internal MatchAllScorer(MatchAllDocsQuery enclosingInstance, IndexReader reader, Similarity similarity) : base(similarity)
{
InitBlock(enclosingInstance);
this.reader = reader;
count = - 1;
maxDoc = reader.MaxDoc();
}
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;
}
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;
}
public TermWeight(TermQuery enclosingInstance, Searcher searcher)
{
InitBlock(enclosingInstance);
this.similarity = Enclosing_Instance.GetSimilarity(searcher);
idfExp = similarity.IdfExplain(Enclosing_Instance.term, searcher);
idf = idfExp.Idf;
}
public TermWeight(TermQuery outerInstance, IndexSearcher searcher, TermContext termStates)
{
this.OuterInstance = outerInstance;
Debug.Assert(termStates != null, "TermContext must not be null");
this.TermStates = termStates;
this.Similarity = searcher.Similarity;
this.Stats = Similarity.ComputeWeight(outerInstance.Boost, searcher.CollectionStatistics(outerInstance.Term_Renamed.Field()), searcher.TermStatistics(outerInstance.Term_Renamed, termStates));
}
internal DocSetIteratorWeight(Query query, Similarity similarity, DocIdSetIterator iter)
{
_query = query;
_similarity = similarity;
_iter = iter;
_queryNorm = 1.0f;
_queryWeight = _query.Boost;
}
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();
}
public SpanWeight(SpanQuery query, Searcher searcher)
{
this.similarity = query.GetSimilarity(searcher);
this.query = query;
terms = new Support.Set<Lucene.Net.Index.Term>();
query.ExtractTerms(terms);
idfExp = similarity.idfExplain(terms.ToArray(), searcher);
idf = idfExp.GetIdf();
}
/// class constructor
public LuceneIREngine()
{
luceneIndexDirectory = null;
writer = null;
ISet <string> stopWords = StopAnalyzer.ENGLISH_STOP_WORDS_SET;
analyzer = new SnowballAnalyzer(VERSION, "English", stopWords);
mySimilarity = new CustomSimilarity();
}
public SpanWeight(SpanQuery query, Searcher searcher)
{
this.similarity = query.GetSimilarity(searcher);
this.query = query;
terms = new System.Collections.Hashtable();
query.ExtractTerms(terms);
idfExp = similarity.idfExplain(new System.Collections.ArrayList(terms.Values), searcher);
idf = idfExp.GetIdf();
}
public ConjunctionScorer(Similarity similarity, Scorer[] scorers)
: base(similarity)
{
this.scorers = scorers;
coord = similarity.Coord(scorers.Length, scorers.Length);
for (int i = 0; i < scorers.Length; i++)
{
if (scorers[i].NextDoc() == NO_MORE_DOCS)
{
// If even one of the sub-scorers does not have any documents, this
// scorer should not attempt to do any more work.
lastDoc = NO_MORE_DOCS;
return ;
}
}
// Sort the array the first time...
// We don't need to sort the array in any future calls because we know
// it will already start off sorted (all scorers on same doc).
// note that this comparator is not consistent with equals!
System.Array.Sort(scorers, new AnonymousClassComparator(this));
// NOTE: doNext() must be called before the re-sorting of the array later on.
// The reason is this: assume there are 5 scorers, whose first docs are 1,
// 2, 3, 5, 5 respectively. Sorting (above) leaves the array as is. Calling
// doNext() here advances all the first scorers to 5 (or a larger doc ID
// they all agree on).
// However, if we re-sort before doNext() is called, the order will be 5, 3,
// 2, 1, 5 and then doNext() will stop immediately, since the first scorer's
// docs equals the last one. So the invariant that after calling doNext()
// all scorers are on the same doc ID is broken.
if (DoNext() == NO_MORE_DOCS)
{
// The scorers did not agree on any document.
lastDoc = NO_MORE_DOCS;
return ;
}
// If first-time skip distance is any predictor of
// scorer sparseness, then we should always try to skip first on
// those scorers.
// Keep last scorer in it's last place (it will be the first
// to be skipped on), but reverse all of the others so that
// they will be skipped on in order of original high skip.
int end = scorers.Length - 1;
int max = end >> 1;
for (int i = 0; i < max; i++)
{
Scorer tmp = scorers[i];
int idx = end - i - 1;
scorers[i] = scorers[idx];
scorers[idx] = tmp;
}
}
/// <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>Create a BooleanScorer2.</summary>
/// <param name="similarity">The similarity to be used.
/// </param>
/// <param name="minNrShouldMatch">The minimum number of optional added scorers
/// that should match during the search.
/// In case no required scorers are added,
/// at least one of the optional scorers will have to
/// match during the search.
/// </param>
public BooleanScorer2(Similarity similarity, int minNrShouldMatch)
: base(similarity)
{
if (minNrShouldMatch < 0)
{
throw new System.ArgumentException("Minimum number of optional scorers should not be negative");
}
coordinator = new Coordinator(this);
this.minNrShouldMatch = minNrShouldMatch;
}
public SpanWeight(SpanQuery query, Searcher searcher)
{
this.similarity = query.GetSimilarity(searcher);
this.internalQuery = query;
terms = Lucene.Net.Support.Compatibility.SetFactory.CreateHashSet<Term>();
query.ExtractTerms(terms);
idfExp = similarity.IdfExplain(terms, searcher);
idf = idfExp.Idf;
}
public FastMatchAllScorer(int maxdoc, int[] delDocs, Similarity similarity, float score)
: base(similarity)
{
doc = -1;
deletedDocs = delDocs;
deletedIndex = 0;
moreDeletions = deletedDocs != null && deletedDocs.Length > 0;
delLen = deletedDocs != null ? deletedDocs.Length : 0;
this.score = score;
maxDoc = maxdoc;
}
public SpanWeight(SpanQuery query, Searcher searcher)
{
this.similarity = query.GetSimilarity(searcher);
this.query = query;
terms = new System.Collections.Hashtable();
query.ExtractTerms(terms);
System.Collections.ArrayList tmp = new System.Collections.ArrayList(terms.Values);
idf = this.query.GetSimilarity(searcher).Idf(tmp, searcher);
}
/// <summary> Creates a new instance of DisjunctionMaxScorer
///
/// </summary>
/// <param name="tieBreakerMultiplier">Multiplier applied to non-maximum-scoring subqueries for a
/// document as they are summed into the result.
