/// <summary>
/// Constructor for enumeration of all terms from specified <c>reader</c> which share a prefix of
/// length <c>prefixLength</c> with <c>term</c> and which have a fuzzy similarity >
/// <c>minSimilarity</c>.
/// <para/>
/// After calling the constructor the enumeration is already pointing to the first
/// valid term if such a term exists.
/// </summary>
/// <exception cref="IOException">If there is a low-level I/O error.</exception>
public LinearFuzzyTermsEnum(SlowFuzzyTermsEnum outerInstance)
: base(outerInstance.m_terms.GetEnumerator())
{
this.outerInstance = outerInstance;
this.boostAtt = Attributes.AddAttribute <IBoostAttribute>();
this.text = new int[outerInstance.m_termLength - outerInstance.m_realPrefixLength];
System.Array.Copy(outerInstance.m_termText, outerInstance.m_realPrefixLength, text, 0, text.Length);
string prefix = UnicodeUtil.NewString(outerInstance.m_termText, 0, outerInstance.m_realPrefixLength);
prefixBytesRef = new BytesRef(prefix);
this.d = new int[this.text.Length + 1];
this.p = new int[this.text.Length + 1];
SetInitialSeekTerm(prefixBytesRef);
}
private void AddTerms(IndexReader reader, FieldVals f)
{
if (f.queryString == null)
{
return;
}
Terms terms = MultiFields.GetTerms(reader, f.fieldName);
if (terms == null)
{
return;
}
TokenStream ts = analyzer.GetTokenStream(f.fieldName, f.queryString);
try
{
ICharTermAttribute termAtt = ts.AddAttribute <ICharTermAttribute>();
int corpusNumDocs = reader.NumDocs;
ISet <string> processedTerms = new JCG.HashSet <string>();
ts.Reset();
while (ts.IncrementToken())
{
string term = termAtt.ToString();
if (!processedTerms.Contains(term))
{
processedTerms.Add(term);
ScoreTermQueue variantsQ = new ScoreTermQueue(MAX_VARIANTS_PER_TERM); //maxNum variants considered for any one term
float minScore = 0;
Term startTerm = new Term(f.fieldName, term);
AttributeSource atts = new AttributeSource();
IMaxNonCompetitiveBoostAttribute maxBoostAtt =
atts.AddAttribute <IMaxNonCompetitiveBoostAttribute>();
#pragma warning disable 612, 618
SlowFuzzyTermsEnum fe = new SlowFuzzyTermsEnum(terms, atts, startTerm, f.minSimilarity, f.prefixLength);
#pragma warning restore 612, 618
//store the df so all variants use same idf
int df = reader.DocFreq(startTerm);
int numVariants = 0;
int totalVariantDocFreqs = 0;
BytesRef possibleMatch;
IBoostAttribute boostAtt =
fe.Attributes.AddAttribute <IBoostAttribute>();
while ((possibleMatch = fe.Next()) != null)
{
numVariants++;
totalVariantDocFreqs += fe.DocFreq;
float score = boostAtt.Boost;
if (variantsQ.Count < MAX_VARIANTS_PER_TERM || score > minScore)
{
ScoreTerm st = new ScoreTerm(new Term(startTerm.Field, BytesRef.DeepCopyOf(possibleMatch)), score, startTerm);
variantsQ.InsertWithOverflow(st);
minScore = variantsQ.Top.Score; // maintain minScore
}
maxBoostAtt.MaxNonCompetitiveBoost = variantsQ.Count >= MAX_VARIANTS_PER_TERM ? minScore : float.NegativeInfinity;
}
if (numVariants > 0)
{
int avgDf = totalVariantDocFreqs / numVariants;
if (df == 0) //no direct match we can use as df for all variants
{
df = avgDf; //use avg df of all variants
}
// take the top variants (scored by edit distance) and reset the score
// to include an IDF factor then add to the global queue for ranking
// overall top query terms
int size = variantsQ.Count;
for (int i = 0; i < size; i++)
{
ScoreTerm st = variantsQ.Pop();
st.Score = (st.Score * st.Score) * sim.Idf(df, corpusNumDocs);
q.InsertWithOverflow(st);
}
}
}
}
ts.End();
}
finally
{
IOUtils.DisposeWhileHandlingException(ts);
}
}
