C# LuceneNet 40 查询内容精确度自定义排序

要实现自定义排序，您需要实现自定义评分器。评分器可以根据您的需求为每个匹配文档分配一个分数。以下是一个示例评分器，根据查询的精确度和文档的长度为文档分配分数：

public class CustomScorer : DefaultSimilarity
{
    public override float Coord(int overlap, int maxOverlap)
    {
        return (float) overlap / maxOverlap;
    }

    public override float Tf(float freq)
    {
        return (float) Math.Sqrt(freq);
    }

    public override float LengthNorm(FieldInvertState state)
    {
        return state.Boost / (float) Math.Sqrt(state.Length);
    }

    public override float Idf(long docFreq, long numDocs)
    {
        return (float) Math.Log(numDocs / (double) (docFreq + 1)) + 1;
    }

    public override float Slope(float freq)
    {
        return (float) Math.Sqrt(freq);
    }

    public override float ScorePayload(int doc, int start, int end, BytesRef payload)
    {
        return BitConverter.ToSingle(payload.Bytes, payload.Offset);
    }

    public override Explanation IdfExplain(CollectionStatistics collectionStats, TermStatistics termStats)
    {
        long df = termStats.DocFreq;
        long docCount = collectionStats.DocCount == -1 ? collectionStats.MaxDoc : collectionStats.DocCount;
        float idf = Idf(df, docCount);
        return Explanation.Match(idf, $"idf(docFreq={df}, docCount={docCount})");
    }

    public override Explanation TfNormExplain(FieldInvertState state)
    {
        float boost = state.Boost;
        float lengthNorm = LengthNorm(state);
        return Explanation.Match(boost * lengthNorm, $"tfNorm, computed as boost * lengthNorm, with boost={boost}, lengthNorm={lengthNorm}");
    }

    public override Explanation SlopeExplain(float freq)
    {
        float freq2 = Slope(freq);
        return Explanation.Match(freq2, $"tf, computed as sqrt(freq), with freq={freq}");
    }

    public override Explanation ScorePayloadExplanation(IndexSearcher searcher, String field, int doc, int start, int end, BytesRef payload)
    {
        float payloadScore = ScorePayload(doc, start, end, payload);
        return Explanation.Match(payloadScore, $"scorePayload(...) = {payloadScore}");
    }

    public override float ComputeNorm(FieldInvertState state)
    {
        return state.Boost / (float) Math.Sqrt(state.Length);
    }

    public override float QueryNorm(float sumOfSquaredWeights)
    {
        return (float) Math.Sqrt(sumOfSquaredWeights);
    }

    public override float Coord(int overlap, int maxOverlap, float queryBoost)
    {
        return Coord(overlap, maxOverlap) * queryBoost;
    }

    public override float ScoreDoc(Searcher searcher, int doc, float freq)
    {
        Document d = searcher.Doc(doc);
        string text = d.Get("text");
        Query query = searcher.GetIndexReader().GetQuery();
        float queryLength = query.Match(text).Length;
        float docLength = text.Length;
        float score = base.ScoreDoc(searcher, doc, freq);
        score *= Math.Min(queryLength / docLength, 1);
        return score;
    }
}

然后，您可以将此评分器与查询一起使用，如下所示：

var searcher = new IndexSearcher(index, true);
searcher.Similarity = new CustomScorer();
Query query = new TermQuery(new Term("title", "lucene"));
TopDocs docs = searcher.Search(query, null, 10);
foreach (ScoreDoc scoreDoc in docs.ScoreDocs)
{
    Document doc = searcher.Doc(scoreDoc.Doc);
    Console.WriteLine($"Title: {doc.Get("title")}, Score: {scoreDoc.Score}");
}

在此示例中，我们使用 TermQuery 查询匹配标题中包含 "lucene" 的文档，并使用自定义评分器为每个匹配文档分配分数。评分器使用 ScoreDoc 方法计算文档的初始分数，然后将其乘以查询精确度（即查询的长度与文档长度的比率）。最后，我们按降序排序匹配文档，以便最高分的文档排在前面。