/// </param>
/// <param name="similarity">-- not used since our definition involves neither coord nor terms
/// directly
/// </param>
/// <param name="subScorers">The sub scorers this Scorer should iterate on
/// </param>
/// <param name="numScorers">The actual number of scorers to iterate on. Note that the array's
/// length may be larger than the actual number of scorers.
/// </param>
public DisjunctionMaxScorer(float tieBreakerMultiplier, Similarity similarity, Scorer[] subScorers, int numScorers):base(similarity)
{
this.tieBreakerMultiplier = tieBreakerMultiplier;
// The passed subScorers array includes only scorers which have documents
// (DisjunctionMaxQuery takes care of that), and their nextDoc() was already
// called.
this.subScorers = subScorers;
this.numScorers = numScorers;
Heapify();
}
public SectionSearchScorer(Query query, Similarity similarity, float score, IndexReader reader)
: base(similarity)
{
_curScr = score;
SectionSearchQueryPlanBuilder builer = new SectionSearchQueryPlanBuilder(reader);
_plan = builer.GetPlan(query);
if (_plan != null)
{
_curDoc = -1;
//_more = true; // NOT USED
}
else
{
_curDoc = DocIdSetIterator.NO_MORE_DOCS;
//_more = false; // NOT USED
}
}
public FacetBasedBoostingScorer(FacetBasedBoostScorerBuilder parent, BoboIndexReader reader, Similarity similarity, Scorer innerScorer)
: base(similarity)
{
_innerScorer = innerScorer;
List<BoboDocScorer> list = new List<BoboDocScorer>();
foreach (var boostEntry in parent._boostMaps)
{
string facetName = boostEntry.Key;
IFacetHandler handler = reader.GetFacetHandler(facetName);
if (!(handler is IFacetScoreable))
throw new ArgumentException(facetName + " does not implement FacetScoreable");
IFacetScoreable facetScoreable = (IFacetScoreable)handler;
BoboDocScorer scorer = facetScoreable.GetDocScorer(reader, parent._scoringFunctionFactory, boostEntry.Value);
if (scorer != null) list.Add(scorer);
}
_facetScorers = list.ToArray();
_docid = -1;
}
/*internal*/
public BooleanScorer(Similarity similarity, int minNrShouldMatch, System.Collections.IList optionalScorers, System.Collections.IList prohibitedScorers)
: base(similarity)
{
InitBlock();
this.minNrShouldMatch = minNrShouldMatch;
if (optionalScorers != null && optionalScorers.Count > 0)
{
for (System.Collections.IEnumerator si = optionalScorers.GetEnumerator(); si.MoveNext(); )
{
Scorer scorer = (Scorer) si.Current;
maxCoord++;
if (scorer.NextDoc() != NO_MORE_DOCS)
{
scorers = new SubScorer(scorer, false, false, bucketTable.NewCollector(0), scorers);
}
}
}
if (prohibitedScorers != null && prohibitedScorers.Count > 0)
{
for (System.Collections.IEnumerator si = prohibitedScorers.GetEnumerator(); si.MoveNext(); )
{
Scorer scorer = (Scorer) si.Current;
int mask = nextMask;
nextMask = nextMask << 1;
prohibitedMask |= mask; // update prohibited mask
if (scorer.NextDoc() != NO_MORE_DOCS)
{
scorers = new SubScorer(scorer, false, true, bucketTable.NewCollector(mask), scorers);
}
}
}
coordFactors = new float[maxCoord];
Similarity sim = GetSimilarity();
for (int i = 0; i < maxCoord; i++)
{
coordFactors[i] = sim.Coord(i, maxCoord - 1);
}
}
/// <summary> Creates a {@link Scorer} with the given similarity and lists of required,
/// prohibited and optional scorers. In no required scorers are added, at least
/// one of the optional scorers will have to match during the search.
///
/// </summary>
/// <param name="similarity">The similarity to be used.
/// </param>
/// <param name="minNrShouldMatch">The minimum number of optional added scorers that should match
/// during the search. In case no required scorers are added, at least
/// one of the optional scorers will have to match during the search.
/// </param>
/// <param name="required">the list of required scorers.
/// </param>
/// <param name="prohibited">the list of prohibited scorers.
/// </param>
/// <param name="optional">the list of optional scorers.
