Class MultiSimilarity


public class MultiSimilarity extends Similarity
Implements the CombSUM method for combining evidence from multiple similarity values described in: Joseph A. Shaw, Edward A. Fox. In Text REtrieval Conference (1993), pp. 243-252
WARNING: This API is experimental and might change in incompatible ways in the next release.
  • Field Details

    • sims

      protected final Similarity[] sims
      the sub-similarities used to create the combined score
  • Constructor Details

    • MultiSimilarity

      public MultiSimilarity(Similarity[] sims)
      Creates a MultiSimilarity which will sum the scores of the provided sims.
  • Method Details

    • computeNorm

      public long computeNorm(FieldInvertState state)
      Description copied from class: Similarity
      Computes the normalization value for a field, given the accumulated state of term processing for this field (see FieldInvertState).

      Matches in longer fields are less precise, so implementations of this method usually set smaller values when state.getLength() is large, and larger values when state.getLength() is small.

      Note that for a given term-document frequency, greater unsigned norms must produce scores that are lower or equal, ie. for two encoded norms n1 and n2 so that Long.compareUnsigned(n1, n2) > 0 then SimScorer.score(freq, n1) <= SimScorer.score(freq, n2) for any legal freq.

      0 is not a legal norm, so 1 is the norm that produces the highest scores.

      Specified by:
      computeNorm in class Similarity
      state - current processing state for this field
      computed norm value
    • scorer

      public Similarity.SimScorer scorer(float boost, CollectionStatistics collectionStats, TermStatistics... termStats)
      Description copied from class: Similarity
      Compute any collection-level weight (e.g. IDF, average document length, etc) needed for scoring a query.
      Specified by:
      scorer in class Similarity
      boost - a multiplicative factor to apply to the produces scores
      collectionStats - collection-level statistics, such as the number of tokens in the collection.
      termStats - term-level statistics, such as the document frequency of a term across the collection.
      SimWeight object with the information this Similarity needs to score a query.