Hi,
this is a re-post, because the first time I re-used another thread
(sorry for any inconvenience):
this is my first post to this mailing list, so I first want to say hello
to all of you!
You are doing a great job
In org.apache.lucene.search.FuzzyTermEnum I found an optimised
implementation of the Levenstein-Algorithms which makes use of the fact
that the algorithm can be aborted if a given minimum similarity cannot
be reached anymore. I isolated that algorithm into a subclass of
org.apache.lucene.spell.StringDistance, since we usually can make use of
this optimisation.
With our current miminum similarity setting of 0.75 this algorithm needs
against our test data only about 22% of run time compared to the
original algorithm from the spell package.
With a further optimisation candidate (see below) the runtime can be
further reduced by another third to only 14% of original Levenstein.
So, my first question is: is it worth adding a further method to the
StringDistance-Interface:
float getDistance(String left, String right, float
minimumSimilarity)
so that applications can make use of possible optimisations
(StringDistance-Implementations without optimisations would just skip
the minimSimilarity parameter)?
The idea of the optimsation candidate is about calculating only those
fields in the "virtual" matrix that are near its diagonal.
It is only a candidants since we have not prooven it to work. But with
all our test data (0.5 billion comparisons) there is no difference to
the original algorithm.
Do you have any counter examples?
Since this is my first post, is this the right mailing list?
Best Regards,
Sven
Here is the code taken from FuzzyTermEnum with some modfications (p and
d are initialised somewhere else):
public float getDistance(final String left, final String right,
float minimumSimilarity) {
if (left.length() > right.length()) // candidate works only if
longer string is right
return getDistanceInner(right, left, minimumSimilarity);
else
return getDistanceInner(left, right, minimumSimilarity);
}
private float getDistanceInner(final String left, final String
right, float minimumSimilarity) {
final int m = right.length();
final int n = left.length();
final int maxLength = Math.max(m, n);
if (n == 0) {
//we don't have anything to compare. That means if we just
add
//the letters for m we get the new word
return (m == 0) ? 1f : 0f;
}
if (m == 0) {
return 0f;
}
// be patient with rounding errors (1.0000001f instead of 1f).
final int maxDistance = (int) ((1.0000001f-minimumSimilarity) *
maxLength);
if (maxDistance < Math.abs(m-n)) {
//just adding the characters of m to n or vice-versa results
in
//too many edits
//for example "pre" length is 3 and "prefixes" length is 8.
We can see that
//given this optimal circumstance, the edit distance cannot be
less than 5.
//which is 8-3 or more precisely Math.abs(3-8).
//if our maximum edit distance is 4, then we can discard this
word
//without looking at it.
return 0.0f;
}
// if no edits are allowed, strings must be equal
if (maxDistance == 0)
return left.equals(right) ? 1f : 0f;
// init matrix d
for (int i = 0; i<=n; i++) {
p[i] = i;
}
// start computing edit distance
for (int j = 1; j<=m; j++) { // iterates through target
int bestPossibleEditDistance = m;
final char t_j = right.charAt(j-1); // jth character of t
d[0] = j;
//-------> here is the optimisation candiates
//only iterate through a maxDistance corridor
final int startAt = Math.max(1, j - maxDistance );
final int finishAt = Math.min(n, maxDistance - 1 + j);
for (int i=startAt; i<=finishAt; ++i) { // iterates through
text
//--------
// minimum of cell to the left+1, to the top+1, diagonally
left and up +(0|1)
final char t_i = left.charAt(i-1);
if (t_j != t_i) {
d[i] = Math.min(Math.min(d[i-1], p[i]), p[i-1]) + 1;
} else {
d[i] = Math.min(Math.min(d[i-1], p[i]) + 1, p[i-1]);
}
bestPossibleEditDistance =
Math.min(bestPossibleEditDistance, d[i]);
}
//After calculating row i, the best possible edit distance
//can be found by found by finding the smallest value in a
given column.
//If the bestPossibleEditDistance is greater than the max
distance, abort.
if (j > maxDistance && bestPossibleEditDistance > maxDistance)
{ //equal is okay, but not greater
//the closest the target can be to the text is just too far
away.
//this target is leaving the party early.
return 0.0f;
}
// copy current distance counts to 'previous row' distance
counts: swap p and d
int _d[] = p;
p = d;
d = _d;
}
// our last action in the above loop was to switch d and p, so p
now
// actually has the most recent cost counts
// this will return less than 0.0 when the edit distance is
// greater than the number of characters in the shorter word.
// but this was the formula that was previously used in
FuzzyTermEnum,
// so it has not been changed (even though minimumSimilarity
must be
// greater than 0.0)
return 1.0f - ((float)p[n] / (float) (maxLength));
}
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