/// </param>
public BooleanScorer2(Similarity similarity, int minNrShouldMatch, System.Collections.IList required, System.Collections.IList prohibited, System.Collections.IList optional)
: base(similarity)
{
if (minNrShouldMatch < 0)
{
throw new System.ArgumentException("Minimum number of optional scorers should not be negative");
}
coordinator = new Coordinator(this);
this.minNrShouldMatch = minNrShouldMatch;
optionalScorers = optional;
coordinator.maxCoord += optional.Count;
requiredScorers = required;
coordinator.maxCoord += required.Count;
prohibitedScorers = prohibited;
coordinator.Init();
countingSumScorer = MakeCountingSumScorer();
}
public LuceneCore()
{
//the stopword is adapted from https://github.com/pyelasticsearch/pyelasticsearch
string STOPWORDS = "a able about across after all almost also am among an and " +
"any are as at be because been but by can cannot could dear " +
"did do does either else ever every for from get got had has " +
"have he her hers him his how however i if in into is it its " +
"just least let like likely may me might most must my " +
"neither no nor not of off often on only or other our own " +
"rather said say says she should since so some than that the " +
"their them then there these they this tis to too twas us " +
"wants was we were what when where which while who whom why " +
"will with would yet you your";
string[] stopArray = STOPWORDS.Split();
int myC = 0;
HashSet <String> stopSet = new HashSet <String>();
foreach (var a in stopArray)
{
stopSet.Add(stopArray[myC]);
myC++;
}
//System.IO.FileInfo[] stopArray = STOPWORDS.Split();
luceneIndexDirectory = null;
writer = null;
AsIsanalyzer = new Lucene.Net.Analysis.WhitespaceAnalyzer();
analyzer = new Lucene.Net.Analysis.Standard.StandardAnalyzer(VERSION, stopSet);
//analyzer = new Lucene.Net.Analysis.SimpleAnalyzer();
//analyzer = new SnowballAnalyzer(VERSION, "English", stopArray);
//parser = new QueryParser(Lucene.Net.Util.Version.LUCENE_30, TEXT_FN, analyzer);
string[] fieldList = new string[] { uRL, qText };
MultiParser = new MultiFieldQueryParser(VERSION, fieldList, analyzer);
MultiAsIsparser = new MultiFieldQueryParser(VERSION, fieldList, AsIsanalyzer);
newSimilarity = new NewSimilarity();
}
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 SpanWeight(SpanQuery query, IndexSearcher searcher)
{
this.Similarity = searcher.Similarity;
this.query = query;
TermContexts = new Dictionary<Term, TermContext>();
SortedSet<Term> terms = new SortedSet<Term>();
query.ExtractTerms(terms);
IndexReaderContext context = searcher.TopReaderContext;
TermStatistics[] termStats = new TermStatistics[terms.Count];
int i = 0;
foreach (Term term in terms)
{
TermContext state = TermContext.Build(context, term);
termStats[i] = searcher.TermStatistics(term, state);
TermContexts[term] = state;
i++;
}
string field = query.Field;
if (field != null)
{
Stats = Similarity.ComputeWeight(query.Boost, searcher.CollectionStatistics(query.Field), termStats);
}
}
private int maxDoc; // document count
public CachedDfSource(System.Collections.IDictionary dfMap, int maxDoc, Similarity similarity)
{
this.dfMap = dfMap;
this.maxDoc = maxDoc;
SetSimilarity(similarity);
}
private void InitBlock(int df, int max, float idf, Similarity enclosingInstance)
{
this.df = df;
this.max = max;
this.idf = idf;
this.enclosingInstance = enclosingInstance;
}
private void InitBlock(float idf, Similarity enclosingInstance)
{
this.idf = idf;
this.enclosingInstance = enclosingInstance;
}
public MatchAllDocsWeight(MatchAllDocsQuery enclosingInstance, Searcher searcher)
{
InitBlock(enclosingInstance);
this.similarity = searcher.GetSimilarity();
}
public BooleanWeight(BooleanQuery enclosingInstance, Searcher searcher)
{
InitBlock(enclosingInstance);
this.similarity = Enclosing_Instance.GetSimilarity(searcher);
for (int i = 0; i < Enclosing_Instance.clauses.Count; i++)
{
BooleanClause c = (BooleanClause) Enclosing_Instance.clauses[i];
weights.Add(c.GetQuery().CreateWeight(searcher));
}
}
public TermWeight(TermQuery enclosingInstance, Searcher searcher)
{
InitBlock(enclosingInstance);
this.similarity = Enclosing_Instance.GetSimilarity(searcher);
idf = similarity.Idf(Enclosing_Instance.term, searcher); // compute idf
}
public virtual void TestKnownSetOfDocuments()
{
System.String test1 = "eating chocolate in a computer lab"; //6 terms
System.String test2 = "computer in a computer lab"; //5 terms
System.String test3 = "a chocolate lab grows old"; //5 terms
System.String test4 = "eating chocolate with a chocolate lab in an old chocolate colored computer lab"; //13 terms
System.Collections.IDictionary test4Map = new System.Collections.Hashtable();
test4Map["chocolate"] = 3;
test4Map["lab"] = 2;
test4Map["eating"] = 1;
test4Map["computer"] = 1;
test4Map["with"] = 1;
test4Map["a"] = 1;
test4Map["colored"] = 1;
test4Map["in"] = 1;
test4Map["an"] = 1;
test4Map["computer"] = 1;
test4Map["old"] = 1;
Document testDoc1 = new Document();
SetupDoc(testDoc1, test1);
Document testDoc2 = new Document();
SetupDoc(testDoc2, test2);
Document testDoc3 = new Document();
