internal BM25DocScorer(BM25Similarity outerInstance, BM25Stats stats, NumericDocValues norms)
{
this.outerInstance = outerInstance;
this.stats = stats;
this.weightValue = stats.Weight * (outerInstance.k1 + 1);
this.cache = stats.Cache;
this.norms = norms;
}
internal BM25DocScorer(BM25Similarity outerInstance, BM25Stats stats, NumericDocValues norms)
{
this.OuterInstance = outerInstance;
this.Stats = stats;
this.WeightValue = stats.Weight * (outerInstance.K1_Renamed + 1);
this.Cache = stats.Cache;
this.Norms = norms;
}
private Explanation ExplainScore(int doc, Explanation freq, BM25Stats stats, NumericDocValues norms)
{
Explanation result = new Explanation();
result.Description = "score(doc=" + doc + ",freq=" + freq + "), product of:";
Explanation boostExpl = new Explanation(stats.QueryBoost * stats.TopLevelBoost, "boost");
if (boostExpl.Value != 1.0f)
{
result.AddDetail(boostExpl);
}
result.AddDetail(stats.Idf);
Explanation tfNormExpl = new Explanation();
tfNormExpl.Description = "tfNorm, computed from:";
tfNormExpl.AddDetail(freq);
tfNormExpl.AddDetail(new Explanation(k1, "parameter k1"));
if (norms == null)
{
tfNormExpl.AddDetail(new Explanation(0, "parameter b (norms omitted for field)"));
tfNormExpl.Value = (freq.Value * (k1 + 1)) / (freq.Value + k1);
}
else
{
float doclen = DecodeNormValue((byte)norms.Get(doc));
tfNormExpl.AddDetail(new Explanation(b, "parameter b"));
tfNormExpl.AddDetail(new Explanation(stats.Avgdl, "avgFieldLength"));
tfNormExpl.AddDetail(new Explanation(doclen, "fieldLength"));
tfNormExpl.Value = (freq.Value * (k1 + 1)) / (freq.Value + k1 * (1 - b + b * doclen / stats.Avgdl));
}
result.AddDetail(tfNormExpl);
result.Value = boostExpl.Value * stats.Idf.Value * tfNormExpl.Value;
return(result);
}
public override sealed SimScorer GetSimScorer(SimWeight stats, AtomicReaderContext context)
{
BM25Stats bm25stats = (BM25Stats)stats;
return(new BM25DocScorer(this, bm25stats, context.AtomicReader.GetNormValues(bm25stats.Field)));
}
private Explanation ExplainScore(int doc, Explanation freq, BM25Stats stats, NumericDocValues norms)
{
Explanation result = new Explanation();
result.Description = "score(doc=" + doc + ",freq=" + freq + "), product of:";
Explanation boostExpl = new Explanation(stats.QueryBoost * stats.TopLevelBoost, "boost");
if (boostExpl.Value != 1.0f)
{
result.AddDetail(boostExpl);
}
result.AddDetail(stats.Idf);
Explanation tfNormExpl = new Explanation();
tfNormExpl.Description = "tfNorm, computed from:";
tfNormExpl.AddDetail(freq);
tfNormExpl.AddDetail(new Explanation(K1_Renamed, "parameter k1"));
if (norms == null)
{
tfNormExpl.AddDetail(new Explanation(0, "parameter b (norms omitted for field)"));
tfNormExpl.Value = (freq.Value * (K1_Renamed + 1)) / (freq.Value + K1_Renamed);
}
else
{
float doclen = DecodeNormValue((sbyte)norms.Get(doc));
tfNormExpl.AddDetail(new Explanation(b, "parameter b"));
tfNormExpl.AddDetail(new Explanation(stats.Avgdl, "avgFieldLength"));
tfNormExpl.AddDetail(new Explanation(doclen, "fieldLength"));
tfNormExpl.Value = (freq.Value * (K1_Renamed + 1)) / (freq.Value + K1_Renamed * (1 - b + b * doclen / stats.Avgdl));
}
result.AddDetail(tfNormExpl);
result.Value = boostExpl.Value * stats.Idf.Value * tfNormExpl.Value;
return result;
}
internal BM25DocScorer(BM25Similarity outerInstance, BM25Stats stats, NumericDocValues norms)
{
this.OuterInstance = outerInstance;
this.Stats = stats;
this.WeightValue = stats.Weight * (outerInstance.K1_Renamed + 1);
this.Cache = stats.Cache;
this.Norms = norms;
}
