public NgramLanguageModel(
Indexer<String> charIndexer,
CountDbBig[] countDbs,
Set<Integer> activeCharacters,
LMType type,
double lmPower) {
this.charIndexer = charIndexer;
this.countDbs = countDbs;
this.maxOrder = countDbs.length;
this.type = type;
this.lmPower = lmPower;
this.allContextsSet = new HashSet<LongArrWrapper>();
this.allContexts = new ArrayList<int[]>();
for (int i = 0; i < this.maxOrder - 1; i++) {
for (long[] key : countDbs[i].getKeys()) {
if (key != null && countDbs[i].getCount(key, CountType.HISTORY_TYPE_INDEX) > 0) {
allContextsSet.add(new LongArrWrapper(key));
allContexts.add(LongNgram.convertToIntArr(key));
}
}
}
if (activeCharacters == null) throw new RuntimeException("activeCharacters is null!");
this.activeCharacters = activeCharacters;
}
public void checkNormalizes(int[] context) {
double totalProb = 0;
for (int i = 0; i < charIndexer.size(); i++) {
totalProb += getCharNgramProb(context, i);
}
System.out.println(
"Total prob for context " + LongNgram.toString(context, charIndexer) + ": " + totalProb);
}
public boolean containsContext(int[] context) {
if (context.length == 0) return true;
else return allContextsSet.contains(new LongArrWrapper(LongNgram.convertToLong(context)));
}