SetupDoc(testDoc3, test3);
Document testDoc4 = new Document();
SetupDoc(testDoc4, test4);
Directory dir = new MockRAMDirectory();
try
{
IndexWriter writer = new IndexWriter(dir, new SimpleAnalyzer(), true, IndexWriter.MaxFieldLength.LIMITED, null);
Assert.IsTrue(writer != null);
writer.AddDocument(testDoc1, null);
writer.AddDocument(testDoc2, null);
writer.AddDocument(testDoc3, null);
writer.AddDocument(testDoc4, null);
writer.Close();
IndexSearcher knownSearcher = new IndexSearcher(dir, true, null);
TermEnum termEnum = knownSearcher.reader_ForNUnit.Terms(null);
TermDocs termDocs = knownSearcher.reader_ForNUnit.TermDocs(null);
//System.out.println("Terms: " + termEnum.size() + " Orig Len: " + termArray.length);
Similarity sim = knownSearcher.Similarity;
while (termEnum.Next(null) == true)
{
Term term = termEnum.Term;
//System.out.println("Term: " + term);
termDocs.Seek(term, null);
while (termDocs.Next(null))
{
int docId = termDocs.Doc;
int freq = termDocs.Freq;
//System.out.println("Doc Id: " + docId + " freq " + freq);
ITermFreqVector vector = knownSearcher.reader_ForNUnit.GetTermFreqVector(docId, "field", null);
float tf = sim.Tf(freq);
float idf = sim.Idf(knownSearcher.DocFreq(term, null), knownSearcher.MaxDoc);
//float qNorm = sim.queryNorm()
//This is fine since we don't have stop words
float lNorm = sim.LengthNorm("field", vector.GetTerms().Length);
//float coord = sim.coord()
//System.out.println("TF: " + tf + " IDF: " + idf + " LenNorm: " + lNorm);
Assert.IsTrue(vector != null);
System.String[] vTerms = vector.GetTerms();
int[] freqs = vector.GetTermFrequencies();
for (int i = 0; i < vTerms.Length; i++)
{
if (term.Text.Equals(vTerms[i]))
{
Assert.IsTrue(freqs[i] == freq);
}
}
}
//System.out.println("--------");
}
Query query = new TermQuery(new Term("field", "chocolate"));
ScoreDoc[] hits = knownSearcher.Search(query, null, 1000, null).ScoreDocs;
//doc 3 should be the first hit b/c it is the shortest match
Assert.IsTrue(hits.Length == 3);
float score = hits[0].Score;
/*System.out.println("Hit 0: " + hits.id(0) + " Score: " + hits.score(0) + " String: " + hits.doc(0).toString());
* System.out.println("Explain: " + knownSearcher.explain(query, hits.id(0)));
* System.out.println("Hit 1: " + hits.id(1) + " Score: " + hits.score(1) + " String: " + hits.doc(1).toString());
* System.out.println("Explain: " + knownSearcher.explain(query, hits.id(1)));
* System.out.println("Hit 2: " + hits.id(2) + " Score: " + hits.score(2) + " String: " + hits.doc(2).toString());
* System.out.println("Explain: " + knownSearcher.explain(query, hits.id(2)));*/
Assert.IsTrue(hits[0].Doc == 2);
Assert.IsTrue(hits[1].Doc == 3);
Assert.IsTrue(hits[2].Doc == 0);
ITermFreqVector vector2 = knownSearcher.reader_ForNUnit.GetTermFreqVector(hits[1].Doc, "field", null);
Assert.IsTrue(vector2 != null);
//System.out.println("Vector: " + vector);
System.String[] terms = vector2.GetTerms();
int[] freqs2 = vector2.GetTermFrequencies();
Assert.IsTrue(terms != null && terms.Length == 10);
for (int i = 0; i < terms.Length; i++)
{
System.String term = terms[i];
//System.out.println("Term: " + term);
int freq = freqs2[i];
Assert.IsTrue(test4.IndexOf(term) != -1);
System.Int32 freqInt = -1;
try
{
freqInt = (System.Int32)test4Map[term];
}
catch (Exception)
{
Assert.IsTrue(false);
}
Assert.IsTrue(freqInt == freq);
}
SortedTermVectorMapper mapper = new SortedTermVectorMapper(new TermVectorEntryFreqSortedComparator());
knownSearcher.reader_ForNUnit.GetTermFreqVector(hits[1].Doc, mapper, null);
var vectorEntrySet = mapper.TermVectorEntrySet;
Assert.IsTrue(vectorEntrySet.Count == 10, "mapper.getTermVectorEntrySet() Size: " + vectorEntrySet.Count + " is not: " + 10);
TermVectorEntry last = null;
foreach (TermVectorEntry tve in vectorEntrySet)
{
if (tve != null && last != null)
{
Assert.IsTrue(last.Frequency >= tve.Frequency, "terms are not properly sorted");
System.Int32 expectedFreq = (System.Int32)test4Map[tve.Term];
//we expect double the expectedFreq, since there are two fields with the exact same text and we are collapsing all fields
Assert.IsTrue(tve.Frequency == 2 * expectedFreq, "Frequency is not correct:");
}
last = tve;
}
FieldSortedTermVectorMapper fieldMapper = new FieldSortedTermVectorMapper(new TermVectorEntryFreqSortedComparator());
knownSearcher.reader_ForNUnit.GetTermFreqVector(hits[1].Doc, fieldMapper, null);
var map = fieldMapper.FieldToTerms;
Assert.IsTrue(map.Count == 2, "map Size: " + map.Count + " is not: " + 2);
vectorEntrySet = map["field"];
Assert.IsTrue(vectorEntrySet != null, "vectorEntrySet is null and it shouldn't be");
Assert.IsTrue(vectorEntrySet.Count == 10, "vectorEntrySet Size: " + vectorEntrySet.Count + " is not: " + 10);
knownSearcher.Close();
}
catch (System.IO.IOException e)
{
System.Console.Error.WriteLine(e.StackTrace);
Assert.IsTrue(false);
}
}
/// <summary>
/// Tests that a query matches the an expected set of documents using Hits.
///
/// <p>
/// Note that when using the Hits API, documents will only be returned
/// if they have a positive normalized score.
