Since fuzzy searching is kind of slow, I took a look at it to see if it could be improved. I saw speed improvements of 10% - 60% by making a couple changes. Along the way I fixed a potential bug or two that I saw.
The patch is here: http://www.hagerfamily.com/patches/FuzzyTermEnumOptimizePatch.txt I've never submitted a patch before, so don't flame me if I do or say anything wrong. What Changed? Since the word was discarded if the edit distance for the word was above a certain threshold, I updated the distance algorithm to abort if at any time during the calculation it is determined that the best possible outcome of the edit distance algorithm is above this threshold. The source code has a great explanation. I also reduced the amount of floating point math, reduced the amount of potential space the array takes in its first dimension, removed the potential divide by 0 error when one term is an empty string, and fixed a bug where an IllegalArgumentException was thrown if the class was somehow initialized wrong, instead of looking at the arguments. The behavior is almost identical. The exception is that similarity is set to 0.0 when it is guaranteed to be below the minimum similarity. Results I saw the biggest improvement from longer words, which makes a sense. My long word was "bridgetown" and I saw a 60% improvement on this. The biggest improvement are for words that are farthest away from the median length of the words in the index. Short words (1-3 characters) saw a 30% improvement. Medium words saw a 10% improvement (5-7 characters). These improvements are with the prefix set to 0. Would someone be willing to validate that they see similar results? Especially on large indexes. Other Questions I am still wondering about two things. 1) Why can't minimumSimilarity be less than 0? Similarity may be negative, especially for small words. 2) Why does the formula for similarity not return a number between 0.0 (no common characters) and 1.0 (identical) inclusive? This would be easy to do, just use Math.max() instead of Math.min(). Of course this would change the order of the results. As an example the similarity for "to" and "todor" = 1 - 3/2 = -0.5 "tod" and "for" = 1 - 2/3 = +0.33 Jonathan --------------------------------------------------------------------- To unsubscribe, e-mail: [EMAIL PROTECTED] For additional commands, e-mail: [EMAIL PROTECTED]
