Thanks Masaru, so it is the precision loss issue by encoding/decoding. That 
makes sense.


On Friday, March 27, 2015 at 11:04:12 AM UTC+8, Masaru Hasegawa wrote:
>
> Hi,
>
> I believe it's because field norm is encoded in single byte.
> See  <http://lucene.apache.org/core/4_10_2/core/index.html>
> http://lucene.apache.org/core/4_10_2/core/org/apache/lucene/search/similarities/DefaultSimilarity.html
>
> <http://lucene.apache.org/core/4_10_2/core/index.html>
>
> Masaru
>  
> On March 26, 2015 at 14:36:45, Xudong You ([email protected] 
> <javascript:>) wrote:
>
>  
> Per this post "theory behind relevance scoring" 
> http://www.elastic.co/guide/en/elasticsearch/guide/current/scoring-theory.html
>
> Elasticsearch calculate the field-length norm as follows:
>  
> norm(d) = 1 / √numTerms
>
>
>  But per my testing, seems the actual result value calculated does not meet 
> above formula.
>
>
>  Following is my index docs:
>
>
>  1.
> {
> "title" : "quick brown fox"
> }
>
> 2.
> {
> "title" : "quick fox"
> }
>
>
> Then I query "fox" with following query:
>  POST /vsmtest/test/_search?explain
> {
>   "query" : {
>     "match" : {"title":"fox"}
>   }
> }
>  
> The result norm value are follows:
>
> doc 1:
>                          {
>                            "value": 0.5,
>                            "description": "fieldNorm(doc=0)"
>                         }
>  doc 2:
>                          {
>                            "value": 0.625,
>                            "description": "fieldNorm(doc=0)"
>                         }
>  
> Can anyone help me understand how does 0.5 and 0.625 calculated per the 
> formula?
>  norm(d) = 1 / √numTerms
>
>
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