abstract public float coord(int overlap,
    int maxOverlap)
Computes a score factor based on the fraction of all query terms that a
document contains. This value is multiplied into scores.

The presence of a large portion of the query terms indicates a better
match with the query, so implementations of this method usually return
larger values when the ratio between these parameters is large and smaller
values when the ratio between them is small.

 public static float decodeNorm(byte b) 
{
   
    for (int i = 0; i <  256; i++)
      NORM_TABLE[i] = SmallFloat.byte315ToFloat((byte)i);
    return NORM_TABLE[b & 0xFF];  // & 0xFF maps negative bytes to positive above 127
}

Decodes a normalization factor stored in an index.

 public static byte encodeNorm(float f) 
{
    return SmallFloat.floatToByte315(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.

 public static Similarity getDefault() 
{
    return Similarity.defaultImpl;
}

Return the default Similarity implementation used by indexing and search code.

This is initially an instance of DefaultSimilarity .

 public static float[] getNormDecoder() 
{
    return NORM_TABLE;
}

Returns a table for decoding normalization bytes.

 public float idf(Term term,
    Searcher searcher) throws IOException 
{
    return idf(searcher.docFreq(term), searcher.maxDoc());
}

Computes a score factor for a simple term.

The default implementation is:

return idf(searcher.docFreq(term), searcher.maxDoc());

Note that Searcher#maxDoc() is used instead of org.apache.lucene.index.IndexReader#numDocs() because it is proportional to Searcher#docFreq(Term) , i.e., when one is inaccurate, so is the other, and in the same direction.

 public float idf(Collection terms,
    Searcher searcher) throws IOException 
{
    float idf = 0.0f;
    Iterator i = terms.iterator();
    while (i.hasNext()) {
      idf += idf((Term)i.next(), searcher);
    }
    return idf;
}

Computes a score factor for a phrase.

The default implementation sums the #idf(Term,Searcher) factor for each term in the phrase.

 abstract public float idf(int docFreq,
    int numDocs)
Computes a score factor based on a term's document frequency (the number
of documents which contain the term). This value is multiplied by the
#tf(int)  factor for each term in the query and these products are
then summed to form the initial score for a document.

Terms that occur in fewer documents are better indicators of topic, so
implementations of this method usually return larger values for rare terms,
and smaller values for common terms.

 abstract public float lengthNorm(String fieldName,
    int numTokens)
Computes the normalization value for a field given the total number of
terms contained in a field. These values, together with field boosts, are
stored in an index and multipled into scores for hits on each field by the
search code.

Matches in longer fields are less precise, so implementations of this
method usually return smaller values when numTokens is large,
and larger values when numTokens is small.

That these values are computed under
org.apache.lucene.index.IndexWriter#addDocument(org.apache.lucene.document.Document) 
and stored then using
#encodeNorm(float) .
Thus they have limited precision, and documents
must be re-indexed if this method is altered.

 abstract public float queryNorm(float sumOfSquaredWeights)
Computes the normalization value for a query given the sum of the squared
weights of each of the query terms. This value is then multipled into the
weight of each query term.

This does not affect ranking, but rather just attempts to make scores
from different queries comparable.

 public float scorePayload(String fieldName,
    byte[] payload,
    int offset,
    int length) 
{
    //Do nothing
    return 1;
}

Calculate a scoring factor based on the data in the payload. Overriding implementations are responsible for interpreting what is in the payload. Lucene makes no assumptions about what is in the byte array.

The default implementation returns 1.

 public static  void setDefault(Similarity similarity) 
{
                    
    Similarity.defaultImpl = similarity;
}

Set the default Similarity implementation used by indexing and search code.

 abstract public float sloppyFreq(int distance)
Computes the amount of a sloppy phrase match, based on an edit distance.
This value is summed for each sloppy phrase match in a document to form
the frequency that is passed to #tf(float) .

A phrase match with a small edit distance to a document passage more
closely matches the document, so implementations of this method usually
return larger values when the edit distance is small and smaller values
when it is large.

 public float tf(int freq) 
{
    return tf((float)freq);
}

Computes a score factor based on a term or phrase's frequency in a document. This value is multiplied by the #idf(Term, Searcher) factor for each term in the query and these products are then summed to form the initial score for a document.

Terms and phrases repeated in a document indicate the topic of the document, so implementations of this method usually return larger values when freq is large, and smaller values when freq is small.

The default implementation calls #tf(float) .

 abstract public float tf(float freq)
Computes a score factor based on a term or phrase's frequency in a
document. This value is multiplied by the #idf(Term, Searcher) 
factor for each term in the query and these products are then summed to
form the initial score for a document.

Terms and phrases repeated in a document indicate the topic of the
document, so implementations of this method usually return larger values
when freq is large, and smaller values when freq
is small.