public override Query Rewrite(IndexReader reader)
{
FilteredTermEnum enumerator = GetEnum(reader);
int maxClauseCount = BooleanQuery.GetMaxClauseCount();
ScoreTermQueue stQueue = new ScoreTermQueue(maxClauseCount);
try
{
do
{
float minScore = 0.0f;
float score = 0.0f;
Term t = enumerator.Term();
if (t != null)
{
score = enumerator.Difference();
// terms come in alphabetical order, therefore if queue is full and score
// not bigger than minScore, we can skip
if (stQueue.Size() < maxClauseCount || score > minScore)
{
stQueue.Insert(new ScoreTerm(t, score));
minScore = ((ScoreTerm)stQueue.Top()).score; // maintain minScore
}
}
}while (enumerator.Next());
}
finally
{
enumerator.Close();
}
BooleanQuery query = new BooleanQuery(true);
int size = stQueue.Size();
for (int i = 0; i < size; i++)
{
ScoreTerm st = (ScoreTerm)stQueue.Pop();
TermQuery tq = new TermQuery(st.term); // found a match
tq.SetBoost(GetBoost() * st.score); // set the boost
query.Add(tq, BooleanClause.Occur.SHOULD); // add to query
}
return(query);
}
private void AddTerms(IndexReader reader, FieldVals f)
{
if (f.queryString == null)
{
return;
}
TokenStream ts = analyzer.TokenStream(f.fieldName, new System.IO.StringReader(f.queryString));
TermAttribute termAtt = (TermAttribute)ts.AddAttribute(typeof(TermAttribute));
int corpusNumDocs = reader.NumDocs();
Term internSavingTemplateTerm = new Term(f.fieldName); //optimization to avoid constructing new Term() objects
Hashtable processedTerms = new Hashtable();
while (ts.IncrementToken())
{
String term = termAtt.Term();
if (!processedTerms.Contains(term))
{
processedTerms.Add(term, term);
ScoreTermQueue variantsQ = new ScoreTermQueue(MAX_VARIANTS_PER_TERM); //maxNum variants considered for any one term
float minScore = 0;
Term startTerm = internSavingTemplateTerm.CreateTerm(term);
FuzzyTermEnum fe = new FuzzyTermEnum(reader, startTerm, f.minSimilarity, f.prefixLength);
TermEnum origEnum = reader.Terms(startTerm);
int df = 0;
if (startTerm.Equals(origEnum.Term()))
{
df = origEnum.DocFreq(); //store the df so all variants use same idf
}
int numVariants = 0;
int totalVariantDocFreqs = 0;
do
{
Term possibleMatch = fe.Term();
if (possibleMatch != null)
{
numVariants++;
totalVariantDocFreqs += fe.DocFreq();
float score = fe.Difference();
if (variantsQ.Size() < MAX_VARIANTS_PER_TERM || score > minScore)
{
ScoreTerm st = new ScoreTerm(possibleMatch, score, startTerm);
variantsQ.Insert(st);
minScore = ((ScoreTerm)variantsQ.Top()).score; // maintain minScore
}
}
}while (fe.Next());
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.Size();
for (int i = 0; i < size; i++)
{
ScoreTerm st = (ScoreTerm)variantsQ.Pop();
st.score = (st.score * st.score) * sim.Idf(df, corpusNumDocs);
q.Insert(st);
}
}
}
}
}
public override Query Rewrite(IndexReader reader)
{
FilteredTermEnum enumerator = GetEnum(reader);
int maxClauseCount = BooleanQuery.GetMaxClauseCount();
ScoreTermQueue stQueue = new ScoreTermQueue(maxClauseCount);
try
{
do
{
float minScore = 0.0f;
float score = 0.0f;
Term t = enumerator.Term();
if (t != null)
{
score = enumerator.Difference();
// terms come in alphabetical order, therefore if queue is full and score
// not bigger than minScore, we can skip
if (stQueue.Size() < maxClauseCount || score > minScore)
{
stQueue.Insert(new ScoreTerm(t, score));
minScore = ((ScoreTerm) stQueue.Top()).score; // maintain minScore
}
}
}
while (enumerator.Next());
}
finally
{
enumerator.Close();
}
BooleanQuery query = new BooleanQuery(true);
int size = stQueue.Size();
for (int i = 0; i < size; i++)
{
ScoreTerm st = (ScoreTerm) stQueue.Pop();
TermQuery tq = new TermQuery(st.term); // found a match
tq.SetBoost(GetBoost() * st.score); // set the boost
query.Add(tq, BooleanClause.Occur.SHOULD); // add to query
}
return query;
}