org.apache.lucene.search.uhighlight

## Class PassageScorer

- java.lang.Object
- - org.apache.lucene.search.uhighlight.PassageScorer

-     public class PassageScorer
      extends Object

  Ranks passages found by `UnifiedHighlighter`.

  Each passage is scored as a miniature document within the document. The final score is computed as `norm(int)` * ∑ (`weight(int, int)` * `tf(int, int)`). The default implementation is `norm(int)` * BM25.

  WARNING: This API is experimental and might change in incompatible ways in the next release.

- - ### Constructor Summary

    Constructors
    Constructor and Description
    `PassageScorer()` Creates PassageScorer with these default values: `k1 = 1.2`, `b = 0.75`.
    `PassageScorer(float k1, float b, float pivot)` Creates PassageScorer with specified scoring parameters

  - ### Method Summary

    All Methods Instance Methods Concrete Methods
    Modifier and Type | Method and Description
    --- | ---
    `float` | `norm(int passageStart)` Normalize a passage according to its position in the document.
    `float` | `score(Passage passage, int contentLength)`
    `float` | `tf(int freq, int passageLen)` Computes term weight, given the frequency within the passage and the passage's length.
    `float` | `weight(int contentLength, int totalTermFreq)` Computes term importance, given its in-document statistics.

    - ### Methods inherited from class java.lang.Object

      `clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait`

- - ### Constructor Detail

    - #### PassageScorer

          public PassageScorer()

      Creates PassageScorer with these default values:
      - `k1 = 1.2`,
      - `b = 0.75`.
      - `pivot = 87`

    - #### PassageScorer

          public PassageScorer(float k1,
                               float b,
                               float pivot)

      Creates PassageScorer with specified scoring parameters
      Parameters:
        `k1` - Controls non-linear term frequency normalization (saturation).
        `b` - Controls to what degree passage length normalizes tf values.
        `pivot` - Pivot value for length normalization (some rough idea of average sentence length in characters).

  - ### Method Detail

    - #### weight

          public float weight(int contentLength,
                              int totalTermFreq)

      Computes term importance, given its in-document statistics.
      Parameters:
        `contentLength` - length of document in characters
        `totalTermFreq` - number of time term occurs in document
      Returns:
        term importance

    - #### tf

          public float tf(int freq,
                          int passageLen)

      Computes term weight, given the frequency within the passage and the passage's length.
      Parameters:
        `freq` - number of occurrences of within this passage
        `passageLen` - length of the passage in characters.
      Returns:
        term weight

    - #### norm

          public float norm(int passageStart)

      Normalize a passage according to its position in the document.

      Typically passages towards the beginning of the document are more useful for summarizing the contents.

      The default implementation is `1 + 1/log(pivot + passageStart)`

      Parameters:
        `passageStart` - start offset of the passage
      Returns:
        a boost value multiplied into the passage's core.

    - #### score

          public float score(Passage passage,
                             int contentLength)

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