Package org.apache.lucene.search.similarities

Examples of org.apache.lucene.search.similarities.DefaultSimilarity

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  public void testSlopScoring() throws IOException {
    Directory directory = newDirectory();
    RandomIndexWriter writer = new RandomIndexWriter(random(), directory,
        newIndexWriterConfig(TEST_VERSION_CURRENT, new MockAnalyzer(random()))
          .setMergePolicy(newLogMergePolicy())
          .setSimilarity(new DefaultSimilarity()));

    Document doc = new Document();
    doc.add(newTextField("field", "foo firstname lastname foo", Field.Store.YES));
    writer.addDocument(doc);
   
    Document doc2 = new Document();
    doc2.add(newTextField("field", "foo firstname zzz lastname foo", Field.Store.YES));
    writer.addDocument(doc2);
   
    Document doc3 = new Document();
    doc3.add(newTextField("field", "foo firstname zzz yyy lastname foo", Field.Store.YES));
    writer.addDocument(doc3);
   
    IndexReader reader = writer.getReader();
    writer.close();

    IndexSearcher searcher = newSearcher(reader);
    searcher.setSimilarity(new DefaultSimilarity());
    PhraseQuery query = new PhraseQuery();
    query.add(new Term("field", "firstname"));
    query.add(new Term("field", "lastname"));
    query.setSlop(Integer.MAX_VALUE);
    ScoreDoc[] hits = searcher.search(query, null, 1000).scoreDocs;
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    IndexReader r = w.getReader();
    IndexSearcher s = newSearcher(r);
    // this test relies upon coord being the default implementation,
    // otherwise scores are different!
    s.setSimilarity(new DefaultSimilarity());

    BooleanQuery q = new BooleanQuery();
    q.add(new TermQuery(new Term("field", "a")), BooleanClause.Occur.SHOULD);

    // LUCENE-2617: make sure that a term not in the index still contributes to the score via coord factor
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      writer.close();
      // we don't wrap with AssertingIndexSearcher in order to have the original scorer in setScorer.
      searcher = newSearcher(reader, true, false);
     
      // set a similarity that does not normalize our boost away
      searcher.setSimilarity(new DefaultSimilarity() {
        @Override
        public float queryNorm(float sumOfSquaredWeights) {
          return 1.0f;
        }
      });
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    add("This is a test", "object", writer);
    add("a note", "note", writer);
   
    IndexReader reader = writer.getReader();
    IndexSearcher searcher = newSearcher(reader);
    searcher.setSimilarity(new DefaultSimilarity() {
      @Override
      public Explanation idfExplain(CollectionStatistics collectionStats, TermStatistics termStats[]) {
        return new Explanation(10f, "just a test");
      }
    });
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  }
 
  public void testIDF() throws Exception {
    Similarity saved = searcher.getSimilarity();
    try {
      searcher.setSimilarity(new DefaultSimilarity());
      assertHits(new FunctionQuery(
          new IDFValueSource("bogus", "bogus", "text", new BytesRef("test"))),
          new float[] { 0.5945349f, 0.5945349f });
    } finally {
      searcher.setSimilarity(saved);
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  public void testNorm() throws Exception {
    Similarity saved = searcher.getSimilarity();
    try {
      // no norm field (so agnostic to indexed similarity)
      searcher.setSimilarity(new DefaultSimilarity());
      assertHits(new FunctionQuery(
          new NormValueSource("byte")),
          new float[] { 0f, 0f });
    } finally {
      searcher.setSimilarity(saved);
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  public void testTF() throws Exception {
    Similarity saved = searcher.getSimilarity();
    try {
      // no norm field (so agnostic to indexed similarity)
      searcher.setSimilarity(new DefaultSimilarity());
      assertHits(new FunctionQuery(
          new TFValueSource("bogus", "bogus", "text", new BytesRef("test"))),
          new float[] { (float)Math.sqrt(3d), (float)Math.sqrt(1d) });
      assertHits(new FunctionQuery(
          new TFValueSource("bogus", "bogus", "string", new BytesRef("bar"))),
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    IndexSearcher search = newSearcher(reader);

    // test for correct application of query normalization
    // must use a non score normalizing method for this.
   
    search.setSimilarity(new DefaultSimilarity());
    Query q = csrq("data", "1", "6", T, T);
    q.setBoost(100);
    search.search(q, null, new Collector() {
      private int base = 0;
      private Scorer scorer;
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    }
    writer.close();
    littleReader = DirectoryReader.open(directory);
    searcher = newSearcher(littleReader);
    // this is intentionally using the baseline sim, because it compares against bigSearcher (which uses a random one)
    searcher.setSimilarity(new DefaultSimilarity());

    // Make big index
    dir2 = new MockDirectoryWrapper(random(), new RAMDirectory(directory, IOContext.DEFAULT));

    // First multiply small test index:
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    query.add(new TermQuery(new Term(field, "zz")), BooleanClause.Occur.SHOULD);

    int[] expDocNrs = {2, 3};
    Similarity oldSimilarity = searcher.getSimilarity();
    try {
      searcher.setSimilarity(new DefaultSimilarity(){
        @Override
        public float coord(int overlap, int maxOverlap) {
          return overlap / ((float)maxOverlap - 1);
        }
      });
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