private boolean[][] labelConnectionsIn(InstanceList trainingSet) {
int numLabels = outputAlphabet.size();
boolean[][] connections = new boolean[numLabels][numLabels];
for (Instance instance : trainingSet) {
FeatureSequence output = (FeatureSequence) instance.getTarget();
for (int j = 1; j < output.size(); j++) {
int sourceIndex = outputAlphabet.lookupIndex(output.get(j - 1));
int destIndex = outputAlphabet.lookupIndex(output.get(j));
assert (sourceIndex >= 0 && destIndex >= 0);
connections[sourceIndex][destIndex] = true;
}
}
return connections;
}
public TransitionIterator(
State source, FeatureSequence inputSeq, int inputPosition, String output, HMM hmm) {
this.source = source;
this.hmm = hmm;
this.inputSequence = inputSeq;
this.inputFeature = new Integer(inputSequence.getIndexAtPosition(inputPosition));
this.inputPos = inputPosition;
this.weights = new double[source.destinations.length];
for (int transIndex = 0; transIndex < source.destinations.length; transIndex++) {
if (output == null || output.equals(source.labels[transIndex])) {
weights[transIndex] = 0;
// xxx should this be emission of the _next_ observation?
// double logEmissionProb =
// hmm.emissionMultinomial[source.getIndex()].logProbability
// (inputSeq.get (inputPosition));
int destIndex = source.getDestinationState(transIndex).getIndex();
double logEmissionProb =
hmm.emissionMultinomial[destIndex].logProbability(inputSeq.get(inputPosition));
double logTransitionProb =
hmm.transitionMultinomial[source.getIndex()].logProbability(
source.destinationNames[transIndex]);
// weight = logProbability
weights[transIndex] = (logEmissionProb + logTransitionProb);
assert (!Double.isNaN(weights[transIndex]));
} else weights[transIndex] = IMPOSSIBLE_WEIGHT;
}
nextIndex = 0;
while (nextIndex < source.destinations.length && weights[nextIndex] == IMPOSSIBLE_WEIGHT)
nextIndex++;
}
