.getConfiguration()
.getConfigurationDir()
.getInfoFileWriter()
.write(method.toString());
getGuide().getConfiguration().getConfigurationDir().getInfoFileWriter().flush();
} catch (IOException e) {
throw new GuideException("Could not write learner settings to the information file. ", e);
}
}
}
/**
* Moves all instance from this atomic model into the destination atomic model and add the divide
* feature. This method is used by the feature divide model to sum up all model below a certain
* threshold.
*
* @param model the destination atomic model
* @param divideFeature the divide feature
* @param divideFeatureIndexVector the divide feature index vector
* @throws MaltChainedException
*/
public void moveAllInstances(
AtomicModel model, FeatureFunction divideFeature, ArrayList<Integer> divideFeatureIndexVector)
throws MaltChainedException {
if (method == null) {
throw new GuideException("The learner cannot be found. ");
} else if (model == null) {
throw new GuideException("The guide model cannot be found. ");
} else if (divideFeature == null) {
throw new GuideException("The divide feature cannot be found. ");
} else if (divideFeatureIndexVector == null) {
throw new GuideException("The divide feature index vector cannot be found. ");
}
((Modifiable) divideFeature).setFeatureValue(index);
method.moveAllInstances(model.getMethod(), divideFeature, divideFeatureIndexVector);
method.terminate();
method = null;
}
public void terminate() throws MaltChainedException {
if (method != null) {
method.terminate();
method = null;
}
featureVector = null;
parent = null;
}
public boolean predict(SingleDecision decision) throws MaltChainedException {
try {
if (getGuide().getGuideMode() == ClassifierGuide.GuideMode.BATCH) {
throw new GuideException("Cannot predict during batch training. ");
}
return method.predict(featureVector, decision);
} catch (NullPointerException e) {
throw new GuideException("The learner cannot be found. ", e);
}
}
/* (non-Javadoc)
* @see java.lang.Object#toString()
*/
public String toString() {
final StringBuilder sb = new StringBuilder();
sb.append(method.toString());
return sb.toString();
}
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);
}
}
