final class SimilarityBase.BasicSimScorer extends Similarity.SimScorer
score(float, long) and
explain(Explanation, long) methods to
SimilarityBase.score(BasicStats, double, double) and
SimilarityBase.explain(BasicStats, Explanation, double),
respectively.| Modifier and Type | Field and Description |
|---|---|
(package private) BasicStats |
stats |
| Constructor and Description |
|---|
BasicSimScorer(BasicStats stats) |
| Modifier and Type | Method and Description |
|---|---|
Explanation |
explain(Explanation freq,
long norm)
Explain the score for a single document
|
(package private) double |
getLengthValue(long norm) |
float |
score(float freq,
long norm)
Score a single document.
|
final BasicStats stats
BasicSimScorer(BasicStats stats)
double getLengthValue(long norm)
public float score(float freq,
long norm)
Similarity.SimScorerfreq is the document-term sloppy
frequency and must be finite and positive. norm is the
encoded normalization factor as computed by
Similarity.computeNorm(FieldInvertState) at index time, or
1 if norms are disabled. norm is never 0.
Score must not decrease when freq increases, ie. if
freq1 > freq2, then score(freq1, norm) >=
score(freq2, norm) for any value of norm that may be produced
by Similarity.computeNorm(FieldInvertState).
Score must not increase when the unsigned norm increases, ie. if
Long.compareUnsigned(norm1, norm2) > 0 then
score(freq, norm1) <= score(freq, norm2) for any legal
freq.
As a consequence, the maximum score that this scorer can produce is bound
by score(Float.MAX_VALUE, 1).
score in class Similarity.SimScorerfreq - sloppy term frequency, must be finite and positivenorm - encoded normalization factor or 1 if norms are disabledpublic Explanation explain(Explanation freq, long norm)
Similarity.SimScorerexplain in class Similarity.SimScorerfreq - Explanation of how the sloppy term frequency was computednorm - encoded normalization factor, as returned by Similarity.computeNorm(org.apache.lucene.index.FieldInvertState), or 1 if norms are disabled