alessandrobenedetti commented on code in PR #926:
URL: https://github.com/apache/lucene/pull/926#discussion_r883955103
##########
lucene/core/src/java/org/apache/lucene/index/VectorSimilarityFunction.java:
##########
@@ -47,63 +42,30 @@ public float convertToScore(float similarity) {
DOT_PRODUCT {
@Override
public float compare(float[] v1, float[] v2) {
- return dotProduct(v1, v2);
- }
-
- @Override
- public float convertToScore(float similarity) {
- return (1 + similarity) / 2;
+ return (1 + dotProduct(v1, v2)) / 2;
}
},
/**
* Cosine similarity. NOTE: the preferred way to perform cosine similarity
is to normalize all
* vectors to unit length, and instead use {@link
VectorSimilarityFunction#DOT_PRODUCT}. You
* should only use this function if you need to preserve the original
vectors and cannot normalize
- * them in advance.
+ * them in advance. The similarity score is normalised to assure it is
positive.
*/
COSINE {
@Override
public float compare(float[] v1, float[] v2) {
- return cosine(v1, v2);
- }
-
- @Override
- public float convertToScore(float similarity) {
- return (1 + similarity) / 2;
+ return (1 + cosine(v1, v2)) / 2;
}
};
/**
- * If true, the scores associated with vector comparisons are nonnegative
and in reverse order;
- * that is, lower scores represent more similar vectors. Otherwise, if
false, higher scores
- * represent more similar vectors, and scores may be negative or positive.
- */
- public final boolean reversed;
-
- VectorSimilarityFunction(boolean reversed) {
- this.reversed = reversed;
- }
-
- VectorSimilarityFunction() {
- reversed = false;
- }
-
- /**
- * Calculates a similarity score between the two vectors with a specified
function.
+ * Calculates a similarity score between the two vectors with a specified
function. Higher
+ * similarity scores correspond to closer vectors.
*
* @param v1 a vector
* @param v2 another vector, of the same dimension
* @return the value of the similarity function applied to the two vectors
*/
public abstract float compare(float[] v1, float[] v2);
Review Comment:
should we call it 'similarity'?
if we want to name the method with a verb maybe:
getSimilarity?
scoreSimilarity?
computeSimilarity?
I think they are almost synonyms here but in general, I think we are using
the similarity as a score.
Not a strong opinion anyway, happy to change it
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