/// </p> </summary>
/// <param name="query"> the query to test </param>
/// <param name="searcher"> the searcher to test the query against </param>
/// <param name="defaultFieldName"> used for displaing the query in assertion messages </param>
/// <param name="results"> a list of documentIds that must match the query </param>
/// <param name="similarity">
/// LUCENENET specific
/// Removes dependency on <see cref="LuceneTestCase.ClassEnv.Similarity"/>
/// </param>
/// <seealso cref= #checkHitCollector </seealso>
public static void DoCheckHits(Random random, Query query, string defaultFieldName, IndexSearcher searcher, int[] results, Similarity similarity)
{
ScoreDoc[] hits = searcher.Search(query, 1000).ScoreDocs;
SortedSet <int?> correct = new SortedSet <int?>();
for (int i = 0; i < results.Length; i++)
{
correct.Add(Convert.ToInt32(results[i], CultureInfo.InvariantCulture));
}
SortedSet <int?> actual = new SortedSet <int?>();
for (int i = 0; i < hits.Length; i++)
{
actual.Add(Convert.ToInt32(hits[i].Doc, CultureInfo.InvariantCulture));
}
Assert.AreEqual(correct, actual, query.ToString(defaultFieldName));
QueryUtils.Check(random, query, searcher, LuceneTestCase.Rarely(random), similarity);
}
/// <summary>
/// Tests that a query matches the an expected set of documents using a
/// HitCollector.
///
/// <p>
/// Note that when using the HitCollector API, documents will be collected
/// if they "match" regardless of what their score is.
/// </p> </summary>
/// <param name="query"> the query to test </param>
/// <param name="searcher"> the searcher to test the query against </param>
/// <param name="defaultFieldName"> used for displaying the query in assertion messages </param>
/// <param name="results"> a list of documentIds that must match the query </param>
/// <param name="similarity">
/// LUCENENET specific
/// Removes dependency on <see cref="LuceneTestCase.ClassEnv.Similarity"/>
/// </param>
/// <seealso cref=#checkHits </seealso>
public static void CheckHitCollector(Random random, Query query, string defaultFieldName, IndexSearcher searcher, int[] results, Similarity similarity)
{
QueryUtils.Check(random, query, searcher, similarity);
Trace.TraceInformation("Checked");
SortedSet <int?> correct = new SortedSet <int?>();
for (int i = 0; i < results.Length; i++)
{
correct.Add(Convert.ToInt32(results[i], CultureInfo.InvariantCulture));
}
SortedSet <int?> actual = new SortedSet <int?>();
ICollector c = new SetCollector(actual);
searcher.Search(query, c);
Assert.AreEqual(correct, actual, "Simple: " + query.ToString(defaultFieldName));
for (int i = -1; i < 2; i++)
{
actual.Clear();
IndexSearcher s = QueryUtils.WrapUnderlyingReader(random, searcher, i, similarity);
s.Search(query, c);
Assert.AreEqual(correct, actual, "Wrap Reader " + i + ": " + query.ToString(defaultFieldName));
}
}
public ConstantScorer(ConstantScoreQuery enclosingInstance, Similarity similarity, IndexReader reader, Weight w) : base(similarity)
{
InitBlock(enclosingInstance);
theScore = w.GetValue();
docIdSetIterator = Enclosing_Instance.filter.GetDocIdSet(reader).Iterator();
}
public override Explanation Explain(IndexReader reader, int doc)
{
Explanation result = new Explanation();
result.Description = "weight(" + Query + " in " + doc + "), product of:";
System.Text.StringBuilder docFreqs = new System.Text.StringBuilder();
System.Text.StringBuilder query = new System.Text.StringBuilder();
query.Append('\"');
docFreqs.Append(idfExp.Explain());
for (int i = 0; i < Enclosing_Instance.terms.Count; i++)
{
if (i != 0)
{
query.Append(" ");
}
Term term = Enclosing_Instance.terms[i];
query.Append(term.Text);
}
query.Append('\"');
Explanation idfExpl = new Explanation(idf, "idf(" + Enclosing_Instance.field + ":" + docFreqs + ")");
// explain query weight
Explanation queryExpl = new Explanation();
queryExpl.Description = "queryWeight(" + Query + "), product of:";
Explanation boostExpl = new Explanation(Enclosing_Instance.Boost, "boost");
if (Enclosing_Instance.Boost != 1.0f)
{
queryExpl.AddDetail(boostExpl);
}
queryExpl.AddDetail(idfExpl);
Explanation queryNormExpl = new Explanation(queryNorm, "queryNorm");
queryExpl.AddDetail(queryNormExpl);
queryExpl.Value = boostExpl.Value * idfExpl.Value * queryNormExpl.Value;
result.AddDetail(queryExpl);
// explain field weight
Explanation fieldExpl = new Explanation();
fieldExpl.Description = "fieldWeight(" + Enclosing_Instance.field + ":" + query + " in " + doc + "), product of:";
PhraseScorer scorer = (PhraseScorer)Scorer(reader, true, false);
if (scorer == null)
{
return(new Explanation(0.0f, "no matching docs"));
}
Explanation tfExplanation = new Explanation();
int d = scorer.Advance(doc);
float phraseFreq = (d == doc) ? scorer.CurrentFreq() : 0.0f;
tfExplanation.Value = similarity.Tf(phraseFreq);
tfExplanation.Description = "tf(phraseFreq=" + phraseFreq + ")";
