/**
* The termCompare method in FuzzyTermEnum uses Levenshtein distance to calculate the distance
* between the given term and the comparing term.
*
* <p>If the minSimilarity is >= 1.0, this uses the maxEdits as the comparison. Otherwise,
* this method uses the following logic to calculate similarity.
*
* <pre>
* similarity = 1 - ((float)distance / (float) (prefixLength + Math.min(textlen, targetlen)));
* </pre>
*
* where distance is the Levenshtein distance for the two words.
*/
@Override
protected final AcceptStatus accept(BytesRef term) {
if (StringHelper.startsWith(term, prefixBytesRef)) {
utf32.copyUTF8Bytes(term);
final int distance =
calcDistance(utf32.ints(), realPrefixLength, utf32.length() - realPrefixLength);
// Integer.MIN_VALUE is the sentinel that Levenshtein stopped early
if (distance == Integer.MIN_VALUE) {
return AcceptStatus.NO;
}
// no need to calc similarity, if raw is true and distance > maxEdits
if (raw == true && distance > maxEdits) {
return AcceptStatus.NO;
}
final float similarity =
calcSimilarity(distance, (utf32.length() - realPrefixLength), text.length);
// if raw is true, then distance must also be <= maxEdits by now
// given the previous if statement
if (raw == true || (raw == false && similarity > minSimilarity)) {
boostAtt.setBoost((similarity - minSimilarity) * scale_factor);
return AcceptStatus.YES;
} else {
return AcceptStatus.NO;
}
} else {
return AcceptStatus.END;
}
}
private UserDictionary(List<String[]> featureEntries) throws IOException {
int wordId = CUSTOM_DICTIONARY_WORD_ID_OFFSET;
// TODO: should we allow multiple segmentations per input 'phrase'?
// the old treemap didn't support this either, and i'm not sure if it's needed/useful?
Collections.sort(
featureEntries,
new Comparator<String[]>() {
@Override
public int compare(String[] left, String[] right) {
return left[0].compareTo(right[0]);
}
});
List<String> data = new ArrayList<>(featureEntries.size());
List<int[]> segmentations = new ArrayList<>(featureEntries.size());
PositiveIntOutputs fstOutput = PositiveIntOutputs.getSingleton();
Builder<Long> fstBuilder = new Builder<>(FST.INPUT_TYPE.BYTE2, fstOutput);
IntsRefBuilder scratch = new IntsRefBuilder();
long ord = 0;
for (String[] values : featureEntries) {
String[] segmentation = values[1].replaceAll(" *", " ").split(" ");
String[] readings = values[2].replaceAll(" *", " ").split(" ");
String pos = values[3];
if (segmentation.length != readings.length) {
throw new RuntimeException(
"Illegal user dictionary entry "
+ values[0]
+ " - the number of segmentations ("
+ segmentation.length
+ ")"
+ " does not the match number of readings ("
+ readings.length
+ ")");
}
int[] wordIdAndLength = new int[segmentation.length + 1]; // wordId offset, length, length....
wordIdAndLength[0] = wordId;
for (int i = 0; i < segmentation.length; i++) {
wordIdAndLength[i + 1] = segmentation[i].length();
data.add(readings[i] + INTERNAL_SEPARATOR + pos);
wordId++;
}
// add mapping to FST
String token = values[0];
scratch.grow(token.length());
scratch.setLength(token.length());
for (int i = 0; i < token.length(); i++) {
scratch.setIntAt(i, (int) token.charAt(i));
}
fstBuilder.add(scratch.get(), ord);
segmentations.add(wordIdAndLength);
ord++;
}
this.fst = new TokenInfoFST(fstBuilder.finish(), false);
this.data = data.toArray(new String[data.size()]);
this.segmentations = segmentations.toArray(new int[segmentations.size()][]);
}