C# Search.Weight类代码示例

			internal MatchAllScorer(MatchAllDocsQuery enclosingInstance, IndexReader reader, Similarity similarity, Weight w, byte[] norms):base(similarity)
			{
				InitBlock(enclosingInstance);
				this.termDocs = reader.TermDocs(null);
				score = w.Value;
				this.norms = norms;
			}

 internal Hits(Searcher s, Query q, Filter f)
 {
     weight = q.Weight(s);
     searcher = s;
     filter = f;
     GetMoreDocs(50); // retrieve 100 initially
 }

        internal ExactPhraseScorer(Weight weight, PhraseQuery.PostingsAndFreq[] postings, Similarity.SimScorer docScorer)
            : base(weight)
        {
            this.DocScorer = docScorer;

            ChunkStates = new ChunkState[postings.Length];

            EndMinus1 = postings.Length - 1;

            // min(cost)
            Cost_Renamed = postings[0].Postings.Cost();

            for (int i = 0; i < postings.Length; i++)
            {
                // Coarse optimization: advance(target) is fairly
                // costly, so, if the relative freq of the 2nd
                // rarest term is not that much (> 1/5th) rarer than
                // the first term, then we just use .nextDoc() when
                // ANDing.  this buys ~15% gain for phrases where
                // freq of rarest 2 terms is close:
                bool useAdvance = postings[i].DocFreq > 5 * postings[0].DocFreq;
                ChunkStates[i] = new ChunkState(postings[i].Postings, -postings[i].Position, useAdvance);
                if (i > 0 && postings[i].Postings.NextDoc() == DocIdSetIterator.NO_MORE_DOCS)
                {
                    NoDocs = true;
                    return;
                }
            }
        }

		private float freq; //prhase frequency in current doc as computed by phraseFreq().
		
		internal PhraseScorer(Weight weight, TermPositions[] tps, int[] offsets, Similarity similarity, byte[] norms):base(similarity)
		{
			this.norms = norms;
			this.weight = weight;
			this.value_Renamed = weight.Value;
			
			// 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
			first.doc = - 1;
		}

 protected internal DisjunctionScorer(Weight weight, Scorer[] subScorers)
     : base(weight)
 {
     this.SubScorers = subScorers;
     this.NumScorers = subScorers.Length;
     Heapify();
 }

        private void SearchWithFilter(IndexReader reader, Weight weight, Filter filter, Collector collector)
        {
            DocIdSet docIdSet = filter.GetDocIdSet(reader);
            if (docIdSet == null)
                return;
            Scorer scorer = weight.Scorer(reader, true, false);
            if (scorer == null)
                return;
            scorer.DocID();

            DocIdSetIterator docIdSetIterator = docIdSet.Iterator();
            if (docIdSetIterator == null)
                return;
            int target = docIdSetIterator.NextDoc();
            int num = scorer.Advance(target);
            collector.SetScorer(scorer);
            while (true)
            {
                while (num != target)
                {
                    if (num > target)
                        target = docIdSetIterator.Advance(num);
                    else
                        num = scorer.Advance(target);
                }
                if (num != DocIdSetIterator.NO_MORE_DOCS && !((GroupCollector)collector).GroupLimitReached)
                {
                    collector.Collect(num);
                    target = docIdSetIterator.NextDoc();
                    num = scorer.Advance(target);
                }
                else
                    break;
            }
        }

			internal MatchAllScorer(MatchAllDocsQuery enclosingInstance, IndexReader reader, Similarity similarity, Weight w):base(similarity)
			{
				InitBlock(enclosingInstance);
				this.reader = reader;
				id = - 1;
				maxId = reader.MaxDoc() - 1;
				score = w.GetValue();
			}

 internal MatchAllScorer(MatchAllDocsQuery outerInstance, IndexReader reader, Bits liveDocs, Weight w, float score)
     : base(w)
 {
     this.OuterInstance = outerInstance;
     this.LiveDocs = liveDocs;
     this.Score_Renamed = score;
     MaxDoc = reader.MaxDoc;
 }

		public /*internal*/ bool debugCheckedForDeletions = false; // for test purposes.
		
		internal Hits(Searcher s, Query q, Filter f)
		{
			weight = q.Weight(s);
			searcher = s;
			filter = f;
			nDeletions = CountDeletions(s);
			GetMoreDocs(50); // retrieve 100 initially
			lengthAtStart = length;
		}

		/// <summary> Construct a <code>TermScorer</code>.
		/// 
		/// </summary>
		/// <param name="weight">The weight of the <code>Term</code> in the query.
		/// </param>
		/// <param name="td">An iterator over the documents matching the <code>Term</code>.
		/// </param>
		/// <param name="similarity">The <code>Similarity</code> implementation to be used for score
		/// computations.
		/// </param>
		/// <param name="norms">The field norms of the document fields for the <code>Term</code>.
		/// </param>
		public /*internal*/ TermScorer(Weight weight, TermDocs td, Similarity similarity, byte[] norms):base(similarity)
		{
			this.weight = weight;
			this.termDocs = td;
			this.norms = norms;
			this.weightValue = weight.GetValue();
			
			for (int i = 0; i < SCORE_CACHE_SIZE; i++)
				scoreCache[i] = GetSimilarity().Tf(i) * weightValue;
		}

