Package org.apache.lucene.search.similarities

Class DefaultSimilarity

java.lang.Object
org.apache.lucene.search.similarities.Similarity
org.apache.lucene.search.similarities.TFIDFSimilarity
org.apache.lucene.search.similarities.DefaultSimilarity
public class DefaultSimilarity extends TFIDFSimilarity
Expert: Default scoring implementation which encodes norm values as a single byte before being stored. At search time, the norm byte value is read from the index directory and decoded back to a float norm value. This encoding/decoding, while reducing index size, comes with the price of precision loss - it is not guaranteed that decode(encode(x)) = x. For instance, decode(encode(0.89)) = 0.75.

Compression of norm values to a single byte saves memory at search time, because once a field is referenced at search time, its norms - for all documents - are maintained in memory.

The rationale supporting such lossy compression of norm values is that given the difficulty (and inaccuracy) of users to express their true information need by a query, only big differences matter.
 
Last, note that search time is too late to modify this norm part of scoring, e.g. by using a different Similarity for search.

  • Field Details

    • discountOverlaps

      protected boolean discountOverlaps
      True if overlap tokens (tokens with a position of increment of zero) are discounted from the document's length.

  • Constructor Details

    • DefaultSimilarity

      public DefaultSimilarity()
      Sole constructor: parameter-free

  • Method Details

    • coord

      public float coord(int overlap, int maxOverlap)
      Implemented as overlap / maxOverlap.
      Specified by:
      coord in class TFIDFSimilarity
      Parameters:
      overlap - the number of query terms matched in the document
      maxOverlap - the total number of terms in the query
      Returns:
      a score factor based on term overlap with the query

    • queryNorm

      public float queryNorm(float sumOfSquaredWeights)
      Implemented as 1/sqrt(sumOfSquaredWeights).
      Specified by:
      queryNorm in class TFIDFSimilarity
      Parameters:
      sumOfSquaredWeights - the sum of the squares of query term weights
      Returns:
      a normalization factor for query weights

    • encodeNormValue

      public final long encodeNormValue(float f)
      Encodes a normalization factor for storage in an index.

      The encoding uses a three-bit mantissa, a five-bit exponent, and the zero-exponent point at 15, thus representing values from around 7x10^9 to 2x10^-9 with about one significant decimal digit of accuracy. Zero is also represented. Negative numbers are rounded up to zero. Values too large to represent are rounded down to the largest representable value. Positive values too small to represent are rounded up to the smallest positive representable value.

      Specified by:
      encodeNormValue in class TFIDFSimilarity
      See Also:

    • decodeNormValue

      public final float decodeNormValue(long norm)
      Decodes the norm value, assuming it is a single byte.
      Specified by:
      decodeNormValue in class TFIDFSimilarity
      See Also:

    • lengthNorm

      public float lengthNorm(FieldInvertState state)
      Implemented as state.getBoost()*lengthNorm(numTerms), where numTerms is FieldInvertState.getLength() if setDiscountOverlaps(boolean) is false, else it's FieldInvertState.getLength() - FieldInvertState.getNumOverlap().
      Specified by:
      lengthNorm in class TFIDFSimilarity
      Parameters:
      state - statistics of the current field (such as length, boost, etc)
      Returns:
      an index-time normalization value

    • tf

      public float tf(float freq)
      Implemented as sqrt(freq).
      Specified by:
      tf in class TFIDFSimilarity
      Parameters:
      freq - the frequency of a term within a document
      Returns:
      a score factor based on a term's within-document frequency

    • sloppyFreq

      public float sloppyFreq(int distance)
      Implemented as 1 / (distance + 1).
      Specified by:
      sloppyFreq in class TFIDFSimilarity
      Parameters:
      distance - the edit distance of this sloppy phrase match
      Returns:
      the frequency increment for this match
      See Also:

    • scorePayload

      public float scorePayload(int doc, int start, int end, BytesRef payload)
      The default implementation returns 1
      Specified by:
      scorePayload in class TFIDFSimilarity
      Parameters:
      doc - The docId currently being scored.
      start - The start position of the payload
      end - The end position of the payload
      payload - The payload byte array to be scored
      Returns:
      An implementation dependent float to be used as a scoring factor

    • idf

      public float idf(long docFreq, long numDocs)
      Implemented as log(numDocs/(docFreq+1)) + 1.
      Specified by:
      idf in class TFIDFSimilarity
      Parameters:
      docFreq - the number of documents which contain the term
      numDocs - the total number of documents in the collection
      Returns:
      a score factor based on the term's document frequency

    • setDiscountOverlaps

      public void setDiscountOverlaps(boolean v)
      Determines whether overlap tokens (Tokens with 0 position increment) are ignored when computing norm. By default this is true, meaning overlap tokens do not count when computing norms.
      See Also:

    • getDiscountOverlaps

      public boolean getDiscountOverlaps()
      Returns true if overlap tokens are discounted from the document's length.
      See Also:

    • toString

      public String toString()
      Overrides:
      toString in class Object