How to use

org.apache.lucene.search.PhraseQueue

constructor

SloppyPhraseMatcher(PhraseQuery.PostingsAndFreq[] postings, int slop, float matchCost, boolean captureLeadMatch) {
 super(approximation(postings), matchCost);
 this.slop = slop;
 this.numPostings = postings.length;
 this.captureLeadMatch = captureLeadMatch;
 pq = new PhraseQueue(postings.length);
 phrasePositions = new PhrasePositions[postings.length];
 for (int i = 0; i < postings.length; ++i) {
  phrasePositions[i] = new PhrasePositions(postings[i].postings, postings[i].position, i, postings[i].terms);
 }
}

SloppyPhraseScorer(Weight weight, PhraseQuery.PostingsAndFreq[] postings,
  int slop, Similarity.SimScorer docScorer, boolean needsScores,
  float matchCost) {
 super(weight);
 this.docScorer = docScorer;
 this.needsScores = needsScores;
 this.slop = slop;
 this.numPostings = postings==null ? 0 : postings.length;
 pq = new PhraseQueue(postings.length);
 DocIdSetIterator[] iterators = new DocIdSetIterator[postings.length];
 phrasePositions = new PhrasePositions[postings.length];
 for (int i = 0; i < postings.length; ++i) {
  iterators[i] = postings[i].postings;
  phrasePositions[i] = new PhrasePositions(postings[i].postings, postings[i].position, i, postings[i].terms);
 }
 conjunction = ConjunctionDISI.intersectIterators(Arrays.asList(iterators));
 this.matchCost = matchCost;
}

SloppyPhraseMatcher(PhraseQuery.PostingsAndFreq[] postings, int slop, float matchCost, boolean captureLeadMatch) {
 super(approximation(postings), matchCost);
 this.slop = slop;
 this.numPostings = postings.length;
 this.captureLeadMatch = captureLeadMatch;
 pq = new PhraseQueue(postings.length);
 phrasePositions = new PhrasePositions[postings.length];
 for (int i = 0; i < postings.length; ++i) {
  phrasePositions[i] = new PhrasePositions(postings[i].postings, postings[i].position, i, postings[i].terms);
 }
}

SloppyPhraseScorer(Weight weight, PhraseQuery.PostingsAndFreq[] postings,
  int slop, Similarity.SimScorer docScorer, boolean needsScores,
  float matchCost) {
 super(weight);
 this.docScorer = docScorer;
 this.needsScores = needsScores;
 this.slop = slop;
 this.numPostings = postings==null ? 0 : postings.length;
 pq = new PhraseQueue(postings.length);
 DocIdSetIterator[] iterators = new DocIdSetIterator[postings.length];
 phrasePositions = new PhrasePositions[postings.length];
 for (int i = 0; i < postings.length; ++i) {
  iterators[i] = postings[i].postings;
  phrasePositions[i] = new PhrasePositions(postings[i].postings, postings[i].position, i, postings[i].terms);
 }
 conjunction = ConjunctionDISI.intersectIterators(Arrays.asList(iterators));
 this.matchCost = matchCost;
}

PhraseScorer(Weight weight, TermPositions[] tps, int[] positions, Similarity similarity,
       byte[] norms) {
 super(similarity);
 this.norms = norms;
 this.weight = weight;
 this.value = weight.getValue();
 // convert tps to a list
 for (int i = 0; i < tps.length; i++) {
  PhrasePositions pp = new PhrasePositions(tps[i], positions[i]);
  if (last != null) {			  // add next to end of list
   last.next = pp;
  } else
   first = pp;
  last = pp;
 }
 pq = new PhraseQueue(tps.length);             // construct empty pq
}

PhraseScorer(Weight weight, TermPositions[] tps, int[] offsets, Similarity similarity,
       byte[] norms) {
 super(similarity);
 this.norms = norms;
 this.weight = weight;
 this.value = weight.getValue();
 // convert tps to a list of phrase positions.
 // note: phrase-position differs from term-position in that its position
 // reflects the phrase offset: pp.pos = tp.pos - offset.
 // this allows to easily identify a matching (exact) phrase 
 // when all PhrasePositions have exactly the same position.
 for (int i = 0; i < tps.length; i++) {
  PhrasePositions pp = new PhrasePositions(tps[i], offsets[i]);
  if (last != null) {			  // add next to end of list
   last.next = pp;
  } else
   first = pp;
  last = pp;
 }
 pq = new PhraseQueue(tps.length);             // construct empty pq
}

PhraseScorer(Weight weight, TermPositions[] tps, int[] offsets, Similarity similarity,
       byte[] norms) {
 super(similarity);
 this.norms = norms;
 this.weight = weight;
 this.value = weight.getValue();
 // convert tps to a list of phrase positions.
 // note: phrase-position differs from term-position in that its position
 // reflects the phrase offset: pp.pos = tp.pos - offset.
 // this allows to easily identify a matching (exact) phrase 
 // when all PhrasePositions have exactly the same position.
 for (int i = 0; i < tps.length; i++) {
  PhrasePositions pp = new PhrasePositions(tps[i], offsets[i]);
  if (last != null) {			  // add next to end of list
   last.next = pp;
  } else
   first = pp;
  last = pp;
 }
 pq = new PhraseQueue(tps.length);             // construct empty pq
}