        /// <summary> A search implementation which spans a new thread for each
        /// Searchable, waits for each search to complete and merge
        /// the results back together.
        /// </summary>
        public override TopDocs Search(Weight weight, Filter filter, int nDocs)
        {
            HitQueue hq = new HitQueue(nDocs, false);
            int totalHits = 0;
            MultiSearcherThread[] msta = new MultiSearcherThread[searchables.Length];
            for (int i = 0; i < searchables.Length; i++)
            {
                // search each searchable
                // Assume not too many searchables and cost of creating a thread is by far inferior to a search
                msta[i] = new MultiSearcherThread(searchables[i], weight, filter, nDocs, hq, i, starts, "MultiSearcher thread #" + (i + 1));
                msta[i].Start();
            }

            for (int i = 0; i < searchables.Length; i++)
            {
                try
                {
                    msta[i].Join();
                }
                catch (System.Threading.ThreadInterruptedException ie)
                {
                    // In 3.0 we will change this to throw
                    // InterruptedException instead
                    SupportClass.ThreadClass.Current().Interrupt();
                    throw new System.SystemException(ie.Message, ie);
                }
                System.IO.IOException ioe = msta[i].GetIOException();
                if (ioe == null)
                {
                    totalHits += msta[i].Hits();
                }
                else
                {
                    // if one search produced an IOException, rethrow it
                    throw ioe;
                }
            }

            ScoreDoc[] scoreDocs = new ScoreDoc[hq.Size()];
            for (int i = hq.Size() - 1; i >= 0; i--)
            // put docs in array
                scoreDocs[i] = (ScoreDoc) hq.Pop();

            float maxScore = (totalHits == 0)?System.Single.NegativeInfinity:scoreDocs[0].score;

            return new TopDocs(totalHits, scoreDocs, maxScore);
        }

 public override void Search(Weight weight, Filter filter, Collector collector)
 {
     if (filter == null)
     {
         for (int index = 0; index < this.subReaders.Length; ++index)
         {
             collector.SetNextReader(this.subReaders[index], this.docStarts[index]);
             Scorer scorer = weight.Scorer(this.subReaders[index], !collector.AcceptsDocsOutOfOrder, true);
             if (scorer != null)
                 this.SearchWithScorer(this.subReaders[index], weight, scorer, collector);
         }
     }
     else
     {
         for (int index = 0; index < this.subReaders.Length; ++index)
         {
             collector.SetNextReader(this.subReaders[index], this.docStarts[index]);
             this.SearchWithFilter(this.subReaders[index], weight, filter, collector);
         }
     }
 }

		internal PhraseScorer(Weight weight, TermPositions[] tps, int[] positions, Similarity similarity, byte[] norms) : base(similarity)
		{
			this.norms = norms;
			this.weight = weight;
			this.value_Renamed = 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
		}

        public void CreateSpatialFilterAndWeight(PointRadiusCriterion geoFilter, Filter currentFilter, Weight currentWeight)
        {
            var spatialContext = SpatialContext.GEO;
            var geohashTree = new GeohashPrefixTree(spatialContext, 10);
            var strategy = new RecursivePrefixTreeStrategy(geohashTree, geoFilter.FieldName);
            var point = spatialContext.MakePoint(geoFilter.Longitude, geoFilter.Latitude);

            var spatialArgs = new SpatialArgs(SpatialOperation.Intersects, spatialContext.MakeCircle(point,
                DistanceUtils.Dist2Degrees(geoFilter.RadiusKm, DistanceUtils.EARTH_MEAN_RADIUS_KM)));

            var circle = spatialContext.MakeCircle(point,
                    DistanceUtils.Dist2Degrees(geoFilter.RadiusKm, DistanceUtils.EARTH_MEAN_RADIUS_KM));
            var circleCells = strategy.GetGrid().GetWorldNode().GetSubCells(circle);

            var luceneFilters = new List<Filter>();
            if (currentFilter != null)
                luceneFilters.Add(currentFilter);

            var tempSpatial = strategy.MakeFilter(spatialArgs);
                luceneFilters.Add(tempSpatial);
            
            if (geoFilter.Sort != PointRadiusCriterion.SortOption.None)
            {
                var valueSource = strategy.MakeDistanceValueSource(point);
                var funcQ = new FunctionQuery(valueSource);
                // this is a bit odd... but boosting the score negatively orders results 
                if (geoFilter.Sort == PointRadiusCriterion.SortOption.Ascending)
                {
                    funcQ.Boost = -1;
                }
                spatialWeight = funcQ.CreateWeight(this);
                spatialWeight.GetSumOfSquaredWeights();

                luceneFilters.Add(new QueryWrapperFilter(currentWeight.Query));
            }

            spatialFilter = new ChainedFilter(luceneFilters.ToArray(), 1);
        }

        /// <summary> A search implementation which executes each
        /// <see cref="Searchable"/> in its own thread and waits for each search to complete
        /// and merge the results back together.
        /// </summary>
        public override TopDocs Search(Weight weight, Filter filter, int nDocs)
        {
            HitQueue hq = new HitQueue(nDocs, false);
            object lockObj = new object();

            TopDocs[] results = new TopDocs[searchables.Length];
            //search each searchable
            Parallel.For(0, searchables.Length, (i) => results[i] = MultiSearcherCallableNoSort(ThreadLock.MonitorLock, lockObj, searchables[i], weight, filter,
                                                                            nDocs, hq, i, starts));
            int totalHits = 0;
            float maxScore = float.NegativeInfinity;

            foreach (TopDocs topDocs in results)
            {
                totalHits += topDocs.TotalHits;
                maxScore = Math.Max(maxScore, topDocs.MaxScore);
            }

            ScoreDoc[] scoreDocs = new ScoreDoc[hq.Size()];
            for (int i = hq.Size() - 1; i >= 0; i--) // put docs in array
                scoreDocs[i] = hq.Pop();

            return new TopDocs(totalHits, scoreDocs, maxScore);
        }