fieldExpl.AddDetail(tfExplanation);
fieldExpl.AddDetail(idfExpl);
Explanation fieldNormExpl = new Explanation();
byte[] fieldNorms = reader.Norms(Enclosing_Instance.field);
float fieldNorm = fieldNorms != null?Similarity.DecodeNorm(fieldNorms[doc]) : 1.0f;
fieldNormExpl.Value = fieldNorm;
fieldNormExpl.Description = "fieldNorm(field=" + Enclosing_Instance.field + ", doc=" + doc + ")";
fieldExpl.AddDetail(fieldNormExpl);
fieldExpl.Value = tfExplanation.Value * idfExpl.Value * fieldNormExpl.Value;
result.AddDetail(fieldExpl);
// combine them
result.Value = queryExpl.Value * fieldExpl.Value;
if (queryExpl.Value == 1.0f)
{
return(fieldExpl);
}
return(result);
}
public virtual Explanation Explain(IndexReader reader, int doc)
{
Explanation result = new Explanation();
result.SetDescription("weight(" + GetQuery() + " in " + doc + "), product of:");
System.Text.StringBuilder docFreqs = new System.Text.StringBuilder();
System.Text.StringBuilder query = new System.Text.StringBuilder();
query.Append('\"');
for (int i = 0; i < Enclosing_Instance.terms.Count; i++)
{
if (i != 0)
{
docFreqs.Append(" ");
query.Append(" ");
}
Term term = (Term)Enclosing_Instance.terms[i];
docFreqs.Append(term.Text());
docFreqs.Append("=");
docFreqs.Append(reader.DocFreq(term));
query.Append(term.Text());
}
query.Append('\"');
Explanation idfExpl = new Explanation(idf, "idf(" + Enclosing_Instance.field + ": " + docFreqs + ")");
// explain query weight
Explanation queryExpl = new Explanation();
queryExpl.SetDescription("queryWeight(" + GetQuery() + "), product of:");
Explanation boostExpl = new Explanation(Enclosing_Instance.GetBoost(), "boost");
if (Enclosing_Instance.GetBoost() != 1.0f)
{
queryExpl.AddDetail(boostExpl);
}
queryExpl.AddDetail(idfExpl);
Explanation queryNormExpl = new Explanation(queryNorm, "queryNorm");
queryExpl.AddDetail(queryNormExpl);
queryExpl.SetValue(boostExpl.GetValue() * idfExpl.GetValue() * queryNormExpl.GetValue());
result.AddDetail(queryExpl);
// explain field weight
Explanation fieldExpl = new Explanation();
fieldExpl.SetDescription("fieldWeight(" + Enclosing_Instance.field + ":" + query + " in " + doc + "), product of:");
Explanation tfExpl = Scorer(reader).Explain(doc);
fieldExpl.AddDetail(tfExpl);
fieldExpl.AddDetail(idfExpl);
Explanation fieldNormExpl = new Explanation();
byte[] fieldNorms = reader.Norms(Enclosing_Instance.field);
float fieldNorm = fieldNorms != null?Similarity.DecodeNorm(fieldNorms[doc]) : 0.0f;
fieldNormExpl.SetValue(fieldNorm);
fieldNormExpl.SetDescription("fieldNorm(field=" + Enclosing_Instance.field + ", doc=" + doc + ")");
fieldExpl.AddDetail(fieldNormExpl);
fieldExpl.SetValue(tfExpl.GetValue() * idfExpl.GetValue() * fieldNormExpl.GetValue());
result.AddDetail(fieldExpl);
// combine them
result.SetValue(queryExpl.GetValue() * fieldExpl.GetValue());
if (queryExpl.GetValue() == 1.0f)
{
return(fieldExpl);
}
return(result);
}
private void InitBlock(float fIdf, System.Text.StringBuilder exp, Similarity enclosingInstance)
{
this.fIdf = fIdf;
this.exp = exp;
this.enclosingInstance = enclosingInstance;
}
public ConstantWeight(ConstantScoreQuery enclosingInstance, Searcher searcher)
{
InitBlock(enclosingInstance);
this.similarity = Enclosing_Instance.GetSimilarity(searcher);
}
internal SloppyPhraseScorer(Weight weight, TermPositions[] tps, int[] offsets, Similarity similarity, int slop, byte[] norms) : base(weight, tps, offsets, similarity, norms)
{
this.slop = slop;
}
public ConjunctionScorer(Similarity similarity) : base(similarity)
{
}
internal ExactPhraseScorer(Weight weight, TermPositions[] tps, int[] offsets, Similarity similarity, byte[] norms) : base(weight, tps, offsets, similarity, norms)
{
}
public override Explanation Explain(IndexReader reader, int doc, IState state)
{
ComplexExplanation result = new ComplexExplanation();
result.Description = "weight(" + Query + " in " + doc + "), product of:";
Explanation expl = new Explanation(idf, idfExp.Explain());
// explain query weight
Explanation queryExpl = new Explanation();
queryExpl.Description = "queryWeight(" + Query + "), product of:";
Explanation boostExpl = new Explanation(Enclosing_Instance.Boost, "boost");
if (Enclosing_Instance.Boost != 1.0f)
{
queryExpl.AddDetail(boostExpl);
}
queryExpl.AddDetail(expl);
Explanation queryNormExpl = new Explanation(queryNorm, "queryNorm");
queryExpl.AddDetail(queryNormExpl);
queryExpl.Value = boostExpl.Value * expl.Value * queryNormExpl.Value;
result.AddDetail(queryExpl);
// explain field weight
System.String field = Enclosing_Instance.term.Field;
ComplexExplanation fieldExpl = new ComplexExplanation();
fieldExpl.Description = "fieldWeight(" + Enclosing_Instance.term + " in " + doc + "), product of:";
Explanation tfExplanation = new Explanation();
int tf = 0;
TermDocs termDocs = reader.TermDocs(enclosingInstance.term, state);
