private void computeStep(
final HiddenMarkovModel hmm, final MLDataPair o, final int t, final int j) {
double minDelta = Double.MAX_VALUE;
int min_psy = 0;
for (int i = 0; i < hmm.getStateCount(); i++) {
final double thisDelta = this.delta[t - 1][i] - Math.log(hmm.getTransitionProbability(i, j));
if (minDelta > thisDelta) {
minDelta = thisDelta;
min_psy = i;
}
}
this.delta[t][j] = minDelta - Math.log(hmm.getStateDistribution(j).probability(o));
this.psy[t][j] = min_psy;
}
public ViterbiCalculator(final MLDataSet oseq, final HiddenMarkovModel hmm) {
if (oseq.size() < 1) {
throw new IllegalArgumentException("Must not have empty sequence");
}
this.delta = new double[oseq.size()][hmm.getStateCount()];
this.psy = new int[oseq.size()][hmm.getStateCount()];
this.stateSequence = new int[oseq.size()];
for (int i = 0; i < hmm.getStateCount(); i++) {
this.delta[0][i] =
-Math.log(hmm.getPi(i)) - Math.log(hmm.getStateDistribution(i).probability(oseq.get(0)));
this.psy[0][i] = 0;
}
final Iterator<MLDataPair> oseqIterator = oseq.iterator();
if (oseqIterator.hasNext()) {
oseqIterator.next();
}
int t = 1;
while (oseqIterator.hasNext()) {
final MLDataPair observation = oseqIterator.next();
for (int i = 0; i < hmm.getStateCount(); i++) {
computeStep(hmm, observation, t, i);
}
t++;
}
this.lnProbability = Double.MAX_VALUE;
for (int i = 0; i < hmm.getStateCount(); i++) {
final double thisProbability = this.delta[oseq.size() - 1][i];
if (this.lnProbability > thisProbability) {
this.lnProbability = thisProbability;
this.stateSequence[oseq.size() - 1] = i;
}
}
this.lnProbability = -this.lnProbability;
for (int t2 = oseq.size() - 2; t2 >= 0; t2--) {
this.stateSequence[t2] = this.psy[t2 + 1][this.stateSequence[t2 + 1]];
}
}
