| public abstract Idf ( int docFreq, int numDocs ) : float | ||
| docFreq | int | the number of documents which contain the term /// |
| numDocs | int | the total number of documents in the collection /// |
| return | float | |
public PhraseWeight(PhraseQuery enclosingInstance, Searcher searcher)
{
InitBlock(enclosingInstance);
this.similarity = Enclosing_Instance.GetSimilarity(searcher);
idf = similarity.Idf(Enclosing_Instance.terms, searcher);
}
public MultiPhraseWeight(MultiPhraseQuery enclosingInstance, Searcher searcher)
{
InitBlock(enclosingInstance);
this.similarity = Enclosing_Instance.GetSimilarity(searcher);
// compute idf
int maxDoc = searcher.MaxDoc;
foreach (Term[] terms in enclosingInstance.termArrays)
{
foreach (Term term in terms)
{
idf += similarity.Idf(searcher.DocFreq(term), maxDoc);
}
}
}
public PhraseWeight(PhraseQuery enclosingInstance, Searcher searcher)
{
InitBlock(enclosingInstance);
this.similarity = Enclosing_Instance.GetSimilarity(searcher);
idf = similarity.Idf(Enclosing_Instance.terms, searcher);
}
public virtual void TestKnownSetOfDocuments()
{
System.String[] termArray = new System.String[] { "eating", "chocolate", "in", "a", "computer", "lab", "grows", "old", "colored", "with", "an" };
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 RAMDirectory();
try
{
IndexWriter writer = new IndexWriter(dir, new SimpleAnalyzer(), true);
Assert.IsTrue(writer != null);
writer.AddDocument(testDoc1);
writer.AddDocument(testDoc2);
writer.AddDocument(testDoc3);
writer.AddDocument(testDoc4);
writer.Close();
IndexSearcher knownSearcher = new IndexSearcher(dir);
TermEnum termEnum = knownSearcher.reader.Terms();
TermDocs termDocs = knownSearcher.reader.TermDocs();
//System.out.println("Terms: " + termEnum.size() + " Orig Len: " + termArray.length);
Similarity sim = knownSearcher.GetSimilarity();
while (termEnum.Next() == true)
{
Term term = termEnum.Term();
//System.out.println("Term: " + term);
termDocs.Seek(term);
while (termDocs.Next())
{
int docId = termDocs.Doc();
int freq = termDocs.Freq();
//System.out.println("Doc Id: " + docId + " freq " + freq);
TermFreqVector vector = knownSearcher.reader.GetTermFreqVector(docId, "Field");
float tf = sim.Tf(freq);
float idf = sim.Idf(term, knownSearcher);
//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]) == true)
{
Assert.IsTrue(freqs[i] == freq);
}
}
}
//System.out.println("--------");
}
Query query = new TermQuery(new Term("Field", "chocolate"));
Hits hits = knownSearcher.Search(query);
//doc 3 should be the first hit b/c it is the shortest match
Assert.IsTrue(hits.Length() == 3);
float score = hits.Score(0);
/*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(testDoc3.ToString().Equals(hits.Doc(0).ToString()));
Assert.IsTrue(testDoc4.ToString().Equals(hits.Doc(1).ToString()));
Assert.IsTrue(testDoc1.ToString().Equals(hits.Doc(2).ToString()));
TermFreqVector vector2 = knownSearcher.reader.GetTermFreqVector(hits.Id(1), "Field");
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 = (System.Int32)test4Map[term];
System.Object tmpFreqInt = test4Map[term];
Assert.IsTrue(tmpFreqInt != null);
Assert.IsTrue(freqInt == freq);
}
knownSearcher.Close();
}
catch (System.IO.IOException e)
{
System.Console.Error.WriteLine(e.StackTrace);
Assert.IsTrue(false);
}
}
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 TermWeight(TermQuery enclosingInstance, Searcher searcher)
{
InitBlock(enclosingInstance);
this.similarity = Enclosing_Instance.GetSimilarity(searcher);
idf = similarity.Idf(Enclosing_Instance.term, searcher); // compute idf
}
public MultiPhraseWeight(MultiPhraseQuery enclosingInstance, Searcher searcher)
{
InitBlock(enclosingInstance);
this.similarity = Enclosing_Instance.GetSimilarity(searcher);
// compute idf
int maxDoc = searcher.MaxDoc;
foreach (Term[] terms in enclosingInstance.termArrays)
{
foreach (Term term in terms)
{
idf += similarity.Idf(searcher.DocFreq(term), maxDoc);
}
}
}
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);
Assert.IsTrue(writer != null);
writer.AddDocument(testDoc1);
writer.AddDocument(testDoc2);
writer.AddDocument(testDoc3);
writer.AddDocument(testDoc4);
writer.Close();
IndexSearcher knownSearcher = new IndexSearcher(dir);
TermEnum termEnum = knownSearcher.reader_ForNUnit.Terms();
TermDocs termDocs = knownSearcher.reader_ForNUnit.TermDocs();
//System.out.println("Terms: " + termEnum.size() + " Orig Len: " + termArray.length);
Similarity sim = knownSearcher.GetSimilarity();
while (termEnum.Next() == true)
{
Term term = termEnum.Term();
//System.out.println("Term: " + term);
termDocs.Seek(term);
while (termDocs.Next())
{
int docId = termDocs.Doc();
int freq = termDocs.Freq();
//System.out.println("Doc Id: " + docId + " freq " + freq);
TermFreqVector vector = knownSearcher.reader_ForNUnit.GetTermFreqVector(docId, "field");
float tf = sim.Tf(freq);
float idf = sim.Idf(term, knownSearcher);
//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).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);
TermFreqVector vector2 = knownSearcher.reader_ForNUnit.GetTermFreqVector(hits[1].doc, "field");
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);
System.Collections.Generic.SortedDictionary <object, object> vectorEntrySet = mapper.GetTermVectorEntrySet();
Assert.IsTrue(vectorEntrySet.Count == 10, "mapper.getTermVectorEntrySet() Size: " + vectorEntrySet.Count + " is not: " + 10);
TermVectorEntry last = null;
foreach (TermVectorEntry tve in vectorEntrySet.Keys)
{
if (tve != null && last != null)
{
Assert.IsTrue(last.GetFrequency() >= tve.GetFrequency(), "terms are not properly sorted");
System.Int32 expectedFreq = (System.Int32)test4Map[tve.GetTerm()];
//we expect double the expectedFreq, since there are two fields with the exact same text and we are collapsing all fields
Assert.IsTrue(tve.GetFrequency() == 2 * expectedFreq, "Frequency is not correct:");
}
last = tve;
}
FieldSortedTermVectorMapper fieldMapper = new FieldSortedTermVectorMapper(new TermVectorEntryFreqSortedComparator());
knownSearcher.reader_ForNUnit.GetTermFreqVector(hits[1].doc, fieldMapper);
System.Collections.IDictionary map = fieldMapper.GetFieldToTerms();
Assert.IsTrue(map.Count == 2, "map Size: " + map.Count + " is not: " + 2);
vectorEntrySet = (System.Collections.Generic.SortedDictionary <Object, Object>)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);
}
}
public TermWeight(TermQuery enclosingInstance, Searcher searcher)
{
InitBlock(enclosingInstance);
this.similarity = Enclosing_Instance.GetSimilarity(searcher);
idf = similarity.Idf(Enclosing_Instance.term, searcher); // compute idf
}
public override float Idf(int docFreq, int numDocs)
{
return(delegee.Idf(docFreq, numDocs));
}