if (termDocs != null)
{
try
{
if (termDocs.SkipTo(doc, state) && termDocs.Doc == doc)
{
tf = termDocs.Freq;
}
}
finally
{
termDocs.Close();
}
tfExplanation.Value = similarity.Tf(tf);
tfExplanation.Description = "tf(termFreq(" + enclosingInstance.term + ")=" + tf + ")";
}
else
{
tfExplanation.Value = 0.0f;
tfExplanation.Description = "no matching term";
}
fieldExpl.AddDetail(tfExplanation);
fieldExpl.AddDetail(expl);
Explanation fieldNormExpl = new Explanation();
byte[] fieldNorms = reader.Norms(field, state);
float fieldNorm = fieldNorms != null?Similarity.DecodeNorm(fieldNorms[doc]) : 1.0f;
fieldNormExpl.Value = fieldNorm;
fieldNormExpl.Description = "fieldNorm(field=" + field + ", doc=" + doc + ")";
fieldExpl.AddDetail(fieldNormExpl);
fieldExpl.Match = tfExplanation.IsMatch;
fieldExpl.Value = tfExplanation.Value * expl.Value * fieldNormExpl.Value;
result.AddDetail(fieldExpl);
System.Boolean?tempAux = fieldExpl.Match;
result.Match = tempAux;
// combine them
result.Value = queryExpl.Value * fieldExpl.Value;
if (queryExpl.Value == 1.0f)
{
return(fieldExpl);
}
return(result);
}
public AnonymousSimilarityDelegator(FuzzyTermQuery parent, Similarity result) : base(result)
{
this.parent = parent;
}
public virtual void TestSimple()
{
Directory dir = NewDirectory();
RandomIndexWriter iw = new RandomIndexWriter(Random(), dir, Similarity, TimeZone);
Document doc = new Document();
Field field = NewTextField("foo", "", Field.Store.NO);
doc.Add(field);
Field dvField = new SingleDocValuesField("foo_boost", 0.0F);
doc.Add(dvField);
Field field2 = NewTextField("bar", "", Field.Store.NO);
doc.Add(field2);
field.SetStringValue("quick brown fox");
field2.SetStringValue("quick brown fox");
dvField.SetSingleValue(2f); // boost x2
iw.AddDocument(doc);
field.SetStringValue("jumps over lazy brown dog");
field2.SetStringValue("jumps over lazy brown dog");
dvField.SetSingleValue(4f); // boost x4
iw.AddDocument(doc);
IndexReader ir = iw.Reader;
iw.Dispose();
// no boosting
IndexSearcher searcher1 = NewSearcher(ir, false, Similarity);
Similarity @base = searcher1.Similarity;
// boosting
IndexSearcher searcher2 = NewSearcher(ir, false, Similarity);
searcher2.Similarity = new PerFieldSimilarityWrapperAnonymousInnerClassHelper(this, field, @base);
// in this case, we searched on field "foo". first document should have 2x the score.
TermQuery tq = new TermQuery(new Term("foo", "quick"));
QueryUtils.Check(Random(), tq, searcher1, Similarity);
QueryUtils.Check(Random(), tq, searcher2, Similarity);
TopDocs noboost = searcher1.Search(tq, 10);
TopDocs boost = searcher2.Search(tq, 10);
Assert.AreEqual(1, noboost.TotalHits);
Assert.AreEqual(1, boost.TotalHits);
//System.out.println(searcher2.Explain(tq, boost.ScoreDocs[0].Doc));
Assert.AreEqual(boost.ScoreDocs[0].Score, noboost.ScoreDocs[0].Score * 2f, SCORE_EPSILON);
// this query matches only the second document, which should have 4x the score.
tq = new TermQuery(new Term("foo", "jumps"));
QueryUtils.Check(Random(), tq, searcher1, Similarity);
QueryUtils.Check(Random(), tq, searcher2, Similarity);
noboost = searcher1.Search(tq, 10);
boost = searcher2.Search(tq, 10);
Assert.AreEqual(1, noboost.TotalHits);
Assert.AreEqual(1, boost.TotalHits);
Assert.AreEqual(boost.ScoreDocs[0].Score, noboost.ScoreDocs[0].Score * 4f, SCORE_EPSILON);
// search on on field bar just for kicks, nothing should happen, since we setup
// our sim provider to only use foo_boost for field foo.
tq = new TermQuery(new Term("bar", "quick"));
QueryUtils.Check(Random(), tq, searcher1, Similarity);
QueryUtils.Check(Random(), tq, searcher2, Similarity);
noboost = searcher1.Search(tq, 10);
boost = searcher2.Search(tq, 10);
Assert.AreEqual(1, noboost.TotalHits);
Assert.AreEqual(1, boost.TotalHits);
Assert.AreEqual(boost.ScoreDocs[0].Score, noboost.ScoreDocs[0].Score, SCORE_EPSILON);
ir.Dispose();
dir.Dispose();
}
internal MatchAllScorer(MatchAllDocsQuery enclosingInstance, IndexReader reader, Similarity similarity, Weight w, byte[] norms) : base(similarity)
{
InitBlock(enclosingInstance);
this.termDocs = reader.TermDocs(null);
score = w.GetValue();
this.norms = norms;
}
public BoostingSimilarity(Similarity sim, string boostField)
{
this.Sim = sim;
this.BoostField = boostField;
}
public override float Score()
{
return(norms == null?score:score *Similarity.DecodeNorm(norms[DocID()]));
}
public PerFieldSimilarityWrapperAnonymousInnerClassHelper(TestDocValuesScoring outerInstance, Field field, Similarity @base)
{
this.OuterInstance = outerInstance;
this.Field = field;
this.@base = @base;
fooSim = new BoostingSimilarity(@base, "foo_boost");
}
public AnonymousClassIDFExplanation1(int df, int max, float idf, Similarity enclosingInstance)
{
InitBlock(df, max, idf, enclosingInstance);
}
/// <summary>Construct a {@link Similarity} that delegates all methods to another.
///
/// </summary>
/// <param name="delegee">the Similarity implementation to delegate to
/// </param>
public SimilarityDelegator(Similarity delegee)
{
this.delegee = delegee;
}
public AnonymousClassIDFExplanation2(float idf, Similarity enclosingInstance)
{
InitBlock(idf, enclosingInstance);
}
public override Explanation Explain(IndexReader reader, int doc)
{
ComplexExplanation result = new ComplexExplanation();
result.SetDescription("weight(" + GetQuery() + " in " + doc + "), product of:");
Explanation expl = new Explanation(idf, idfExp.Explain());
// explain query weight
Explanation queryExpl = new Explanation();
queryExpl.SetDescription("queryWeight(" + GetQuery() + "), product of:");
Explanation boostExpl = new Explanation(Enclosing_Instance.GetBoost(), "boost");
if (Enclosing_Instance.GetBoost() != 1.0f)
{
queryExpl.AddDetail(boostExpl);
}
queryExpl.AddDetail(expl);
Explanation queryNormExpl = new Explanation(queryNorm, "queryNorm");
queryExpl.AddDetail(queryNormExpl);
queryExpl.SetValue(boostExpl.GetValue() * expl.GetValue() * queryNormExpl.GetValue());
result.AddDetail(queryExpl);
// explain field weight
System.String field = Enclosing_Instance.term.Field();
ComplexExplanation fieldExpl = new ComplexExplanation();
fieldExpl.SetDescription("fieldWeight(" + Enclosing_Instance.term + " in " + doc + "), product of:");
Explanation tfExpl = Scorer(reader, true, false).Explain(doc);
fieldExpl.AddDetail(tfExpl);
fieldExpl.AddDetail(expl);
Explanation fieldNormExpl = new Explanation();
byte[] fieldNorms = reader.Norms(field);
float fieldNorm = fieldNorms != null?Similarity.DecodeNorm(fieldNorms[doc]) : 1.0f;
fieldNormExpl.SetValue(fieldNorm);
fieldNormExpl.SetDescription("fieldNorm(field=" + field + ", doc=" + doc + ")");
fieldExpl.AddDetail(fieldNormExpl);
fieldExpl.SetMatch(tfExpl.IsMatch());
fieldExpl.SetValue(tfExpl.GetValue() * expl.GetValue() * fieldNormExpl.GetValue());
result.AddDetail(fieldExpl);
System.Boolean?tempAux = fieldExpl.GetMatch();
result.SetMatch(tempAux);
// combine them
result.SetValue(queryExpl.GetValue() * fieldExpl.GetValue());
if (queryExpl.GetValue() == 1.0f)
{
return(fieldExpl);
}
return(result);
}
public AnonymousClassIDFExplanation3(float fIdf, System.Text.StringBuilder exp, Similarity enclosingInstance)
{
InitBlock(fIdf, exp, enclosingInstance);
}
public MatchNoDocsWeight(Searcher searcher, MatchNoDocsQuery enclosingInstance)
{
this.enclosingInstance = enclosingInstance;
this.similarity = searcher.Similarity;
}
/// <summary>Set the default Similarity implementation used by indexing and search
/// code.
///
/// </summary>
/// <seealso cref="Searcher.SetSimilarity(Similarity)">
/// </seealso>
/// <seealso cref="Lucene.Net.Index.IndexWriter.SetSimilarity(Similarity)">
/// </seealso>
public static void SetDefault(Similarity similarity)
{
Similarity.defaultImpl = similarity;
}
public override Explanation Explain(IndexReader reader, int doc)
{
ComplexExplanation result = new ComplexExplanation();
result.Description = "weight(" + Query + " in " + doc + "), product of:";
Explanation idfExpl = new Explanation(idf, "idf(" + Query + ")");
// explain query weight
Explanation queryExpl = new Explanation();
queryExpl.Description = "queryWeight(" + Query + "), product of:";
Explanation boostExpl = new Explanation(Enclosing_Instance.Boost, "boost");
if (Enclosing_Instance.Boost != 1.0f)
{
queryExpl.AddDetail(boostExpl);
}
queryExpl.AddDetail(idfExpl);
Explanation queryNormExpl = new Explanation(queryNorm, "queryNorm");
queryExpl.AddDetail(queryNormExpl);
queryExpl.Value = boostExpl.Value * idfExpl.Value * queryNormExpl.Value;
result.AddDetail(queryExpl);
// explain field weight
ComplexExplanation fieldExpl = new ComplexExplanation();
fieldExpl.Description = "fieldWeight(" + Query + " in " + doc + "), product of:";
PhraseScorer scorer = (PhraseScorer)Scorer(reader, true, false);
if (scorer == null)
{
return(new Explanation(0.0f, "no matching docs"));
}
Explanation tfExplanation = new Explanation();
int d = scorer.Advance(doc);
float phraseFreq = (d == doc) ? scorer.CurrentFreq() : 0.0f;
tfExplanation.Value = similarity.Tf(phraseFreq);
tfExplanation.Description = "tf(phraseFreq=" + phraseFreq + ")";
fieldExpl.AddDetail(tfExplanation);
fieldExpl.AddDetail(idfExpl);
Explanation fieldNormExpl = new Explanation();
byte[] fieldNorms = reader.Norms(Enclosing_Instance.field);
float fieldNorm = fieldNorms != null?Similarity.DecodeNorm(fieldNorms[doc]) : 1.0f;
fieldNormExpl.Value = fieldNorm;
fieldNormExpl.Description = "fieldNorm(field=" + Enclosing_Instance.field + ", doc=" + doc + ")";
fieldExpl.AddDetail(fieldNormExpl);
fieldExpl.Match = tfExplanation.IsMatch;
fieldExpl.Value = tfExplanation.Value * idfExpl.Value * fieldNormExpl.Value;
result.AddDetail(fieldExpl);
System.Boolean?tempAux = fieldExpl.Match;
result.Match = tempAux;
// combine them
result.Value = queryExpl.Value * fieldExpl.Value;
if (queryExpl.Value == 1.0f)
{
return(fieldExpl);
}
return(result);
}
